Video: https://youtu.be/C_u4_l84ED8
Karl Isenberg reviews the history of distributed computing, clarifies terminology for layers in the container stack, and does a head to head comparison of several tools in the space, including Kubernetes, Marathon, and Docker Swarm. Learn which features and qualities are critical for container orchestration and how you can apply this knowledge when evaluating platforms.
Platform as a Service (PaaS) provides developers with tools and services to build, run, and manage applications over the internet without having to manage the underlying infrastructure. PaaS handles servers, operating systems, storage, networking, and other services so developers can focus on developing and deploying applications. Common PaaS services include application runtime, messaging, data services, and application management. PaaS allows for efficient, cost-effective application development by abstracting away the complexity of infrastructure management.
A Gentle Introduction To Docker And All Things ContainersJérôme Petazzoni
Docker is a runtime for Linux Containers. It enables "separation of concern" between devs and ops, and solves the "matrix from hell" of software deployment. This presentation explains it all! It also explains the role of the storage backend and compares the various backends available. It gives multiple recipes to build Docker images, including integration with configuration management software like Chef, Puppet, Salt, Ansible. If you already watched other Docker presentations, this is an actualized version (as of mid-November 2013) of the thing!
This presentation includes a comprehensive introduction to Apache Spark. From an explanation of its rapid ascent to performance and developer advantages over MapReduce. We also explore its built-in functionality for application types involving streaming, machine learning, and Extract, Transform and Load (ETL).
Sa introduction to big data pipelining with cassandra & spark west mins...Simon Ambridge
This document provides an overview and outline of a 1-hour introduction to building a big data pipeline using Docker, Cassandra, Spark, Spark-Notebook and Akka. The introduction is presented as a half-day workshop at Devoxx November 2015. It uses a data pipeline environment from Data Fellas and demonstrates how to use scalable distributed technologies like Docker, Spark, Spark-Notebook and Cassandra to build a reactive, repeatable big data pipeline. The key takeaway is understanding how to construct such a pipeline.
Reactive dashboard’s using apache sparkRahul Kumar
Apache Spark's Tutorial talk, In this talk i explained how to start working with Apache spark, feature of apache spark and how to compose data platform with spark. This talk also explains about reactive platform, tools and framework like Play, akka.
NOTE: This was converted to Powerpoint from Keynote. Slideshare does not play the embedded videos. You can download the powerpoint from slideshare and import it into keynote. The videos should work in the keynote.
Abstract:
In this presentation, we will describe the "Spark Kernel" which enables applications, such as end-user facing and interactive applications, to interface with Spark clusters. It provides a gateway to define and run Spark tasks and to collect results from a cluster without the friction associated with shipping jars and reading results from peripheral systems. Using the Spark Kernel as a proxy, applications can be hosted remotely from Spark.
Alpine academy apache spark series #1 introduction to cluster computing wit...Holden Karau
Alpine academy apache spark series #1 introduction to cluster computing with python & a wee bit of scala. This is the first in the series and is aimed at the intro level, the next one will cover MLLib & ML.
Real-Time Anomaly Detection with Spark MLlib, Akka and CassandraNatalino Busa
We present a solution for streaming anomaly detection, named “Coral”, based on Spark, Akka and Cassandra. In the system presented, we run Spark to run the data analytics pipeline for anomaly detection. By running Spark on the latest events and data, we make sure that the model is always up-to-date and that the amount of false positives is kept low, even under changing trends and conditions. Our machine learning pipeline uses Spark decision tree ensembles and k-means clustering. Once the model is trained by Spark, the model’s parameters are pushed to the Streaming Event Processing Layer, implemented in Akka. The Akka layer will then score 1000s of event per seconds according to the last model provided by Spark. Spark and Akka communicate which each other using Cassandra as a low-latency data store. By doing so, we make sure that every element of this solution is resilient and distributed. Spark performs micro-batches to keep the model up-to-date while Akka detects the new anomalies by using the latest Spark-generated data model. The project is currently hosted on Github. Have a look at : http://coral-streaming.github.io
Since 2014, Typesafe has been actively contributing to the Apache Spark project, and has become a certified development support partner of Databricks, the company started by the creators of Spark. Typesafe and Mesosphere have forged a partnership in which Typesafe is the official commercial support provider of Spark on Apache Mesos, along with Mesosphere’s Datacenter Operating Systems (DCOS).
