This session provides an analysis of the machine learning market in the enterprise. The analysis includes vendors, platforms and best practices that should be considered by companies implementing data science solutions at an enterprise scale
Lessons from Large-Scale Cloud Software at DatabricksMatei Zaharia
1) Building cloud software presents unique challenges compared to on-premise software, such as the need for faster release cycles, upgrades without regressions, and multitenancy.
2) Scaling issues are a major cause of outages for cloud systems, including problems reaching resource limits and insufficient isolation between users.
3) Testing cloud systems requires evaluating how they scale and handling varying loads, and failures can indicate problems with dimensions like output size or number of tasks.
1) Databricks provides a machine learning platform for MLOps that includes tools for data ingestion, model training, runtime environments, and monitoring.
2) It offers a collaborative data science workspace for data engineers, data scientists, and ML engineers to work together on projects using notebooks.
3) The platform provides end-to-end governance for machine learning including experiment tracking, reproducibility, and model governance.
Big Data - in the cloud or rather on-premises?Guido Schmutz
You want to implement an Big Data/IoT solution and would like to know if it should be implemented in the cloud or on-premises. You are interested in the cloud offerings of vendors and what benefits they provide and if a similar solution would not be possible on-premises.
This presentation deals with this and other questions. Starting from an vendor-independent reference architecture and corresponding design patterns, different cloud solutions from various vendors are compared and rated. Additionally it will be shown how such solution could be implemented on-premises and how a hybrid Big Data/IoT solution could look like.
- Cloud computing is important for big data applications as it provides variable expense, elastic capacity, and global reach. Amazon Web Services provides data storage, processing, and analytics services across a global network of regions and availability zones.
- Amazon Redshift is a fully managed data warehouse service that allows for fast queries on petabytes of structured data using standard SQL. It uses a columnar data storage format and data compression techniques to improve performance and reduce costs.
- Amazon EMR allows users to easily run Hadoop frameworks like Hive and Pig on AWS without having to manage hardware. It provides a scalable and cost-effective way to process vast amounts of unstructured data in Amazon S3.
- Amazon Kinesis enables real-
Fundamentals Big Data and AI ArchitectureGuido Schmutz
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Modern business is fast and needs to take decisions immediatly. It cannot wait that a traditional BI task that works on data snapshots at some time. Social data, Internet of Things, Just in Time don't undestand "snapshot" and needs working on streaming, live data. Microsoft offers a PaaS solution to satisfy this need with Azure Stream Analytics. Let's see how it works.
The Event Mesh: real-time, event-driven, responsive APIs and beyondSolace
Phil Scanlon, Head of Technology in Asia Pacific & Japan for Solace, describes "The Event Mesh" at API Days Melbourne in September 2018. Scanlon explains the complexities of the Event Mesh using the evolution to event-driven, the anatomy of an event, and real world examples.
Migrating Your Data Platform At a High Growth StartupDatabricks
Migrating their data platform from AWS EMR and notebooks to Databricks, Abnormal Security conducted a two-week proof-of-concept that was successful. They are now migrating jobs ranked by cost to Databricks' configuration framework over the first quarter to reduce costs by 50% while gaining improved usability, operational overhead, and ability to scale. The migration of their platform to a single environment using Databricks will allow them to build their first data lakehouse and gain additional future use cases as the company grows rapidly.
Mainframe Modernization with Precisely and Microsoft AzurePrecisely
Today’s businesses are leveraging Microsoft Azure to modernize operations, transform customer experience, and increase profit. However, if the rich data generated by the mainframe applications is missed in the move to the cloud, you miss the mark.
Without the right solutions in place, migrating mainframe data to Microsoft Azure is expensive, time-consuming, and reliant on highly specialized skillsets. Precisely Connect can quickly integrate mainframe data at scale into Microsoft Azure without sacrificing functionality, security, or ease of use.
View this on-demand webinar to hear from Microsoft Azure and Precisely data integration experts. You will:
- Learn how to build highly scalable, reliable data pipelines between the mainframe and Microsoft Azure services
- Understand how to make your Microsoft Azure implementation ready for mainframe
- Dive into case studies of businesses that have successfully included mainframe data in their cloud modernization efforts with Precisely and Microsoft Azure
The Power Platform consists of four main components: Power Apps, Power Automate, Power BI, and Power Virtual Agents. It allows users to build custom apps, automate workflows, analyze data, and create virtual agents. Data can be connected through over 275 available connectors. Triggers prompt automated flows to begin, while actions allow interaction with data sources. Power Apps is a low-code/no-code platform to build apps that work with data from various sources. Features include AI capabilities and model-driven apps. Case studies show how organizations like Heathrow Airport have used Power Apps to empower workers. The document provides demonstrations of building a basic app, using functions, and sharing an app with other users or groups.
