The document discusses challenges with developing and deploying AI/ML projects and proposes an agile framework called Data-Driven Scrum (DDS) to address these challenges. DDS integrates elements of Scrum and Kanban to allow for iterative experimentation in AI/ML projects. It supports capability-based iterations of varying length and focuses teams on creating models, observing results, and analyzing learnings. DDS aims to improve upon traditional agile methods that do not always translate well to data science work due to uncertainties in task estimation and the need for flexible iterations around data and modeling tasks.
This document provides an overview of different software development processes including the waterfall model, iterative model, Rational Unified Process (RUP), and Agile Development Process (ADP). It describes the key aspects of each process including phases, roles, artifacts, and ceremonies. Specifically, it provides detailed explanations of Scrum, an agile methodology, including Scrum roles like Product Owner and Scrum Master, ceremonies like the Daily Scrum, and artifacts like the Product Backlog and Sprint Backlog. The document concludes with references for further information.
Want to be seen as a leader at the office? Learn how to identify and push back against gender bias by supporting your female colleagues at work. Read the full tips at leanin.org/tips/mvp
Introduction to ChatGPT & how its implemented in UiPath
This document provides an overview of using ChatGPT for intelligent automation through UiPath. It discusses how ChatGPT can be implemented in UiPath using web API activities. It also covers the benefits and limitations of using ChatGPT, as well as best practices for developing ChatGPT models and considerations for privacy, ethics, security and governance. The document concludes with information on UiPath's community resources for learning RPA skills and connecting with other automation professionals.
What Happens When Robots And Machines Learn On Their Own?
This slide deck is an introduction to exponential technologies for an audience of designers and developers of workforce training materials.
The Blended Learning And Technologies Forum (BLAT Forum) is a quarterly event in Auckland, New Zealand that welcomes practitioners, designers and developers of blended learning instructional deliverables across different industries of the New Zealand economy.
Millennials & Money: One Generation, Many Goals & Values
In the 2016 Millennials & Money research, the Edelman Financial Services Sector and Edelman Intelligence teams explored the role of money and financial services in the lives of Millennials throughout the U.S. The research revealed their beliefs and attitudes towards money and uncovered how their goals and values differ based on ethnic and cultural backgrounds.
Read more: http://edl.mn/20R27Tj
What is ChatGPT and how can we use it? This is a talk given at Affiliate Summit West -- January 2023 to explain what ChatGPT is and isn't and how we can use it in Search.
All images were created using Dall-e.
Gartner provides webinars on various topics related to technology. This webinar discusses generative AI, which refers to AI techniques that can generate new unique artifacts like text, images, code, and more based on training data. The webinar covers several topics related to generative AI, including its use in novel molecule discovery, AI avatars, and automated content generation. It provides examples of how generative AI can benefit various industries and recommendations for organizations looking to utilize this emerging technology.
The document discusses the agile mindset, which focuses on how one thinks rather than just skills or methodology. It emphasizes embracing change, learning from failures, and adapting to changing needs. The agile mindset involves thinking with a beginner's mindset, growth mindset, and design thinking approach. It means being comfortable with ambiguity and discomfort while pursuing new ideas through testing and learning.
leewayhertz.com-The architecture of Generative AI for enterprises.pdf
Generative AI is quickly becoming popular among enterprises, with various applications being developed that can change how businesses operate. From code generation to product design and engineering, generative AI impacts a range of enterprise applications.
- The document discusses the evolution of conversational AI over the past 5 years from early rule-based and fixed flow bots to current 3rd generation bots that can handle dynamic flows and open domains.
- Key applications of conversational AI discussed include customer support, HR, CRM, ERP and SCM to improve customer satisfaction, reduce costs and increase efficiency.
- The document provides examples of potential use cases for supply chain and distribution companies and outlines a process for identifying, designing, implementing, testing and deploying conversational AI solutions.
This talk outlines a number of the lessons and principals I have learned in my 5 years with Sauce Labs and experiencing its growth and success from a development and management perspective.
Presenting this set of slides with name - Artificial Intelligence Overview Powerpoint Presentation Slides. This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with thirtyseven slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Artificial Intelligence Overview Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
The document discusses artificial intelligence and Microsoft's offerings. It promotes AI acceleration and digital transformation leadership. It outlines Microsoft's AI leadership framework of industry alignment and user empowerment. It provides historical overviews of AI, machine learning, and deep learning. It describes Microsoft and OpenAI's generative models like GPT-3, DALL-E, and ChatGPT. It discusses Microsoft's responsible AI principles and potential industry uses of GPT-3. It promotes customizing Azure OpenAI and provides prompt engineering examples. It introduces Microsoft 365 Copilot and emphasizes access to business content and context. It offers next steps for AI leadership, including learning opportunities and challenge teams to find use cases. Finally, it advertises a zero
Tuesday is consistently found to be the most productive day of the week for employees according to multiple surveys of HR managers and executives over several decades. Employees are generally least productive between 4-6pm and the week before a major holiday. Taking vacations can boost productivity as employees tend to be more productive after a vacation when returning well-rested and recharged.
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
In the US, people are already implementing the use of converstaionl AI, ChatGPT in everydy mundane tasks. Implementation is not only limited to that. Various industries are also using this revolutionary technology for maintaining a superior customer experience. People are also criticizing ChatGPT for creating employment threats and also being unethical in it's answers. The technology is being widely applauded but everything has certain pain points associated with it.
Scrum an extension pattern language for hyperproductive software development
Scrum is an agile software development framework that utilizes daily stand-up meetings called Scrum Meetings to manage unpredictable processes. During short, 15-minute Scrum Meetings, team members report on tasks completed since the previous meeting, any issues encountered, and their plan for the next 24 hours. This allows for continuous monitoring and adjustment of small, flexible assignments. Scrum Meetings foster transparency, knowledge sharing, and a collaborative culture within self-organizing teams. By frequently inspecting and adapting their process, teams can respond effectively to unpredictability and complexity inherent in software development.