In this webinar with Iulian Dragos, Spark team lead at Typesafe Inc., we reveal how Typesafe supports running Spark in various deployment modes, along with the improvements we made to Spark to help integrate backpressure signals into the underlying technologies, making it a better fit for Reactive Streams. He also show you the functionalities at work, and how to make it simple to deploy to Spark on Mesos with Typesafe.
We will introduce:
Various deployment modes for Spark: Standalone, Spark on Mesos, and Spark with Mesosphere DCOS
Overview of Mesos and how it relates to Mesosphere DCOS
Deeper look at how Spark runs on Mesos
How to manage coarse-grained and fine-grained scheduling modes on Mesos
What to know about a client vs. cluster deployment
A demo running Spark on Mesos
Streaming Analytics with Spark, Kafka, Cassandra and AkkaHelena Edelson
This document discusses a new approach to building scalable data processing systems using streaming analytics with Spark, Kafka, Cassandra, and Akka. It proposes moving away from architectures like Lambda and ETL that require duplicating data and logic. The new approach leverages Spark Streaming for a unified batch and stream processing runtime, Apache Kafka for scalable messaging, Apache Cassandra for distributed storage, and Akka for building fault tolerant distributed applications. This allows building real-time streaming applications that can join streaming and historical data with simplified architectures that remove the need for duplicating data extraction and loading.
This talk will address new architectures emerging for large scale streaming analytics. Some based on Spark, Mesos, Akka, Cassandra and Kafka (SMACK) and other newer streaming analytics platforms and frameworks using Apache Flink or GearPump. Popular architecture like Lambda separate layers of computation and delivery and require many technologies which have overlapping functionality. Some of this results in duplicated code, untyped processes, or high operational overhead, let alone the cost (e.g. ETL).
I will discuss the problem domain and what is needed in terms of strategies, architecture and application design and code to begin leveraging simpler data flows. We will cover how the particular set of technologies addresses common requirements and how collaboratively they work together to enrich and reinforce each other.
Reactive app using actor model & apache sparkRahul Kumar
Developing Application with Big Data is really challenging work, scaling, fault tolerance and responsiveness some are the biggest challenge. Realtime bigdata application that have self healing feature is a dream these days. Apache Spark is a fast in-memory data processing system that gives a good backend for realtime application.In this talk I will show how to use reactive platform, Actor model and Apache Spark stack to develop a system that have responsiveness, resiliency, fault tolerance and message driven feature.
Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, ScalaHelena Edelson
Scala Days, Amsterdam, 2015: Lambda Architecture - Batch and Streaming with Spark, Cassandra, Kafka, Akka and Scala; Fault Tolerance, Data Pipelines, Data Flows, Data Locality, Akka Actors, Spark, Spark Cassandra Connector, Big Data, Asynchronous data flows. Time series data, KillrWeather, Scalable Infrastructure, Partition For Scale, Replicate For Resiliency, Parallelism
Isolation, Data Locality, Location Transparency
Streaming Big Data with Spark, Kafka, Cassandra, Akka & Scala (from webinar)Helena Edelson
This document provides an overview of streaming big data with Spark, Kafka, Cassandra, Akka, and Scala. It discusses delivering meaning in near-real time at high velocity and an overview of Spark Streaming, Kafka and Akka. It also covers Cassandra and the Spark Cassandra Connector as well as integration in big data applications. The presentation is given by Helena Edelson, a Spark Cassandra Connector committer and Akka contributor who is a Scala and big data conference speaker working as a senior software engineer at DataStax.
Microservices, Containers, Docker and a Cloud-Native Architecture in the Midd...Kai Wähner
Microservices are the next step after SOA: Services implement a limited set of functions. Services are developed, deployed and scaled independently. Continuous Integration and Continuous Delivery automate deployments. This way you get shorter time to results and increased flexibility. Containers improve these even more offering a very lightweight and flexible deployment option.