Data Lake and the rise of the microservicesBigstep
By simply looking at structured and unstructured data, Data Lakes enable companies to understand correlations between existing and new external data - such as social media - in ways traditional Business Intelligence tools cannot.
For this you need to find out the most efficient way to store and access structured or unstructured petabyte-sized data across your entire infrastructure.
In this meetup we’ll give answers on the next questions:
1. Why would someone use a Data Lake?
2. Is it hard to build a Data Lake?
3. What are the main features that a Data Lake should bring in?
4. What’s the role of the microservices in the big data world?
Azure architecture design patterns - proven solutions to common challengesIvo Andreev
Building a reliable, scalable, secure applications could happen either following verified design patterns or the hard way - following the trial and error approach. Azure architecture patterns are a tested and accepted solutions of common challenges thus reducing the technical risk to the project by not having to employ a new and untested design. However, most of the patterns are relevant to any distributed system, whether hosted on Azure or on other cloud platforms.
The Basics of Getting Started With Microsoft AzureMicrosoft Azure
The document describes various capabilities provided by Microsoft Azure including hosting virtual machines and web applications, mobile backend services, cloud services, storage options, SQL databases, media services, integration services, identity and access management, virtual networking, and infrastructure as a service. It provides details on virtual machine sizes, disks, networking, security, backups, and cross-premise connectivity in Azure.
Easy Analytics on AWS with Amazon Redshift, Amazon QuickSight, and Amazon Mac...Amazon Web Services
AWS has a large and growing portfolio of big data management and analytics services, designed to be integrated into solution architectures that meet the needs of your business. In this session, we look at analytics through the eyes of a business intelligence analyst, a data scientist, and an application developer, and we explore how to quickly leverage Amazon Redshift, Amazon QuickSight, RStudio, and Amazon Machine Learning to create powerful, yet straightforward, business solutions.
This document contains an agenda for a presentation on Azure Stream Analytics. The agenda includes topics such as analytics in a modern world, why developers are interested in analytics, why use the cloud for analytics, an introduction to Azure Stream Analytics, the Azure Stream Analytics architecture, the Stream Analytics Query Language (SAQL), handling time in Azure Stream Analytics, scaling analytics, and conclusions. The document also includes speaker information and notes on various topics from the agenda.
Value Journal, a monthly news journal from Redington Value Distribution, intends to update the channel on the latest vendor news and Redington Value’s Channel Initiatives.
Key stories from the July Edition:
• VMware introduces integrations with Dell EMC to accelerate workforce transformation
• HPE unveils new high-performance computing solution
• Weathering the cybersecurity storm - Hishamul Hasheel VP, Software and Security, Redington Gulf - Value Distribution
• AWS Greengrass is now available
• CyberArk acquires Conjur for $42 Million
• Fidelis Cybersecurity releases endpoint cloud solution
• Fortinet Threat Landscape report highlights most threats are opportunistic
• Fujitsu rolls out new biometric authentication solution
• Trend Micro launches $100 Million venture fund
• Malwarebytes unveils inaugural channel programme
• Red Hat Ceph storage release expands versatility as object store
• Veeam and Microsoft extend alliance to deliver availability for always-on cloud
Introducing Apache Kafka and why it is important to Oracle, Java and IT profe...Lucas Jellema
Events are playing an increasingly important role in modern application architecture. They represent fast, streaming data, they fuel the interaction between microservices, they are at the core of CQRS and event sourcing. Apache Kafka has quickly emerged as the de facto standard event platform: open source, cross technology, reliable and extremely scalable and available on any platform, in Docker and from the major cloud platforms- including Oracle Cloud’s Event Hub service. This session explains the what, why and how of Apache Kafka. What role does it play, how is it used and what are challenges and tricks for real life applications. How does it fit in with Oracle Database and Fusion Middleware and with Oracle Public Cloud? In several demos, Kafka is seen at work - in real time streaming event analysis through KSQL, in CQRS and microservices scenarios and with user interfaces updated in real time through events and HTML5 server sent events.
This presentation includes a demonstration of remote database synchronization through Twitter.
Modernizing your Application Architecture with Microservicesconfluent
Organizations are quickly adopting microservice architectures to achieve better customer service and improve user experience while limiting downtime and data loss. However, transitioning from a monolithic architecture based on stateful databases to truly stateless microservices can be challenging and requires the right set of solutions.
In this webinar, learn from field experts as they discuss how to convert the data locked in traditional databases into event streams using HVR and Apache Kafka®. They will show you how to implement these solutions through a real-world demo use case of microservice adoption.
You will learn:
-How log-based change data capture (CDC) converts database tables into event streams
-How Kafka serves as the central nervous system for microservices
-How the transition to microservices can be realized without throwing away your legacy infrastructure
Ai & Data Analytics 2018 - Azure Databricks for data scientistAlberto Diaz Martin
This document summarizes a presentation given by Alberto Diaz Martin on Azure Databricks for data scientists. The presentation covered how Databricks can be used for infrastructure management, data exploration and visualization at scale, reducing time to value through model iterations and integrating various ML tools. It also discussed challenges for data scientists and how Databricks addresses them through features like notebooks, frameworks, and optimized infrastructure for deep learning. Demo sections showed EDA, ML pipelines, model export, and deep learning modeling capabilities in Databricks.