· Stability in the Frequency Domain1. Consider a closed-loop sys.docx
· Stability in the Frequency Domain
1. Consider a closed-loop system that has the loop transfer function L(s) = Gc(s)G(s) = Ke-TS / s
1. Determine the gain K so that the phase margin is 60 degrees when T = 0.2.
2. Plot the phase margin versus the time delay T for K as in part (a).
2. Include all MATLAB code, calculations and screenshots in a Word entitled “Lab6_StudentID”.
3. Upload file “Lab6_StudentID”
Shipping SaaS 1
SWE482-1801A-01
Software Engineering Capstone II
Shipping SaaS Team Project
Blake Foster, Courie Gomez, James Allendoerfer, Joseph Robinson, Terelle Allen
With additional contributions by Phillip Hart and Destiny Barrera
1/10/2018
Table of Contents
Project Outline 3
Development Methodology 7
Requirements 9
Design 12
Development and Testing 18
Project Schedule 29
Risk Analysis 36
References 39
Project Outline
*Portions of this section have been repurposed from SWE481-1704B-01 Unit 1 Group Project Proposal
CTU Regional has commissioned the design and implementation of an inventory tracking and shipping service. The service is needed as a centralized stateless/RESTful SaaS (Software as a Service). The team developing the SaaS should be familiar in database communication, API development, and network communication. As the customers place an order, the payment is processed and inventory is pulled from the warehouse. Once completed, the order is fed to the shipping service, which will then take on the role of managing the shipment. The service provides API endpoints related to orders and shipping. The API can be used by as many applications as necessary. Usage examples might include employees updating the status of the order at key points along the way (such as when inventory is packaged, when the order is placed for carrier pickup, and entering tracking), calculating shipping costs, or creating/printing mailing labels. It will also track inventory numbers and aid in the processing and management of the shipments themselves. Employees must have access to make updates as the order progresses. For example, warehouse employees will be able to update when an order has been picked, packed, and when it is shipped.
Here, the Shipping SaaS manages incoming order tracking surrounded by four external systems, each requiring API access and integration. That said, this picture will be expanded and explained in greater detail later in the document. However, there are some issues which need to be addressed.
The design of this application needs to be carefully created. Since it is being designed with an API that can have a number of different GUIs created to interact, the API needs to be efficient, secure, well documented, and reliable. Below is a list of major issues to consider in the development of the service (Kodumal, 2015.)
Issues
Description
SaaS API Support from each system
Warehouse System, Customer Service System, Website Order System.
SaaS Performance
Depending on the size and number of orders, there may be a m.
The document discusses the Scrum framework for agile software development. It notes that traditional defined process approaches make incorrect assumptions that requirements, solutions, developers, and environments can be fully defined and repeated. Scrum addresses this by dividing projects into short "Sprints" of fixed time periods, usually 1 month or less. Each Sprint pulls tasks from a prioritized backlog and aims to deliver working software. Daily Scrum meetings help teams self-organize and resolve issues. At the end of each Sprint, teams demonstrate progress to customers and prioritize new tasks for the next Sprint. By continually adapting requirements and quickly delivering working software, Scrum allows for the uncertainties of software development.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
- ChatGPT was launched in November 2022 and gained over 1 million users in its first 5 days, making it one of the fastest adopted digital products.
- ChatGPT is based on GPT-3, a large language model developed by OpenAI over many years using trillions of words from the internet to power conversational abilities.
- ChatGPT can answer questions, write stories, programs, music and more based on its vast knowledge, but cannot provide fully trustworthy information, create harmful content, or replace all human jobs.
An Agile mindset believes that diverse teams with complementary skills are best equipped to thrive in today’s business environments.
Many organizations, working with Agile methodologies, talk about changing mindsets. I know from extensive experience that Agile principles and practices by themselves will not lead to this kind of transformation. A real Agile transformation is about not just doing Agile, but being Agile.
‘Follow Agile’ mindset will only help us get into the water but ‘Being Agile’ mindset will help us swim in the current. Most Agile implementations fail and their practitioners cannot tell why. Managers jump onto the Agile bandwagon, and quickly discover that the change runs much deeper and wider than they’d been told. Worse yet, people decide for or against Agile without understanding it properly. It does not have to be this way. This will be an interactive workshop leading toward the Agility.
The ChatGPT Cheat Sheet provides concise summaries of ChatGPT's abilities across various domains including natural language processing, code, and structured/unstructured output styles to enhance user proficiency. It also covers media types, expert prompting, and more.
This document provides an overview of different software development processes including the waterfall model, iterative model, Rational Unified Process (RUP), and Agile Development Process (ADP). It describes the key aspects of each process including phases, roles, artifacts, and ceremonies. Specifically, it provides detailed explanations of Scrum, an agile methodology, including Scrum roles like Product Owner and Scrum Master, ceremonies like the Daily Scrum, and artifacts like the Product Backlog and Sprint Backlog. The document concludes with references for further information.
Want to be seen as a leader at the office? Learn how to identify and push back against gender bias by supporting your female colleagues at work. Read the full tips at leanin.org/tips/mvp
Introduction to ChatGPT & how its implemented in UiPathsharonP24
This document provides an overview of using ChatGPT for intelligent automation through UiPath. It discusses how ChatGPT can be implemented in UiPath using web API activities. It also covers the benefits and limitations of using ChatGPT, as well as best practices for developing ChatGPT models and considerations for privacy, ethics, security and governance. The document concludes with information on UiPath's community resources for learning RPA skills and connecting with other automation professionals.
The Future Of Work & The Work Of The FutureArturo Pelayo
What Happens When Robots And Machines Learn On Their Own?
This slide deck is an introduction to exponential technologies for an audience of designers and developers of workforce training materials.
The Blended Learning And Technologies Forum (BLAT Forum) is a quarterly event in Auckland, New Zealand that welcomes practitioners, designers and developers of blended learning instructional deliverables across different industries of the New Zealand economy.
Millennials & Money: One Generation, Many Goals & ValuesEdelman
In the 2016 Millennials & Money research, the Edelman Financial Services Sector and Edelman Intelligence teams explored the role of money and financial services in the lives of Millennials throughout the U.S. The research revealed their beliefs and attitudes towards money and uncovered how their goals and values differ based on ethnic and cultural backgrounds.
Read more: http://edl.mn/20R27Tj
What is ChatGPT and how can we use it? This is a talk given at Affiliate Summit West -- January 2023 to explain what ChatGPT is and isn't and how we can use it in Search.
All images were created using Dall-e.
Gartner provides webinars on various topics related to technology. This webinar discusses generative AI, which refers to AI techniques that can generate new unique artifacts like text, images, code, and more based on training data. The webinar covers several topics related to generative AI, including its use in novel molecule discovery, AI avatars, and automated content generation. It provides examples of how generative AI can benefit various industries and recommendations for organizations looking to utilize this emerging technology.
The document discusses the agile mindset, which focuses on how one thinks rather than just skills or methodology. It emphasizes embracing change, learning from failures, and adapting to changing needs. The agile mindset involves thinking with a beginner's mindset, growth mindset, and design thinking approach. It means being comfortable with ambiguity and discomfort while pursuing new ideas through testing and learning.
leewayhertz.com-The architecture of Generative AI for enterprises.pdfKristiLBurns
Generative AI is quickly becoming popular among enterprises, with various applications being developed that can change how businesses operate. From code generation to product design and engineering, generative AI impacts a range of enterprise applications.