In the middleware world, you use concepts and tools such as an Enterprise Service Bus (ESB), Complex Event Processing (CEP), Business Process Management (BPM) or API Gateways. Many people still think about complex, heavyweight central brokers here. However, Microservices and containers are relevant not just for custom self-developed applications, but they are also a key requirement to make the middleware world more flexible, agile and automated.
This session discusses the requirements, best practices and challenges for creating a good Microservices architecture in the middleware world. A live demo with the open source PaaS framework CloudFoundry shows how technologies and frameworks such as Java, SOAP / REST Web Services, Jenkins and Docker are used to create an agile software development lifecycle to realize “Middleware Microservices”. It also discusses other modern cloud-native alternatives such as Kubernetes, Docker, Mesos, Mesosphere or Amazon ECS / AWS.
Data Science lifecycle with Apache Zeppelin and Spark by Moonsoo LeeSpark Summit
This document discusses Apache Zeppelin, an open-source notebook for interactive data analytics. It provides an overview of Zeppelin's features, including interactive notebooks, multiple backends, interpreters, and a display system. The document also covers Zeppelin's adoption timeline, from its origins as a commercial product in 2012 to becoming an Apache Incubator project in 2014. Future projects involving Zeppelin like Helium and Z-Manager are also briefly described.
Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time PersonalizationPatrick Di Loreto
The gambling industry has arguably been one of the most comprehensively affected by the internet revolution, and if an organization such as William Hill hadn't adapted successfully it would have disappeared. We call this, “Going Reactive.”
The company's latest innovations are very cutting edge platforms for personalization, recommendation, and big data, which are based on Akka, Scala, Play Framework, Kafka, Cassandra, Spark, and Mesos.
Spark Streaming makes it easy to build scalable fault-tolerant streaming applications. In this webinar, developers will learn:
*How Spark Streaming works - a quick review.
*Features in Spark Streaming that help prevent potential data loss.
*Complementary tools in a streaming pipeline - Kafka and Akka.
*Design and tuning tips for Reactive Spark Streaming applications.
Lambda Architecture with Spark, Spark Streaming, Kafka, Cassandra, Akka and S...Helena Edelson
Regardless of the meaning we are searching for over our vast amounts of data, whether we are in science, finance, technology, energy, health care…, we all share the same problems that must be solved: How do we achieve that? What technologies best support the requirements? This talk is about how to leverage fast access to historical data with real time streaming data for predictive modeling for lambda architecture with Spark Streaming, Kafka, Cassandra, Akka and Scala. Efficient Stream Computation, Composable Data Pipelines, Data Locality, Cassandra data model and low latency, Kafka producers and HTTP endpoints as akka actors...
DOD 2016 - Jörg Schad - How Fast Data and Microservices Change the Datacenter.PROIDEA
The application landscape inside our datacenter is changing: there are a number new distributed data processing frameworks such as Kafka or Flink being released on a weekly basis and also the trend towards microservices and container. This has implications for the ways we are running our datacenter. With this growing need of computing power, distributed applications, and larger data centers also the need for a reliable and simple use cluster manager and programming abstraction grows. This talk explains how Apache Mesos and DC/OS allows combining Microservice management and Fast Data systems on a single platform.
DevOps vs. Site Reliability Engineering (SRE) in Age of KubernetesDevOps.com
There is a transformation brewing for DevOps in age of Kubernetes. The tools of the trade, configuration management solutions, have been superseded in agility and preference by development teams who want the declarative choreography of containerized applications. The new preference for mixing developer and operations is the site reliability engineering (SRE) model championed by Google. In this new structure, the need to automate doesn’t stop at the containerized application and DevOps professionals should seek to automate the Kubernetes service itself.