Introduction to Machine learning and Deep LearningNishan Aryal
Overview of Machine Learning and Deep Learning. Brief introduction to different types of BI Reporting tools like Power BI, SSMS, Cortana, Azure ML, TenserFlow and other tools.
Slides from my talk at Big Data Conference 2018 in Vilnius
Doing data science today is far more difficult than it will be in the next 5-10 years. Sharing, collaborating on data science workflows in painful, pushing models into production is challenging.
Let’s explore what Azure provides to ease Data Scientists’ pains. What tools and services can we choose based on a problem definition, skillset or infrastructure requirements?
In this talk, you will learn about Azure Machine Learning Studio, Azure Databricks, Data Science Virtual Machines and Cognitive Services, with all the perks and limitations.
IncQuery Labs provides cloud-based modeling solutions to enable tool integration in model-based systems engineering (MBSE). Their IncQuery tool suite includes a desktop query authoring tool and backend server that allows running complex queries on large models. IncQuery was used to develop an interoperability platform for Airbus that automates workflows involving transformations between modeling tools and generates reports through a web interface.
Google AutoML, AWS SageMaker and other ML tools automate some but not all steps in machine learning workflows. Learn about problem formulation, data engineering, monitoring, and fairness assessment.
IncQuery Server for Teamwork Cloud - Talk at IW2019Istvan Rath
IncQuery Server provides scalable query evaluation over collaborative model repositories. It uses a hybrid database technology that is 10-100x faster than conventional databases and supports large models and complex queries. IncQuery Server integrates with MagicDraw and Teamwork Cloud to enable version control, access control, and customizable queries for model validation and impact analysis.
Getting to 1.5M Ads/sec: How DataXu manages Big DataQubole
DataXu sits at the heart of the all-digital world, providing a data platform that manages tens of millions of dollars of digital advertising investments from Global 500 brands. The DataXu data platform evaluates 1.5 million online ad opportunities every second for our customers, allowing them to manage and optimize their marketing investments across all digital channels. DataXu employs a wide range of AWS services: Cloud Front, Cloud Trail, CloudWatch, Data Pipeline, Direct Connect, Dynamo DB, EC2, EMR, Glacier, IAM, Kinesis, RDS, Redshift, Route53, S3, SNS, SQS, and VPC to run various workloads at scale for DataXu data platform.
In addition, DataXu also uses Qubole Data Service, QDS, to offer a Unified Analytics Interface tool to DataXu customers. Qubole, a member of APN provides self-managing Big data infrastructure in the Cloud which leverages spot pricing for cost-efficiencies, provides fast performance, and most importantly a streamlined user-interface for ease of use.
Attendees will learn how Qubole provided self-managing Hadoop clusters in the AWS Cloud accelerated DataXu’s batch-oriented analysis jobs; and how Qubole integration with Amazon Redshift enabled DataXu to preform low latency and interactive analysis. Further, in the session we'll take a look at how DataXu opened up QDS access to their customers using QDS user interface thereby providing them with a single tool for both batch-oriented and interactive analysis. By using the QDS user interface buyers of the DataXu data service could perform all manner of analysis against the data stored in their AWS S3 bucket.
Speakers:
Scott Ward
Solutions Architect at Amazon Web Services
Ashish Dubey
Solutions Architect at Qubole
Yekesa Kosuru
VP Engineering at DataXu
The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.
This presentation covers an overview of Analytics and Machine learning. It also covers the Microsoft's contribution in Machine learning space. Azure ML Studio, a SaaS based portal to create, experiment and share Machine Learning Solutions to the external world.
In this session we will delve into the world of Azure Databricks and analyze why it is becoming a tool for data Scientist and/or fundamental data Engineer in conjunction with Azure services
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
A Collaborative Data Science Development WorkflowDatabricks
Collaborative data science workflows have several moving parts, and many organizations struggle with developing an efficient and scalable process. Our solution consists of data scientists individually building and testing Kedro pipelines and measuring performance using MLflow tracking. Once a strong solution is created, the candidate pipeline is trained on cloud-agnostic, GPU-enabled containers. If this pipeline is production worthy, the resulting model is served to a production application through MLflow.
Microsoft Azure BI Solutions in the CloudMark Kromer
This document provides an overview of several Microsoft Azure cloud data and analytics services:
- Azure Data Factory is a data integration service that can move and transform data between cloud and on-premises data stores as part of scheduled or event-driven workflows.
- Azure SQL Data Warehouse is a cloud data warehouse that provides elastic scaling for large BI and analytics workloads. It can scale compute resources on demand.