- The document discusses the evolution of conversational AI over the past 5 years from early rule-based and fixed flow bots to current 3rd generation bots that can handle dynamic flows and open domains.
- Key applications of conversational AI discussed include customer support, HR, CRM, ERP and SCM to improve customer satisfaction, reduce costs and increase efficiency.
- The document provides examples of potential use cases for supply chain and distribution companies and outlines a process for identifying, designing, implementing, testing and deploying conversational AI solutions.
This talk outlines a number of the lessons and principals I have learned in my 5 years with Sauce Labs and experiencing its growth and success from a development and management perspective.
Presenting this set of slides with name - Artificial Intelligence Overview Powerpoint Presentation Slides. This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with thirtyseven slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Artificial Intelligence Overview Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
The document discusses artificial intelligence and Microsoft's offerings. It promotes AI acceleration and digital transformation leadership. It outlines Microsoft's AI leadership framework of industry alignment and user empowerment. It provides historical overviews of AI, machine learning, and deep learning. It describes Microsoft and OpenAI's generative models like GPT-3, DALL-E, and ChatGPT. It discusses Microsoft's responsible AI principles and potential industry uses of GPT-3. It promotes customizing Azure OpenAI and provides prompt engineering examples. It introduces Microsoft 365 Copilot and emphasizes access to business content and context. It offers next steps for AI leadership, including learning opportunities and challenge teams to find use cases. Finally, it advertises a zero
Productivity Facts Every Employee Should KnowRobert Half
Tuesday is consistently found to be the most productive day of the week for employees according to multiple surveys of HR managers and executives over several decades. Employees are generally least productive between 4-6pm and the week before a major holiday. Taking vacations can boost productivity as employees tend to be more productive after a vacation when returning well-rested and recharged.
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
In the US, people are already implementing the use of converstaionl AI, ChatGPT in everydy mundane tasks. Implementation is not only limited to that. Various industries are also using this revolutionary technology for maintaining a superior customer experience. People are also criticizing ChatGPT for creating employment threats and also being unethical in it's answers. The technology is being widely applauded but everything has certain pain points associated with it.
Scrum an extension pattern language for hyperproductive software developmentShiraz316
Scrum is an agile software development framework that utilizes daily stand-up meetings called Scrum Meetings to manage unpredictable processes. During short, 15-minute Scrum Meetings, team members report on tasks completed since the previous meeting, any issues encountered, and their plan for the next 24 hours. This allows for continuous monitoring and adjustment of small, flexible assignments. Scrum Meetings foster transparency, knowledge sharing, and a collaborative culture within self-organizing teams. By frequently inspecting and adapting their process, teams can respond effectively to unpredictability and complexity inherent in software development.
· Stability in the Frequency Domain1. Consider a closed-loop sys.docxoswald1horne84988
· Stability in the Frequency Domain
1. Consider a closed-loop system that has the loop transfer function L(s) = Gc(s)G(s) = Ke-TS / s
1. Determine the gain K so that the phase margin is 60 degrees when T = 0.2.
2. Plot the phase margin versus the time delay T for K as in part (a).
2. Include all MATLAB code, calculations and screenshots in a Word entitled “Lab6_StudentID”.
3. Upload file “Lab6_StudentID”
Shipping SaaS 1
SWE482-1801A-01
Software Engineering Capstone II
Shipping SaaS Team Project
Blake Foster, Courie Gomez, James Allendoerfer, Joseph Robinson, Terelle Allen
With additional contributions by Phillip Hart and Destiny Barrera
1/10/2018
Table of Contents
Project Outline 3
Development Methodology 7
Requirements 9
Design 12
Development and Testing 18
Project Schedule 29
Risk Analysis 36
References 39
Project Outline
*Portions of this section have been repurposed from SWE481-1704B-01 Unit 1 Group Project Proposal
CTU Regional has commissioned the design and implementation of an inventory tracking and shipping service. The service is needed as a centralized stateless/RESTful SaaS (Software as a Service). The team developing the SaaS should be familiar in database communication, API development, and network communication. As the customers place an order, the payment is processed and inventory is pulled from the warehouse. Once completed, the order is fed to the shipping service, which will then take on the role of managing the shipment. The service provides API endpoints related to orders and shipping. The API can be used by as many applications as necessary. Usage examples might include employees updating the status of the order at key points along the way (such as when inventory is packaged, when the order is placed for carrier pickup, and entering tracking), calculating shipping costs, or creating/printing mailing labels. It will also track inventory numbers and aid in the processing and management of the shipments themselves. Employees must have access to make updates as the order progresses. For example, warehouse employees will be able to update when an order has been picked, packed, and when it is shipped.
Here, the Shipping SaaS manages incoming order tracking surrounded by four external systems, each requiring API access and integration. That said, this picture will be expanded and explained in greater detail later in the document. However, there are some issues which need to be addressed.
The design of this application needs to be carefully created. Since it is being designed with an API that can have a number of different GUIs created to interact, the API needs to be efficient, secure, well documented, and reliable. Below is a list of major issues to consider in the development of the service (Kodumal, 2015.)
Issues
Description
SaaS API Support from each system
Warehouse System, Customer Service System, Website Order System.
SaaS Performance
Depending on the size and number of orders, there may be a m.
A Pattern-Language-for-software-DevelopmentShiraz316
The document discusses the Scrum framework for agile software development. It notes that traditional defined process approaches make incorrect assumptions that requirements, solutions, developers, and environments can be fully defined and repeated. Scrum addresses this by dividing projects into short "Sprints" of fixed time periods, usually 1 month or less. Each Sprint pulls tasks from a prioritized backlog and aims to deliver working software. Daily Scrum meetings help teams self-organize and resolve issues. At the end of each Sprint, teams demonstrate progress to customers and prioritize new tasks for the next Sprint. By continually adapting requirements and quickly delivering working software, Scrum allows for the uncertainties of software development.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
Current Trends in Agile - opening keynote for Agile Israel 2014Yuval Yeret
Yuval Yeret, AgileSparks’s CTO will give trends overview session – What is hot, what is not, in the lean agile industry/community – with the aim of exposing people and giving a big picture view that places the different trends as well as sessions in the conference into the right context. We will discuss trends like Scaling Agile (SAFe, Less, DAD), DevOps / Continuous Delivery, Modern Management aspects, Modern Change Management approaches such as Open-Agile-Adoption, What is going on in the world of Kanban, Agile Fluency, Technical Safety / Anzeneering, and maybe more.
http://agileisrael2014.com/current-trends-in-agile/
The document discusses sizing Oracle Applications projects from a technical perspective. It emphasizes that properly sizing the production system is important for meeting performance and availability requirements but often underestimated. The key points made are:
- Production system sizing should be determined through a technical architecture study and workload analysis, not by default hardware specifications.