In this webinar, Chris Gaun, Product Marketing Manager at Mesosphere, will cover:
The transformation of DevOps to SRE
How Kubernetes and DC/OS were catalyst for this change
How DevOps professionals can get started with Kubernetes
WHO SHOULD ATTEND
Tech Professionals
Developer Managers
IT Managers
Note the material is technical and is not intended as sales and marketing training
There is a transformation brewing for DevOps in age of Kubernetes. The tools of the trade, configuration management solutions, have been superseded in agility and preference by development teams who want the declarative choreography of containerized applications. The new preference for mixing developer and operations is the site reliability engineering (SRE) model championed by Google. In this new structure, the need to automate doesn’t stop at the containerized application and DevOps professionals should seek to automate the Kubernetes service itself.
Cloud Native Night, January 2018, Munich: Workshop led by Jörg Schad (@joerg_schad, Technical Lead Community Projects at Mesosphere)
Join our Meetup: https://www.meetup.com/de-DE/cloud-native-muc
PLEASE NOTE: During this workshop, Jörg showed many demos and the audience could participate on their laptops. Unfortunately, we can't provide these demos. Nevertheless, Jörg's slides give a deep dive into the topic.
ABSTRACT: Kubernetes has been one of the topics in 2017 and will probably remain so in 2018. In this hands-on technical workshop you will learn how best to deploy, operate, and scale Kubernetes clusters from one to hundreds of nodes using DC/OS. You will learn how to integrate and run Kubernetes alongside traditional applications and fast data services of your choice (e.g. Apache Cassandra, Apache Kafka, Apache Spark, TensorFlow, and more) on any infrastructure.
This workshop best suits operators focussed on keeping their apps and services up and running in production and developers focussed on quickly delivering internal and customer facing apps into production.
You will learn how to:
- Introduction to Kubernetes and DC/OS (including the differences between both)
- Deploy Kubernetes on DC/OS in a secure, highly available, and fault-tolerant manner
- Solve operational challenges of running a large/multiple Kubernetes cluster
- One-click deploy big data stateful and stateless services alongside a Kubernetes cluster
[DO16] Mesosphere : Microservices meet Fast Data on Azure de:code 2017
The document discusses how microservices and fast data can be combined on Azure using the SMACK stack. It describes each component of the stack: Apache Kafka for ingesting data streams, Apache Spark for stream processing, Apache Cassandra for storage, and Akka for acting on the data. It outlines how Mesos provides a datacenter operating system to run these diverse workloads together efficiently using resource multiplexing across servers.
Learn about the challenges the come with deploying and operating Kubernetes at scale and how the Mesosphere DC/OS Kubernetes integration helps solve them.
During this presentation, Joerg Schad discusses:
1. Common challenges associated with getting a Kubernetes cluster up and running
2. The basics of running Kubernetes on Mesosphere DC/OS
3. How failure recovery works with the DC/OS-Kubernetes solution
Downtime is not an option - day 2 operations - Jörg SchadCodemotion
The document discusses container orchestration and microservices on Apache Mesos and DC/OS. It introduces concepts like microservices, containers, container orchestration and scheduling. It then summarizes key Mesos and DC/OS capabilities like running various workloads, multiplexing resources for higher utilization and running distributed applications. It also touches on day 2 operations for containerized workloads like monitoring, maintenance and troubleshooting.
OSDC 2018 | From batch to pipelines – why Apache Mesos and DC/OS are a soluti...NETWAYS
Apache Mesos is a distributed system for running other distributed systems, often described as a distributed kernel. It’s in use at massive scale at some of the worlds largest companies like Netflix, Uber and Yelp, abstracting entire data centres of hardware to allow for workloads to be distributed efficiently. DC/OS is an open source distribution of Mesos, which adds all the functionality to run Mesos in production across any substrate, both on-premise and in the cloud. In this talk, I’ll introduce both Mesos and DC/OS and talk about how they work under the hood, and what the benefits are of running these new kinds of systems for emerging cloud native workloads.
Operating Kubernetes at Scale (Australia Presentation)Mesosphere Inc.
Kubernetes is an amazing technology, but getting it up and running in your data center or VMs is challenging. In this technical webinar, you will learn how best to deploy, operate, and scale Kubernetes clusters from one to hundreds of nodes using DC/OS.