- Azure Machine Learning enables building, training, and deploying machine learning models and creating APIs for predictive analytics.
- Power BI provides interactive reports, visualizations, and dashboards that can combine multiple datasets and be embedded in applications.
This document discusses cloud-native data and patterns for managing data in microservices architectures. It describes using data services and APIs to interface with existing data sources. Patterns like caching data at the edge with various caching strategies are discussed. The document also covers using multiple small databases with each microservice rather than a shared database. Event sourcing and CQRS patterns are presented as ways to integrate data across services. Finally, the impact on roles like database administrators is considered in cloud-native data environments.
<November 2017 Updated from earlier presentations on Cloud-native Data>
Cloud-native applications form the foundation for modern, cloud-scale digital solutions, and the patterns and practices for cloud-native at the app tier are becoming widely understood – statelessness, service discovery, circuit breakers and more. But little has changed in the data tier. Our modern apps are often connected to monolithic shared databases that have monolithic practices wrapped around them. As a result, the autonomy promised by moving to a microservices application architecture is compromised.
What we need are patterns and practices for cloud-native data. The anti-patterns of shared databases and simple proxy-style web services to front them give way to approaches that include use of caches (Netflix calls caching their hidden microservice), database per service and polyglot persistence, modern versions of ETL and data integration and more. In this session, aimed at the application developer/architect, Cornelia will look at those patterns and see how they serve the needs of the cloud-native application.
Microsoft Data Platform Airlift 2017 Rui Quintino Machine Learning with SQL S...Rui Quintino
The document discusses machine learning with SQL Server 2016 and R Services. It provides an overview of machine learning, R programming language, and the challenges of using R with SQL databases prior to SQL Server 2016. SQL Server 2016 introduces R Services, which allows running R code directly in the database for high performance, scalable machine learning. R Services integrates R with SQL Server through in-database deployment and parallel processing capabilities. This eliminates data movement and scaling issues while leveraging existing R and SQL skills.
Deploying ML models in production, with or without CI/CD, is significantly more complicated than deploying traditional applications. That is mainly because ML models do not just consist of the code used for their training, but they also depend on the data they are trained on and on the supporting code. Monitoring ML models also adds additional complexity beyond what is usually done for traditional applications. This talk will cover these problems and best practices for solving them, with special focus on how it's done on the Databricks platform.
1. DevOps and machine learning can be combined through the use of Azure Machine Learning pipelines. Pipelines allow the creation of workflows for data preparation, model training, and model deployment.
2. Azure Machine Learning pipelines support unattended runs, reusability, and tracking of experiments. They can integrate with data sources, compute targets, and model management.
3. Continuous integration and delivery practices like source control, code quality testing, and controlled deployments can be applied to machine learning models through the use of Azure Pipelines and Azure Machine Learning services. This allows models to be deployed and updated reliably in production environments.
This document discusses data science and machine learning concepts and tools. It introduces the IBM Data Science Experience (DSX) and Watson Machine Learning (WML) products, which provide environments for data scientists and developers to build machine learning models. DSX offers notebooks, IDEs and collaboration tools, while WML focuses on visual model creation, access to algorithms, full ML workflows and APIs. It then demonstrates these products.
Similar to A practical guidance of the enterprise machine learning (20)
DeFi is moving towards more granular "micro-primitives" that break down protocols into smaller, modular units. Examples include Uniswap v4 hooks, EigenLayer marketplaces, and Flashbots' decomposed MEV roles. Micro-primitives enable composability but increase complexity and the attack surface. While they can enrich functionality, there is a risk of further fragmenting DeFi without capturing value through applications.
This presentation presents an overview of the challenges and opportunities of generative artificial intelligence in Web3. It includes a brief research history of generative AI as well as some of its immediate applications in Web3.
Maximal extractable value(MEV) is one of the most debated topics in crypto. This session discusses some of the technical architectures, opportunities and challenges that MEV traders and developers should explore.
This session explores the unique aspects of quantitative trading strategies applied to cryptocurrencies. The session covers topics such as challenges of crypto quant strategies, DeFi and many others.
Yield farming or liquidity mining have been at the core of the recent boom of DeFi protocols. From a trading perspective, yield-generating strategies are producing incredibly attractive returns compared to similar strategies traditional capital markets. How to build yield-generating DeFi strategies that correctly balance risk-rewards?
This session discusses the new world of DeFi quant yield-generating strategies. We discuss key building blocks required to implement intelligent DeFi quant strategies in an institutional-grade manner. The session will discuss how to think about elements such as risk quantification, back testing , simulations , protocol interactions and many others in the context of DeFi yield-generating strategies.
This session presents some ideas, lessons learned and techniques used to build high frequency trading strategies in decentralized finance(DeFi). The deck describes some key practical tips that can help quants build HFT strategies for the new word of DeFi.