- Projects often lack adequate resources dedicated to the technical architecture study and production sizing phase.
- Failure to properly size the system through modeling and predictions can result in performance issues or lack of capacity to support the workload.
This document provides guidance on estimating the effort required for a software development project. It discusses estimating human effort by rating functions as easy, medium, hard, or complex and assigning effort estimates in days. Additional activities like analysis, design, and testing are estimated as percentages of the build effort. Hardware requirements like processor power, disk space, and RAM are also addressed at a high level. The overall message is that project estimation is imprecise but essential, and estimates should be revisited regularly as more information becomes available.
Oracle database performance diagnostics - before your beginHemant K Chitale
This is an article that I had written in 2011 for publication on OTN. It never did appear. So I am making it available here. It is not "slides" but is only 7 pages long. I hope you find it useful.
This document summarizes a master's thesis that evaluates cloud-based approaches to data quality management. Specifically, it implements an Azure Data Factory-based ETL system for data quality management on the Microsoft Azure cloud platform. The system uses Azure Storage, HDInsight, Azure ML Studio, and Azure SQL Database. The thesis tests the system on sample telemetry data to clean and improve the data quality. It then assesses the performance, scalability, and pricing of the ETL system for managing large volumes of data in the cloud.
This document presents a study conducted on 12 agile teams at Honeywell to develop models for estimating agile team maturity and the number of story points a team can deliver. The study analyzed data collected over 445 business days from the teams. A model is proposed to measure a team's estimation accuracy over time as an indicator of its maturity with the agile process. A second model is also proposed for estimating the number of story points a team can deliver. The results provide insights on agile estimation and new perspectives for scrum masters to monitor project health and team performance.
TEMPORALLY EXTENDED ACTIONS FOR REINFORCEMENT LEARNING BASED SCHEDULERSijscai
Temporally extended actions have been proved to enhance the performance of reinforcement learning agents. The broader framework of ‘Options’ gives us a flexible way of representing such extended course of action in Markov decision processes. In this work we try to adapt options framework to model an operating system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting for its execution. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and subsequent context switch leads to considerable overhead. In this work we try to utilize the historical performances of a scheduler and try to reduce the number of redundant context switches. We propose a machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better performing timeslice. We measure the importance of states, in option framework, by evaluating the impact of their absence and propose an algorithm to identify such checkpoint states. We present empirical evaluation of our approach in a maze-world navigation and their implications on "adaptive time slice parameter" show efficient throughput time.
Temporally Extended Actions For Reinforcement Learning Based Schedulers IJSCAI Journal
Temporally extended actions have been proved to enhance the performance of reinforcement learning
agents. The broader framework of ‘Options’ gives us a flexible way of representing such extended course of
action in Markov decision processes. In this work we try to adapt options framework to model an operating
system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting
for its execution. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and
subsequent context switch leads to considerable overhead. In this work we try to utilize the historical
performances of a scheduler and try to reduce the number of redundant context switches. We propose a
machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better
performing timeslice. We measure the importance of states, in option framework, by evaluating the impact
of their absence and propose an algorithm to identify such checkpoint states. We present empirical
evaluation of our approach in a maze-world navigation and their implications on "adaptive timeslice
parameter" show efficient throughput time.
Modern Software Methodologies(Agile ,Scrum & Lean) + CASE STUDY(Google)Aditya Taneja
This document discusses modern software methodologies like Agile and Scrum. It provides an overview of Agile principles like valuing individuals, working software, customer collaboration and responding to change. Specific Agile frameworks like Scrum and Kanban are described, including Scrum processes like sprints, stand-up meetings and prioritizing a backlog. Google's software development methodology is also summarized, which focuses on tools like Percolator, Dremel and Pregel for incremental processing, analytics and graph processing. The document concludes with an overview of Google's 20% time rule for employees to work on self-directed projects.
TEMPORALLY EXTENDED ACTIONS FOR REINFORCEMENT LEARNING BASED SCHEDULERSijscai
Temporally extended actions have been proved to enhance the performance of reinforcement learning
agents. The broader framework of ‘Options’ gives us a flexible way of representing such extended course of
action in Markov decision processes. In this work we try to adapt options framework to model an operating
system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting
for its execution. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and
subsequent context switch leads to considerable overhead. In this work we try to utilize the historical
performances of a scheduler and try to reduce the number of redundant context switches. We propose a
machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better
performing timeslice. We measure the importance of states, in option framework, by evaluating the impact
of their absence and propose an algorithm to identify such checkpoint states. We present empirical
evaluation of our approach in a maze-world navigation and their implications on "adaptive timeslice
parameter" show efficient throughput time.
Kanban is a lean methodology for managing workflow. It uses visual signals like cards to limit work-in-progress and optimize flow. Software teams can implement Kanban virtually with boards and cards to visualize work, standardize workflows, and identify blockers. Key benefits include planning flexibility, shortened cycle times from overlapping skills, fewer bottlenecks from limiting work-in-progress, and support for continuous delivery of value. Teams use metrics like control charts and cumulative flow diagrams to continually improve efficiency.
This document discusses Scrum, an agile framework for managing product development. It begins by providing a brief history of Scrum, noting that it originated from rugby terminology and emphasizes self-organizing teams. The document then outlines key Scrum concepts like the product backlog, sprints, increments, and the roles of the product owner and development team. It discusses when Scrum is and isn't applicable, such as for interrupt-driven work where Kanban may be better. The document also introduces the Cynefin framework for determining what approach fits different domains like simple, complicated, complex, chaotic, and disorder. It concludes by noting that while Scrum empowers teams, its implementation can be difficult and make problems visible
Site Reliability Engineering (SRE) is a concept where software engineers are hired to manage the reliability of products and services. SRE implements DevOps principles by automating manual system administration tasks and focusing on availability, performance, change management and monitoring through software. The key aspects of SRE include treating operations as a software problem to be solved through automation; managing services based on service level objectives; minimizing manual toil through automation; and sharing ownership of systems between SRE and product development teams.