Jörg Schad and Adrian Smolski from Mesosphere show how to run Kubernetes on DC/OS, as well as how to integrate and run Kubernetes alongside traditional applications and fast data services of your choice (e.g. Apache Cassandra, Apache Kafka, Apache Spark, TensorFlow, and more) on any infrastructure.
You will learn how to:
1. Deploy Kubernetes in a secure, highly available, and fault-tolerant manner on DC/OS
2. Solve operational challenges of running a large/multiple Kubernetes cluster(s)
3. One-click deploy big data stateful and stateless services alongside a Kubernetes cluster
Jörg is a Technical Lead for Community Projects at Mesosphere in San Francisco. His speaking experience includes various Meetups, international conferences, and lecture halls.
Adrian Smolski is the local Field CTO based out of Sydney, Australia. His background is big data, data science and distributed systems.
Highly efficient container orchestration and continuous delivery with DC/OS a...Christian Bogeberg
Continuous delivery is all the rage these days, but without self-healing, highly available, and fault-tolerant infrastructure to deploy your applications to, it’s really only one piece of a much larger picture. Apache Mesos was born at UC Berkeley and grew into a robust, highly scalable cluster orchestrator while running thousands of nodes at Twitter. Support for Docker containers was added in 2013, and since then, it’s been adopted by companies like Netflix and Apple to run their critical infrastructure. Mesosphere has built the open source Datacenter Operating System (DC/OS) around Apache Mesos to provide all the supplementary tooling necessary to take Mesos to a production environment. Jenkins with DC/OS allows you to spin up build agents dynamically, an approach which has allowed companies like PayPal to cut the footprint of their build farms by hundreds of nodes, saving money on infrastructure by increasing utilization and reducing obstacles to providing teams with the resources they need when they need them.
Sunil Shah and Roger Ignazio introduce DC/OS and demonstrate how to integrate it with the stalwart continuous integration server Jenkins, allowing you to set up a continuous delivery pipeline that takes an application composed of microservices from code repository to Docker Hub to a staging or production server with seamless automation. Sunil and Roger walk attendees through setting up their own pipeline using Jenkins on a DC/OS cluster, from installation and configuration of Jenkins to setting up a build to actually deploying it to a live environment where it can serve traffic, and also cover the internals of practical microservice architecture, including component-level deployment, application-level persistence, and intraprocess communication via service discovery.
Kubernetes is an amazing technology, but getting it up and running in your data center or VMs is challenging. In this technical webinar, you will learn how best to deploy, operate, and scale Kubernetes clusters from one to hundreds of nodes using DC/OS.
Learn how to run Kubernetes on DC/OS, as well as how to integrate and run Kubernetes alongside traditional applications and fast data services of your choice (e.g. Apache Cassandra, Apache Kafka, Apache Spark, TensorFlow, and more) on any infrastructure.
You will learn how to:
1. Deploy Kubernetes in a secure, highly available, and fault-tolerant manner on DC/OS
2. Solve operational challenges of running a large/multiple Kubernetes cluster(s)
3. One-click deploy big data stateful and stateless services alongside a Kubernetes cluster
Jörg is a Technical Lead for Community Projects at Mesosphere in San Francisco. His speaking experience includes various Meetups, international conferences, and lecture halls.
Joel works on the Field Operations team at Mesosphere based in London. Joel has spent the majority of his career exploring and implementing distributed database systems.
EMC World 2016 - Introduction to Mesos and MesosphereDavid vonThenen
This document provides an introduction to Mesos and Mesosphere. It begins with presenters and an agenda. It then defines Mesos as a cluster manager that pools server resources and a scheduler that dispatches workloads. Mesos components include a master, Zookeeper, and agents. Mesos supports generic and framework applications. Mesosphere was founded to deliver Mesos for enterprises and provides a datacenter operating system with rich ecosystem. The document demonstrates how EMC Code integrates persistent external storage with Mesos and Mesosphere through projects like REX-Ray and mesos-module-dvdi. It concludes with an invitation to ask questions.