Simple DeFi Analytics Any Crypto-Investor Should Know About Jesus Rodriguez
This session provides an overview of basic indicators that will help traders and investors better understand DeFi protocols. The session covers unique analytics and visualizations that reveal fascinating insights the top DeFi projects in the market.
This session provides an overview of analytics for decentralized finance(DeFi) protocols. The session also outlines some ideas about the future of market intelligence and DeFi.
DeFi Trading Strategies: Opportunities and ChallengesJesus Rodriguez
This deck discusses some ideas about trading opportunities in the DeFi ecosystem as well as the challenges and risks. The content presents a conceptual framework to think about DeFi quant strategies
This presentations outlines some of the key principles for building deep learning predictive models for crypto assets. The deck includes best practices and lessons learned that provide some perspectives about the challenges and solutions about using deep learning models in the crypto space.
Better Technical Analysis with Blockchain IndicatorsJesus Rodriguez
The document discusses how technical analysis of cryptocurrency assets can be improved by incorporating blockchain indicators. It provides examples of how traditional technical analysis indicators like Fibonacci retracement levels, exponential moving averages, and Bollinger bands can be reinforced with complementary blockchain data on in-out money flows, exchange flows, unspent transaction output analysis, and active addresses. By combining on-chain behavioral data with price-based technical analysis, traders may gain a more robust view of market trends and investor sentiment. The document concludes that technical analysis patterns can inform blockchain indicators and vice versa, representing a promising new approach to cryptocurrency market evaluation.
This slide deck details some of the lessons we learned building price prediction models for cryptocurrencies. The session provides examples and practical tips about the challenges of price predictions in crypto asset markets.
Fascinating Metrics and Analytics About CryptocurrenciesJesus Rodriguez
This document discusses the need for a new approach to analyzing cryptocurrency assets using data science. It argues that cryptocurrencies generate far more behavioral data than traditional assets through their public ledgers. This rich blockchain data can provide insights into metrics like the number of traders profiting from each asset, how long investors hold assets, geographic trading patterns, and concentration among large holders. The document presents examples of data analyses for various cryptocurrencies that could help monitor market trends, predict price movements, and identify risks around exchanges. In conclusion, it advocates applying data science to simplify cryptocurrency analysis and unlock insights from their unique blockchain datasets.
Price PRedictions for Crypto-Assets Using Deep LearningJesus Rodriguez
This slide deck provides an overview of the universe of prediction techniques applied to cryptocurrencies. The content covers emerging prediction models in the deep learning field and how they apply to crypto-assets.
Demystifying Centralized Crypto Exchanges using Data ScienceJesus Rodriguez
Centralized exchanges are one of the most obscure and difficult to understand elements in the crypto landscape. From fake volumes to transaction transformations, centralized exchanges introduce a level of obfuscation that challenges even the most sophisticated analytic techniques. How can we learn to identify and understand the behavior of centralized crypto exchanges?
This session showcases a series of machine learning and data visualization techniques that help us better understand some of the patterns of crypto exchanges. Using gorgeous data visualizations, we will walk you through a journey that clearly illustrates how exchanges process transactions and distribute crypto-assets across their different addresses. Finally, we will illustrate how certain behaviors of crypto exchanges become relevant to specific patterns in the crypto market.
This session provides an outline of data science techniques for crypto-assets. The content introduces the notion of crypto asset fundamental analysis and highlights some shocking data about crypto-assets
Implementing Machine Learning in the Real WorldJesus Rodriguez
This document outlines 15 lessons learned from building large-scale machine learning systems in the real world. Some key challenges discussed include data scientists not being well-suited for engineering work, traditional development methodologies not working for machine learning, the difficulty of data labeling and feature extraction, and the complexities of training, executing, operationalizing, and securing machine learning models at scale. The document provides ideas to address these challenges such as establishing separate data science and engineering teams, implementing automated data labeling strategies, leveraging centralized feature stores, and adopting techniques like transfer learning and continual learning.
In this session, we explored setting up Playwright, an end-to-end testing tool for simulating browser interactions and running TestBox tests. Participants learned to configure Playwright for applications, simulate user interactions to stress-test forms, and handle scenarios like taking screenshots, recording sessions, capturing Chrome dev tools traces, testing login failures, and managing broken JavaScript. The session also covered using Playwright with non-ColdBox sites, providing practical insights into enhancing testing capabilities.
Break data silos with real-time connectivity using Confluent Cloud Connectorsconfluent
Connectors integrate Apache Kafka® with external data systems, enabling you to move away from a brittle spaghetti architecture to one that is more streamlined, secure, and future-proof. However, if your team still spends multiple dev cycles building and managing connectors using just open source Kafka Connect, it’s time to consider a faster and cost-effective alternative.