The document discusses various concepts related to agile software development methodology including Scrum, Kanban, sprints, product and sprint backlogs, daily standups, planning and retrospective meetings. It provides details on Scrum roles like Product Owner and Scrum Master and their responsibilities. Various agile terms are defined like velocity, story boards, spikes, impediments and user stories. The advantages of the agile methodology are highlighted.
AI in Manufacturing: Opportunities & ChallengesTathagat Varma
AI has significant potential to create value in manufacturing through operational performance improvements, workforce augmentation, and sustainability gains. However, manufacturers often struggle to realize this value due to challenges such as a mismatch between AI capabilities and operational needs, a lack of strategic leadership and communication, insufficient cross-functional skills, and issues with data availability and governance. Addressing these adoption challenges will be key to unlocking the full promise of AI in manufacturing.
The document discusses key lessons learned from the COVID-19 pandemic and ongoing uncertainties. It emphasizes that preparing for future crises requires prioritizing people, culture, and resilience. Organizations need to embrace agility, technology, and interconnection to adapt quickly to changing situations. Leaders must build trust, transparency, and flexibility to inspire their teams during uncertain times. Overall, the document stresses using past crises like COVID-19 as opportunities to strengthen preparations for future uncertainties.
This document discusses leadership agility mindsets needed for successful digital transformations. It notes that the majority of change initiatives and digital transformations fail due to today's fast-paced environment with big, continuous changes. True leadership agility requires mindsets of continuously learning, getting help when needed, trusting one's team over being right, and building great teams through inspiration rather than just authority. Leaders must guide transformations through co-creation rather than just direction or management.
In this talk, I have discussed the issues around the need to recognize the business problem being solved, how to identify that, etc. rather than only focusing on the tech.
This document discusses various "cognitive chasms" that can occur during the adoption of artificial intelligence (AI) technologies. It identifies several phases of an AI project where adoption can fall off, such as moving from hype to practical technology implementation, piloting to full production, and initial scaling to achieving business impact. The document provides examples of AI initiatives that failed to progress between these stages due to challenges like unrealistic timelines, lack of data or organizational support, and difficulties demonstrating return on investment or making insights actionable. Overall, the document seeks to examine why many AI projects struggle to move beyond early stages of adoption.
In this talk for the students of IIM Udaipur, I have discussed how AI as technology needs to deliver business value in order for AI as a discipline to be seen as relevant to business. I have also spoken briefly about my own research work.
The document discusses various aspects of nurturing an innovation mindset. It defines innovation and outlines the innovation process. It emphasizes the importance of properly defining problems before attempting to solve them. Organizations need to prioritize problems and consider customers, financial impacts, and time constraints. Fostering an innovation mindset involves being purpose-driven, curious, and willing to take risks and experiment. The document also discusses intrapreneurship and sustaining innovation as ongoing business-as-usual activities through alignment, scaling, continuous integration, and cultural embedding.
What is #ThoughtLeadership? Is it mindless self-promotion, or is it more like some fancy management fad? Is it more like your social media presence, or sharing stories? What is the real deal here? In this talk, I have shared some ideas from others, and also some of my own learning over the years. Hope you find the answers you were looking for...
The document discusses the evolution of project management offices (PMOs) and how increasing complexity affects their performance. It notes that while PMOs were traditionally established to improve project satisfaction through standardized processes, this approach is ineffective in complex, unpredictable environments. As complexity rises from simple to chaotic, linear, mechanistic methods break down. Up to 75% of PMOs fail within 3 years due to not adapting to complexity and focusing only on compliance. A new approach is needed to help PMOs succeed in dynamic landscapes through principles like emergent strategy, learning, and building trust relationships. The Cynefin framework categorizes contexts from simple to complex/chaotic and suggests matching approaches to sensemaking and decision making.
An Introduction to the Systematic Inventive Thinking (SIT) MethodTathagat Varma
This document provides an introduction to Systematic Inventive Thinking (SIT), an innovation method that uses five thinking patterns - subtraction, division, multiplication, task unification, and attribute dependency - to generate creative ideas. These patterns help overcome cognitive biases like functional fixedness that stifle creativity. SIT adheres to principles like the path of most resistance, closed world, and function follows form. It involves defining an existing situation and then applying SIT tools to mentally manipulate the situation and visualize virtual products with novel functions.
The document discusses various frameworks for scaling agile practices in large organizations, including Scrum of Scrums, Nexus, Scrum@Scale, Large Scale Scrum (LeSS), and the Scaled Agile Framework (SAFe). It provides overviews of the key concepts of each framework, such as how they divide work among multiple agile teams, coordinate cross-team efforts, and scale agile principles and practices to the organizational level. The document also discusses some of the challenges of scaling agile and principles that informed the development of these frameworks.
How does one go about blogging? Or, why to even blog in the first place? In this talk, I have shared some of my key learning over last 15 years of blogging
Bridging the gap between Education and LearningTathagat Varma
This document summarizes a presentation about bridging the gap between education and learning in India, specifically for the IT industry. It outlines several problems with the current education system in India including low labor productivity, scientific research output, and number of patents. It also notes that many engineering graduates lack necessary job skills. The presentation identifies challenges such as outdated curriculum, lack of faculty, and fast-changing technology knowledge. It provides recommendations to develop more applied, experience-based education through partnerships with industry and use of new technologies.
This document discusses digital business model innovation and transformation. It begins with an overview of business models and the business model canvas tool. It then covers digital business models, the stages of digital transformation, and frameworks for digital business models. Key components of digital business models are discussed. The document outlines drivers of business model innovation and a framework for digital business model innovation. It also discusses challenges in digital transformation, such as why many initiatives fail. The document concludes with sections on business model innovation opportunities in different areas like resources, offerings, customers, and finances.
25 Years of Evolution of Software Product Management: A practitioner's perspe...Tathagat Varma
How has the role and function of product management evolved over the years? In this talk, I have shared my notes from my personal journey over the last 25 years.
I delivered a guest lecture for the students of the one-year Post Graduate program in Global Supply Chain Management offered by IIM Udaipur. In this talk, I focused on three dimensions of digital journey - technology, process (rather business models) and people.
Tathagat Varma discusses his journey of self-discovery as a technology strategy professional pursuing a doctorate. He shares that the journey has involved exploration across different fields like marketing, statistics, finance, and philosophy through various learning opportunities. Varma notes that amplifying one's learning is important for the journey and that achieving a state of productive confusion is something to be congratulated.
MYIR Product Brochure - A Global Provider of Embedded SOMs & SolutionsLinda Zhang
This brochure gives introduction of MYIR Electronics company and MYIR's products and services.