EMC World 2016 - code.16 Running Stateful Services on Cloud Native Platforms ...{code}
Many of today's PaaS systems are focused on stateless applications, scaling them from 1 to infinity and automatically rescheduling them when something goes wrong. But what about the data they create? How can we create scalable data persistence backends for our services to make sure our stored data is highly available? In this session we will demonstrate stateless applications running on PaaS systems, connecting to data persistence layers like relational and NoSQL databases, all running on Mesos and all stored on highly available distributed storage platforms.
EMC World 2016 - code.08 Introduction to Mesos and Mesosphere{code}
Mesos is a cluster manager unique for simplifying how you operate and scale complex applications. An important distribution is built by industry experts at Mesosphere, who are driving and extending the Mesos architecture. Learn how Mesos helps you build out a homogenous data center strategy and how Mesosphere can help you meet your Enterprise needs in a container platform.
This document discusses running distributed applications in a cloud native way using microservices and container orchestration. It describes how a monolithic "DropBox" application could be refactored into microservices and containerized. Key points include:
- Microservices break applications into modular services that communicate through well-defined interfaces. This makes applications more scalable, resilient, and efficient.
- Container orchestration with Mesos and DC/OS provides scheduling, resource management, and service management for microservices running in containers. It allows multiplexing of workloads across servers for better utilization.
- Minio can be used to provide an object storage microservice using containers. It demonstrates storing unstructured data like files in a
The document discusses Mesosphere's Datacenter Operating System (DCOS). It provides an overview of containers, Apache Mesos, Marathon, and how DCOS integrates these components to function as an operating system for datacenters. Key points include that DCOS treats resources across a cluster as a single shared pool, provides scheduling and orchestration of workloads, and enables easy deployment and management of applications.
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
Performance Budgets for the Real World by Tammy EvertsScyllaDB
Performance budgets have been around for more than ten years. Over those years, we’ve learned a lot about what works, what doesn’t, and what we need to improve. In this session, Tammy revisits old assumptions about performance budgets and offers some new best practices. Topics include:
• Understanding performance budgets vs. performance goals
• Aligning budgets with user experience
• Pros and cons of Core Web Vitals
• How to stay on top of your budgets to fight regressions
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.
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.
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.
Sustainability requires ingenuity and stewardship. Did you know Pigging Solutions pigging systems help you achieve your sustainable manufacturing goals AND provide rapid return on investment.
How? Our systems recover over 99% of product in transfer piping. Recovering trapped product from transfer lines that would otherwise become flush-waste, means you can increase batch yields and eliminate flush waste. From raw materials to finished product, if you can pump it, we can pig it.
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.
Blockchain technology is transforming industries and reshaping the way we conduct business, manage data, and secure transactions. Whether you're new to blockchain or looking to deepen your knowledge, our guidebook, "Blockchain for Dummies", is your ultimate resource.
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.
The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2023 and the first deals of 2024.
How to Avoid Learning the Linux-Kernel Memory ModelScyllaDB
The Linux-kernel memory model (LKMM) is a powerful tool for developing highly concurrent Linux-kernel code, but it also has a steep learning curve. Wouldn't it be great to get most of LKMM's benefits without the learning curve?
This talk will describe how to do exactly that by using the standard Linux-kernel APIs (locking, reference counting, RCU) along with a simple rules of thumb, thus gaining most of LKMM's power with less learning. And the full LKMM is always there when you need it!
Fluttercon 2024: Showing that you care about security - OpenSSF Scorecards fo...Chris Swan
Have you noticed the OpenSSF Scorecard badges on the official Dart and Flutter repos? It's Google's way of showing that they care about security. Practices such as pinning dependencies, branch protection, required reviews, continuous integration tests etc. are measured to provide a score and accompanying badge.
You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter.
The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.
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.
Are you interested in learning about creating an attractive website? Here it is! Take part in the challenge that will broaden your knowledge about creating cool websites! Don't miss this opportunity, only in "Redesign Challenge"!