In this session, we discussed the critical need for comprehensive backups across all aspects of our industry—from code and databases to webservers, file servers, and network configurations. Emphasizing the importance of proactive measures, attendees were urged to ensure their backup systems were tested through restoration processes. The session underscored the risk of discovering backup issues only during crises, highlighting the necessity of verifying backup integrity through restoration tests.
In this session, we explored how the cbfs module empowers developers to abstract and manage file systems seamlessly across their lifecycle. From local development to S3 deployment and customized media providers requiring authentication, cbfs offers flexible solutions. We discussed how cbfs simplifies file handling with enhanced workflow efficiency compared to native methods, along with practical tips to accelerate complex file operations in your projects.
CommandBox was highlighted as a powerful web hosting solution, perfect for developers and businesses alike. Featuring a built-in server and command-line interface, CommandBox simplified web application management. Developers could deploy multiple application instances simultaneously, optimizing development workflows. CommandBox's efficient deployment processes ensured reliable web hosting, seamlessly integrating into existing workflows for scalability and feature enhancements.
Non-Functional Testing Guide_ Exploring Its Types, Importance and Tools.pdfkalichargn70th171
Are you looking for ways to ensure your software development projects are successful? Non-functional testing is an essential part of the process, helping to guarantee that applications and systems meet the necessary non-functional requirements such as availability, scalability, security, and usability.
What is OCR Technology and How to Extract Text from Any Image for FreeTwisterTools
Discover the fascinating world of Optical Character Recognition (OCR) technology with our comprehensive presentation. Learn how OCR converts various types of documents, such as scanned paper documents, PDFs, or images captured by a digital camera, into editable and searchable data. Dive into the history, modern applications, and future trends of OCR technology. Get step-by-step instructions on how to extract text from any image online for free using a simple tool, along with best practices for OCR image preparation. Ideal for professionals, students, and tech enthusiasts looking to harness the power of OCR.
Alluxio Webinar | 10x Faster Trino Queries on Your Data PlatformAlluxio, Inc.
Alluxio Webinar
June. 18, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Jianjian Xie (Staff Software Engineer, Alluxio)
As Trino users increasingly rely on cloud object storage for retrieving data, speed and cloud cost have become major challenges. The separation of compute and storage creates latency challenges when querying datasets; scanning data between storage and compute tiers becomes I/O bound. On the other hand, cloud API costs related to GET/LIST operations and cross-region data transfer add up quickly.
The newly introduced Trino file system cache by Alluxio aims to overcome the above challenges. In this session, Jianjian will dive into Trino data caching strategies, the latest test results, and discuss the multi-level caching architecture. This architecture makes Trino 10x faster for data lakes of any scale, from GB to EB.
What you will learn:
- Challenges relating to the speed and costs of running Trino in the cloud
- The new Trino file system cache feature overview, including the latest development status and test results
- A multi-level cache framework for maximized speed, including Trino file system cache and Alluxio distributed cache
- Real-world cases, including a large online payment firm and a top ridesharing company
- The future roadmap of Trino file system cache and Trino-Alluxio integration
Explore the latest in ColdBox Debugger v4.2.0, featuring the Hyper Collector for HTTP/S request tracking, Lucee SQL Collector for query profiling, and Heap Dump Support for memory leak debugging. Enhancements like the revamped Request Dock and improved SQL/JSON formatting streamline debugging for optimal ColdBox application performance and stability. Ideal for developers familiar with ColdBox, this session focuses on leveraging advanced debugging tools to enhance development efficiency.
Participants explored how visual and functional coherence strengthened brand identity and streamlined development in this session. They learned to maintain consistency across platforms and enhance user experiences using Design Systems. Ideal for brand designers, UI/UX designers, developers, and product managers who sought to optimize efficiency and ensure consistency across projects.
Discover BoxLang, the innovative JVM programming language developed by Ortus Solutions. Designed to harness the power of the Java Virtual Machine, BoxLang offers a modern approach to application development with robust performance and scalability. Join us as we explore the capabilities of BoxLang, its syntax, and how it enhances productivity in software development.