MYIR Electronics Limited (MYIR for short), established in 2011, is a global provider of embedded System-On-Modules (SOMs) and
comprehensive solutions based on various architectures such as ARM, FPGA, RISC-V, and AI. We cater to customers' needs for large-scale production, offering customized design, industry-specific application solutions, and one-stop OEM services.
MYIR, recognized as a national high-tech enterprise, is also listed among the "Specialized
and Special new" Enterprises in Shenzhen, China. Our core belief is that "Our success stems from our customers' success" and embraces the philosophy
of "Make Your Idea Real, then My Idea Realizing!"
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
GDG Cloud Southlake #34: Neatsun Ziv: Automating AppsecJames Anderson
The lecture titled "Automating AppSec" delves into the critical challenges associated with manual application security (AppSec) processes and outlines strategic approaches for incorporating automation to enhance efficiency, accuracy, and scalability. The lecture is structured to highlight the inherent difficulties in traditional AppSec practices, emphasizing the labor-intensive triage of issues, the complexity of identifying responsible owners for security flaws, and the challenges of implementing security checks within CI/CD pipelines. Furthermore, it provides actionable insights on automating these processes to not only mitigate these pains but also to enable a more proactive and scalable security posture within development cycles.
The Pains of Manual AppSec:
This section will explore the time-consuming and error-prone nature of manually triaging security issues, including the difficulty of prioritizing vulnerabilities based on their actual risk to the organization. It will also discuss the challenges in determining ownership for remediation tasks, a process often complicated by cross-functional teams and microservices architectures. Additionally, the inefficiencies of manual checks within CI/CD gates will be examined, highlighting how they can delay deployments and introduce security risks.
Automating CI/CD Gates:
Here, the focus shifts to the automation of security within the CI/CD pipelines. The lecture will cover methods to seamlessly integrate security tools that automatically scan for vulnerabilities as part of the build process, thereby ensuring that security is a core component of the development lifecycle. Strategies for configuring automated gates that can block or flag builds based on the severity of detected issues will be discussed, ensuring that only secure code progresses through the pipeline.
Triaging Issues with Automation:
This segment addresses how automation can be leveraged to intelligently triage and prioritize security issues. It will cover technologies and methodologies for automatically assessing the context and potential impact of vulnerabilities, facilitating quicker and more accurate decision-making. The use of automated alerting and reporting mechanisms to ensure the right stakeholders are informed in a timely manner will also be discussed.
Identifying Ownership Automatically:
Automating the process of identifying who owns the responsibility for fixing specific security issues is critical for efficient remediation. This part of the lecture will explore tools and practices for mapping vulnerabilities to code owners, leveraging version control and project management tools.
Three Tips to Scale the Shift Left Program:
Finally, the lecture will offer three practical tips for organizations looking to scale their Shift Left security programs. These will include recommendations on fostering a security culture within development teams, employing DevSecOps principles to integrate security throughout the development
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/07/intels-approach-to-operationalizing-ai-in-the-manufacturing-sector-a-presentation-from-intel/
Tara Thimmanaik, AI Systems and Solutions Architect at Intel, presents the “Intel’s Approach to Operationalizing AI in the Manufacturing Sector,” tutorial at the May 2024 Embedded Vision Summit.
AI at the edge is powering a revolution in industrial IoT, from real-time processing and analytics that drive greater efficiency and learning to predictive maintenance. Intel is focused on developing tools and assets to help domain experts operationalize AI-based solutions in their fields of expertise.
In this talk, Thimmanaik explains how Intel’s software platforms simplify labor-intensive data upload, labeling, training, model optimization and retraining tasks. She shows how domain experts can quickly build vision models for a wide range of processes—detecting defective parts on a production line, reducing downtime on the factory floor, automating inventory management and other digitization and automation projects. And she introduces Intel-provided edge computing assets that empower faster localized insights and decisions, improving labor productivity through easy-to-use AI tools that democratize AI.
Video traffic on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research and of industrial networked multimedia services certainly was the HTTP Adaptive Streaming (HAS) technique. This resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) which, together with HTTP Live Streaming (HLS), is widely used for multimedia delivery in today’s networks. Existing challenges in multimedia systems research deal with the trade-off between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, latency), and (iii) quality of experience (QoE). Optimizing towards one aspect usually negatively impacts at least one of the other two aspects if not both. This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry. In this talk, we will present selected novel approaches and research results of the first year of the ATHENA CD Lab’s operation. We will highlight HAS-related research on (i) multimedia content provisioning (machine learning for video encoding); (ii) multimedia content delivery (support of edge processing and virtualized network functions for video networking); (iii) multimedia content consumption and end-to-end aspects (player-triggered segment retransmissions to improve video playout quality); and (iv) novel QoE investigations (adaptive point cloud streaming). We will also put the work into the context of international multimedia systems research.
UiPath Community Day Kraków: Devs4Devs ConferenceUiPathCommunity
We are honored to launch and host this event for our UiPath Polish Community, with the help of our partners - Proservartner!
We certainly hope we have managed to spike your interest in the subjects to be presented and the incredible networking opportunities at hand, too!
Check out our proposed agenda below 👇👇
08:30 ☕ Welcome coffee (30')
09:00 Opening note/ Intro to UiPath Community (10')
Cristina Vidu, Global Manager, Marketing Community @UiPath
Dawid Kot, Digital Transformation Lead @Proservartner
09:10 Cloud migration - Proservartner & DOVISTA case study (30')
Marcin Drozdowski, Automation CoE Manager @DOVISTA
Pawel Kamiński, RPA developer @DOVISTA
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
09:40 From bottlenecks to breakthroughs: Citizen Development in action (25')
Pawel Poplawski, Director, Improvement and Automation @McCormick & Company
Michał Cieślak, Senior Manager, Automation Programs @McCormick & Company
10:05 Next-level bots: API integration in UiPath Studio (30')
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
10:35 ☕ Coffee Break (15')
10:50 Document Understanding with my RPA Companion (45')
Ewa Gruszka, Enterprise Sales Specialist, AI & ML @UiPath
11:35 Power up your Robots: GenAI and GPT in REFramework (45')
Krzysztof Karaszewski, Global RPA Product Manager
12:20 🍕 Lunch Break (1hr)
13:20 From Concept to Quality: UiPath Test Suite for AI-powered Knowledge Bots (30')
Kamil Miśko, UiPath MVP, Senior RPA Developer @Zurich Insurance
13:50 Communications Mining - focus on AI capabilities (30')
Thomasz Wierzbicki, Business Analyst @Office Samurai
14:20 Polish MVP panel: Insights on MVP award achievements and career profiling
Kief Morris rethinks the infrastructure code delivery lifecycle, advocating for a shift towards composable infrastructure systems. We should shift to designing around deployable components rather than code modules, use more useful levels of abstraction, and drive design and deployment from applications rather than bottom-up, monolithic architecture and delivery.