2. About Us
• Helping great companies become great software companies
• Building software solutions powered by disruptive enterprise software trends
-Machine learning and data science
-Cyber-security
-Enterprise IOT
-Powered by Cloud and Mobile
• Bringing innovation from startups and academic institutions to the enterprise
• Award winning agencies: Inc 500, American Business Awards, International Business Awards
3. About This Webinar
• Research that brings together big enterprise software trends,
exciting startups and academic research
• Best practices based on real world implementation experience
• No sales pitches
8. Modern Machine Learning
• Advances in storage, compute and data science research are
making machine learning as part of mainstream technology
platforms
• Big data movement
• Machine learning platforms are optimized with developer-friendly
interfaces
• Platform as a service providers have drastically lowered the
entry point for machine learning applications
• R and Python are leading the charge
10. Cloud Machine Learning Platforms: Benefits
• Service abstraction layer over the machine learning infrastructure
• Rich visual modeling tools
• Rich monitoring and tracking interfaces
• Combine multiple platforms: R, Python, etc
• Enable programmatic access to ML models
11. Cloud machine Learning Platforms:: Challenges
• Integration with on-premise data stores
• Extensibility
• Security and privacy
12. On-Premise machine Learning Platforms: Benefits
• Control
• Security
• Integration with on-premise data stores
• Integrated with R and Python machine learning frameworks
13. On-Premise machine Learning Platforms: Challenges
• Code-based modeling interfaces
• Scalability
• Tightly coupled with Hadoop distributions
• Monitoring and management
• Data quality and curation
17. Azure Machine Learning
• Native machine learning capabilities as part of the Azure cloud
• Elastic infrastructure that scale based on the model requirements
• Support over 30 supervised and unsupervised machine learning
algorithms
• Integration with R and Python machine learning libraries
• Expose machine learning models via programmable interfaces
• Integrated with the Cortana Analytics suite
• Integrated with PowerBI
18. • Supports both supervised and
unsupervised models
• Integrated with Azure HDInsight
• Large library of models and sample
gallery
• Support for R and Python code
Visual Model Creation
19. • Visual dashboard to track the
execution of ML models
• Track execution of different steps
within a ML model
• Integrated monitoring experience
with other Azure services
Rich Monitoring and Management Interface
20. • Expose machine learning models as
Web Services APIs
• Integrate ML Models with Azure API
Gateway
• Retrain and extend models via ML
APIs
Programmatic Access to ML Models
22. AWS Machine Learning
• Native machine learning service in AWS
• Provide data exploration and visualization tools
• Supports supervised and unsupervised algorithms
• Integrated data transformation models
• APIs for dynamically creating machine learning models
23. • Programmatic creation of machine
learning models
• Large number of algorithms and recipes
• Data transformation models included in
the language
Sophisticated ML Model Authoring
24. • Sophisticated monitoring for
evaluating ML models
• Integrated with AWS Cloud Watch
• KPIs that evaluate the efficiency of
ML models
Monitoring ML Model Execution
25. • Optimized DSL for data
transformation
• Recipes that abstract common
transformations
• Reuse transformation recipes
across ML models
Embedded Data Transformation
26. • Sophisticated monitoring for
evaluating ML models
• Integrated with AWS Cloud Watch
• KPIs that evaluate the efficiency of
ML models
Monitoring ML Model Execution
28. Databricks Machine Learning
• Scaling Spark machine learning pipelines
• Integrated data visualization tools
• Sophisticated ML monitoring tools
• Combine Python, Scala and R in a single platform
29. • Implementing machine learning
models using Notebooks
• Publishing notebooks to a
centralized catalog
• Leverage Python, Scala or R to
implement machine learning models
Notebooks Based Authoring
30. • Integrate data visualization into
machine learning pipelines
• Reuse data visualization
notebooks across applications
• Evaluate the efficiency of
machine learning pipelines using
visualizations
Machine Learning Data Visualization
31. • Monitor the execution of machine
learning pipelines
• Run machine learning pipelines
manually
• Rapidly modify and deploy machine
learning pipelines
Monitoring and Management
33. • Personality Insights
• Tradeoff Analytics
• Relationship Extraction
• Concept Insights
• Speech to Text
• Text to Speech
• Visual Recognition
• Natural Language Classifier
• Language Identification
• Language Translation
• Question and Answer
• Concept Expansion
• Message Resonance
• AlchemyAPI Services
Large Variety of Cognitive Services
34. • Access services via REST APIs
• SDKs available for different
languages
• Integration with different
services in the BlueMix
platform
Rich Developer Interfaces
40. All of Open Source R plus:
• Big Data scalability
• High-performance analytics
• Development and deployment tools
• Data source connectivity
• Application integration framework
• Multi-platform architecture
• Support, Training and Services
Revolution Analytics (Microsoft)
41. DistributedR
ScaleR
ConnectR
DeployR
In the Cloud Amazon AWS
Workstations & Servers Windows
Red Hat and SUSE Linux
Clustered Systems IBM Platform LSF
Microsoft HPC
EDW IBM Netezza
Teradata
Hadoop Hortonworks
Cloudera
Write Once, Deploy Anywhere
42. DeployR does not provide any application UI.
3 integration modes embed real-time R results
into existing interfaces
Web app, mobile app, desktop app, BI tool,
Excel, …
RBroker Framework :
Simple, high-performance API for Java, .NET
and Javascript apps Supports transactional,
on-demand analytics on a stateless R session
Client Libraries:
Flexible control of R services from Java,
.NET and Javascript apps Also supports
stateful R integrations (e.g. complex GUIs)
DeployR Web Services API:
Integrate R using almost any client languages
Integrate R Scripts Into Third Party Applications
44. • It is built on Apache Spark, a fast and
general engine for large-scale data
processing
• Run programs up to 100x faster than Hadoop
MapReduce in memory, or 10x faster on disk.