Quality Patents: Patents That Stand the Test of TimeAurora Consulting
Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality.
Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality.
Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality.
Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank?
** Episode Overview **
In this first episode of our quality series, Kristen Hansen and the panel discuss:
⦿ What do we mean when we say patent quality?
⦿ Why is patent quality important?
⦿ How to balance quality and budget
⦿ The importance of searching, continuations, and draftsperson domain expertise
⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications
https://www.aurorapatents.com/patently-strategic-podcast.html
An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)
In this follow-up session on knowledge and prompt engineering, we will explore structured prompting, chain of thought prompting, iterative prompting, prompt optimization, emotional language prompts, and the inclusion of user signals and industry-specific data to enhance LLM performance.
Join EIS Founder & CEO Seth Earley and special guest Nick Usborne, Copywriter, Trainer, and Speaker, as they delve into these methodologies to improve AI-driven knowledge processes for employees and customers alike.
AC Atlassian Coimbatore Session Slides( 22/06/2024)apoorva2579
This is the combined Sessions of ACE Atlassian Coimbatore event happened on 22nd June 2024
The session order is as follows:
1.AI and future of help desk by Rajesh Shanmugam
2. Harnessing the power of GenAI for your business by Siddharth
3. Fallacies of GenAI by Raju Kandaswamy
Details of description part II: Describing images in practice - Tech Forum 2024BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and transcript: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
5. However…it is
taking too long
to develop and
deploy…!
u 2-6 months depending on
scope and size. Data
Collection (20%), Data
Cleaning (50%), Data
Exploration (15%), Data
Modeling (10%), Data
Interpretation (5%)
u The time required to deploy
a model is increasing year-
on-year.
u Only 11% of organizations
can put a model into
production within a week,
and 64% take a month or
longer
https://info.algorithmia.com/2021
6. The time of Data Scientists being spent in deploying the
models…and more models means more time spent in
deployment…!
https://info.algorithmia.com/2021
7. …with alarmingly high failure rates!
u It was estimated that 85% of AI projects will fail
and deliver erroneous outcomes through 2022.
u 70% of companies report minimal or no impact
from AI.
u 87% of data science projects never make it into
production.
https://research.aimultiple.com/ai-fail/
8. Low ROI, & Long Payback periods!
u The ROI for AI projects varies greatly, based on
how much experience an organization has.
Leaders showed an average of a 4.3% ROI for
their projects, compared to only 0.2% for
beginning companies.
u Payback periods also varied, with leaders
reporting a typical payback period of 1.2 years
and beginners at 1.6 years.
https://www2.deloitte.com/us/en/insights/industry/technology/artificial-intelligence-roi.html
9. How is AI different?
Traditional Software AI Software
Reasoning Deductive Inductive
Inputs Data + Program Data + Output
Logic Manually pre-programmed to perform a
specific task on a given dataset
Programmed to automatically keep learning
rules from a given dataset
Output Output Models, Rules
Learning Learns one-time from the programmer Learns constantly being the data
Resource Code Data
Solutions Deterministic Probabilistic
Output Consistently remains the same Can improve with usage (or degrade over time)
Business
model
One-time development efforts, followed by
multiple sales, and small maintenance effort
(optional)
Each project is one-off, and needs full lifecycle
management mandatorily
10. Elements of ML systems
https://www.ibm.com/cloud/blog/ai-model-lifecycle-management-overview
11. A typical lifecycle for an AI project
u Scoping and Data
Acquisition
u Experimentation
and Model Building
u Production,
Deployment,
Scaling and
Operationalize
12. Data, data, data…!
u Industry reports indicate up
to 80% efforts in data
wrangling!
u Upto 1/4th of that only in
cleaning and another 1/4th
in labeling
u Just 10% of the time spend
in model training!
https://medium.com/whattolabel/data-labeling-ais-human-bottleneck-24bd10136e52
13. Data trumps algorithms!
In the article “Datasets Over Algorithms”, Alexander Wissner-Gross showed that
the mean time between a new machine learning algorithm being published
and its use in an AI breakthrough was 18 years; however, the mean time
between the required datasets becoming available and those AI
breakthroughs was 3 years. Machine learning without the necessary data and
use cases is merely a pile of nuts and bolts waiting to be built into something
useful. Nonetheless, machine learning is about learning from data, not about
writing code, and that represents a fundamental difference from previous
software engineering practices.
- Agile AI, Carlo Appugliese, Paco Nathan, and William S. Roberts, O’Reilly
14. Data lifecycle
u While CRISP-DM (Cross
Industry Standard
Process for Data
Mining) lifecycle seems
to be a bit dated
(published 1999) and
inactive, it is still a good
reference point on the
key phases of data
lifecycle
u Flows are not
sequential but
back/forth
https://www.ibm.com/docs/en/spss-modeler/SaaS?topic=dm-crisp-help-overview
15. Generic
tasks and
outputs in a
CRISP-DM
Reference
Model
https://www.ibm.com/docs/en/spss-modeler/SaaS?topic=dm-crisp-help-overview
16. CRISP-DM favored over agile methodologies?
https://www.datascience-pm.com/crisp-dm-still-most-popular/
17. Challenges with Scrum in Data
Science projects
u One key challenge of using a sprint-based framework within a data
science context is the fact that task estimation is unreliable. In other words,
if the team can not accurately estimate task duration, the concept of a
sprint, and what can get done within a sprint is problematic.
u Another key challenge is that Scrum’s fixed-length sprints can be
problematic. Even if a team could estimate how long a specific analysis
might take, having a fixed-length sprint might force the team to define an
iteration to include unrelated work items (as well as delay the feedback
from an exploratory analysis), which could help prioritize new work. In short,
a sprint does not allow smaller (or longer) logical chunks of work to be
completed and analyzed in a coherent fashion.
https://www.datascience-pm.com/data-driven-agile/
18. Challenges with traditional Kanban in
Data Science projects
u In general, these challenges include the lack of organizational support and
culture, lack of training and the misunderstanding of key concepts.
u Specifically, Kanban does not define project roles nor any process
specifics.
u The freedom Kanban provides (such as letting teams define their own
process for prioritizing tasks) can be part of the challenge in implementing
Kanban. While this lack of process structure can be a strength (since the
lack of a specified process definition allows teams to implement Kanban
within existing organizational practices), it can also mean that every team
could implement Kanban differently. In other words, a team that wants to
use Kanban needs to figure out its own processes and artifacts.
https://www.datascience-pm.com/data-driven-agile/
19. Data-Driven Scrum (DDS)
u The Data Science Process
Alliance created an alternative
framework called Data Driven
Scrum which is designed with data
science in mind.
u Data Driven Scrum™ (DDS) is
an agile framework specifically
designed for data science teams. DDS
provides a continuous flow framework
for agile data science by integrating
the structure of Scrum with the
continuous flow of Kanban.
https://www.datascience-pm.com/data-driven-scrum/
20. Leveraging Scrum and Kanban…
u DDS can be viewed as a specific instantiation of Scrum with two notable
exceptions:
u The most important exception is that the Scrum Guide requires all iterations (sprints) to be
of equal length in time. However, iterations in DDS vary in duration to allow a logical
increment of work to be done in one iteration (rather than defining the amount of work
that can be done in a specific unit of time).
u The other notable exception is that retrospectives and item reviews are not done at the
end of every iteration, but rather, on a frequency the team deems appropriate.
u DDS also adheres to the Kanban principles (e.g., there is a Kanban board, teams
need to limit WIP, and work items flow across the board). However, the framework
provides more structure than defined by Kanban, such as defined iterations as well
as a more defined framework (ex. roles and meetings). Having a more clearly
defined process that leverages agile best practices, will enable teams to
implement the process in a more consistent and repeatable manner.
https://www.datascience-pm.com/data-driven-scrum/
21. Key Tenets of DDS
u Agile is Iterative Experimentation
Agile is intended to be a sequence of iterative experimentation and adaptation cycles.
u Iterations are Capacity-Based
Teams work iteratively on a given set of items until they are done (no inflexible deadlines).
u Focus on Create, Observe, Analyze
Each iteration always follows three core steps: Create something, observe its performance,
and analyze the results.
u Easily Integrate with Scrum
DDS’s interfaces can be seamlessly integrated within a traditional Scrum-based
organization.
https://www.datascience-pm.com/data-driven-scrum/
22. DDS vs Traditional Scrum: Similarities
u Similar Roles
Just like traditional Scrum, each DDS team is a group of up to about ten people,
one of whom is the product owner, and one of whom is the process expert.
u Similar Events
Just as in traditional Scrum, there is a daily stand-up, as well as Iteration and
Retrospective Reviews.
u Similar Process to create and prioritize Items
Just like traditional Scrum, items are created, prioritized and viewed on a task
board.
https://www.datascience-pm.com/data-driven-scrum/
23. DDS vs Traditional Scrum: Differences
u Functional Iterations
DDS iterations have unknown and varying length iterations (as compared to traditional Scrum sprints, which
have fixed-time durations). This enables iterations that might make sense to be shorter or longer than
average (e.g., an iteration might be shorter than normal due to being able to learn from a quick / short
experiment).
u Uncertain Task Duration
Unlike traditional Scrum (which requires accurate task estimations to know what can fit into a sprint), DDS
naturally accommodates tasks that are difficult to estimate (and task estimation is often difficult within a
data science context).
u Collective Analysis
The entire team focuses on creating, observing and then analyzing an hypothesis, analysis or feature (often
in traditional scrum, this analysis is done by the product owner outside of the codified process).
u Iteration-Independent Meetings
Retrospectives and item reviews and not done at the end of every iteration (as is done in traditional
Scrum), but rather, on a calendar-based frequency the team deems appropriate.
https://www.datascience-pm.com/data-driven-scrum/
24. Principles of DDS
u Allow capability-based iterations – it might be that sometimes it makes sense to
have an iteration that lasts one day, and other times, for an iteration last three
weeks (ex. due to how long it takes to acquire / clean data or how long it takes for
an exploratory analysis). The goal should be to allow logical chunks of work to be
released in a coherent fashion.
u Decoupling meetings from an iteration – since an iteration could be very short (ex.
one day for a specific exploratory analysis), meetings (such as a retrospective to
improve the team’s process) should be based on a logical time-based window, not
linked to each iteration.
u Only require high-level item estimation – In many situations, defining an explicit
timeline for an exploratory analysis is difficult, so one should not need to generate
accurate detailed task estimations in order to use the framework. But, high-level “T-
Shirt” level of effort estimates can be helpful for prioritizing the potential tasks to be
done.
https://www.datascience-pm.com/data-driven-agile/
25. DDS Framework
u Data Driven Scrum supports lean iterative exploratory data science analysis,
and acknowledges that iterations will vary in length due to the phase of the
project (collecting data vs creating a machine learning analysis).
u DDS defines an agile lean process framework that leverages some of the key
concepts of Scrum as well as the key concepts of Kanban, but differently than
Scrumban (which as is more of Kanban within a Scrum Framework and hence,
Scrumban implements Scrum sprints, which as previously noted, introduces
several challenges for the project team).
u In short, DDS teams use a Kanban-like visual board and focus on working on a
specific item or collection of items during an iteration, which is task-based, not
time-boxed. Thus, an iteration more closely aligns with the lean concept of
pulling tasks, in a prioritized manner, when the team has capacity. Each
iteration can be viewed as validating or rejecting a specific lean hypothesis.
https://www.datascience-pm.com/data-driven-agile/
26. Steps in a DDS Iteration
Create: A thing or set of
things that will be created,
put into use with a
hypothesis about what will
happen.
Observe: A set of
observable outcomes of
that use that will be
measured (and any work
that is needed to facilitate
that measurement).
Analyze: Analyzing those
observables and create a
plan for the next iteration
https://www.datascience-pm.com/data-driven-agile/
28. Scaling DDS
The DDS framework is a single team
framework that is designed to be
compatible with the Scrum@Scale
scaling framework.
Each DDS team exposes the
necessary interfaces to collaborate
with other teams (each of which
might be doing Scrum or DDS) via
its roles and artifacts, while
encapsulating its internal workflow.
Team touchpoint DDS Scrum
Metascrum
representation
Product Owner Product owner
Scrum of Scrums
representation
Process Master Scrum Master
Product / release
feedback
Iteration Review Sprint Review
Metrics and
transparency
Item Backlog /
Taskboard
Product Backlog /
Sprint Backlog
29. Recap
u AI / DS / ML is an evolving field, with long development /
deployment cycles, high failure rates and low ROI.
u It is still a software, but yet, not quite like the traditional
software in many ways!
u While agile principles are rather generic problem-solving
methods, some ideas don’t quite apply well.
u Data-Driven Scrum offers an interesting perspective for
delivering DS projects with agility.
u For deployment, AIOps / MLOps orchestration platforms
are fast emerging to provide necessary tool support.