• Write applications quickly in Java, Scala,
or Python.
Spark Mlib
45. • Integrated with Spark SQL for data
queries and transformations
• Integrated with Spark GraphX for
data visualizations
• Integrated with Spark Streaming for
real time data processing
Beyond Machine Learning
46. • Run R and machine learning models
using the same infrastructure
• Leverage R scripts from Spark Mlib
models
• Scale R models as part of a Spark
cluster
• Execute R models programmatically
using Java APIs
Spark Mlib + SparkR
48. • Makes Python machine learning
enterprise – ready
• Graphlab Create
• Dato Distributed
• Dato Predictive Services
Dato
51. Principles:
• Get started fast
• Rapidly iterate
• Combine for new apps
import graphlab as gl
data = gl.SFrame.read_csv('my_data.csv')
model = gl.recommender.create(data,
user_id='user',
item_id='moviez
target='rating')
recommendations = model.recommend(k=5)
Recommender Image search Sentiment Analysis
Data Matching Auto Tagging Churn Predictor
Click Prediction Product Sentiment Object Detector
Search Ranking Summarization …
Sophisticated ML made easy - Toolkits
53. • Powers deep learning capabilities on dozens
of Google’s products
• Interfaces for modeling machine and deep
learning algorithms
• Platform for executing those algorithms
• Scales from mobile devices to a cluster with
thousands of nodes
• Has become one of the most popular projects
in Guthub in less than a week
Google’s Tensor Flow
54. • Based on the principle of a dataflow
graph
• Nodes can perform data operations
but also send or receive data
• Python and C++ libraries. NodeJS, Go
and others in the pipeline
Tensorflow Programming Model
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run(tf.initialize_all_variables())
for i in range(20000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch[0], y_: batch[1], keep_prob: 1.0})
print "step %d, training accuracy %g"%(i, train_accuracy)
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print "test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
55. • Scales from a single device to a large
cluster of nodes
• Tensorflow uses a placement algorithm
based on heuristics to place tasks on
the different nodes in a graph
• The execution engine assigns tasks for
fault tolerance
• Linear scalability model
Tensor Flow Implementation
56. • TensorFlow includes an engine that
enables the visual representation of
the execution graph
• Visualizations include summary
statistics of the different states of
the model
• The visualization engine is included
in the current open source release
Tensor Flow Graph Visualization
59. •Enable foundational building blocks
-Data quality
-Data discovery
-Functional and integration testing
•Predictions are tempting but classification and clustering are
easier
•Run multiple models at once
•Enable programmatic interfaces to interact with ML models
•Start small, deliver quickly, iterate…
Machine Learning in the Enterprise
60. •Machine learning is becoming one of the most important elements of
modern enterprise solutions
•Innovation in machine learning is happening in both the on-premise
and cloud space
•Cloud machine learning innovators include: Azure ML, AWS ML,
Databricks and IBM Watson
•On-premise machine learning innovators include: Spark Mlib,
Microsoft’s Revolution R, Dato, TensorFlow
•Enterprise machine learning solutions should include elements such
as data quality, data governance, etc
•Start small and use real use cases
Summary
63. • Extensions to SciPy (Scientific Python) are called SciKits. SciKit-Learn
provides machine learning algorithms.
• Algorithms for supervised & unsupervised learning
• Built on SciPy and Numpy
• Standard Python API interface
• Sits on top of c libraries, LAPACK, LibSVM, and Cython
• Open Source: BSD License (part of Linux)
• Probably the best general ML framework out there.
Scikit-Learn
64. Load &
Transform Data
Raw Data
Feature
Extraction
Build Model
Feature
Evaluation
Very Simple Prediction Model
Evaluate
Model
65. Assess how model will generalize to independent data set (e.g.
data not in the training set).
1. Divide data into training and test splits
2. Fit model on training, predict on test
3. Determine accuracy, precision and recall
4. Repeat k times with different splits then average as F1
Predicted Class A Predicted Class B
Actual A True A False B #A
Actual B False A True B #B
#P(A) #P(B) total
Simple Programming Model-Cross Validation (classification)
66. How to evaluate clusters? Visualization (but only in 2D)
Data Visualization
68. • Developer friendly machine learning platform
• Completely open source
• Based on Apache Spark
PredictionIO
69. • PredictionIO platform
A machine learning stack for building, evaluating
and deploying engines with machine learning
algorithms.
• Event Server
An open source machine learning analytics layer for
unifying events from multiple platforms
• Template Gallery
engine templates for different type of machine
learning applications
A Simple Architecture
70. • Execute models asynchronous via event
interface
• Query data programmatically via REST
interface
• Various SDKs provided as part of the platform
Model Execution
71. • Visual model for model creation
• Integrated with a template gallery
• Ability to test and valite engines
Rich Model Creation Interface