Kubeflow at Spotify (For the Kubeflow Summit)
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A lightning talk discussing some important challenges facing ML engineers and how the introduction of Kubeflow Pipelines will help. Full slides w/ speaker notes here: https://docs.google.com/presentation/d/12dwhS_x4568G6XQjI9SEUacD-n4hFQczBcRBLdbHNEM/edit
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Kubeflow is an open-source project that makes deploying machine learning workflows on Kubernetes simple and scalable. It provides components for machine learning tasks like notebooks, model training, serving, and pipelines. Kubeflow started as a Google side project but is now used by many companies like Spotify, Cisco, and Itaú for machine learning operations. It allows running workflows defined in notebooks or pipelines as Kubernetes jobs and serves models for production.
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The slides were delivered by James Betteley, Head of Education at the DevOpsGuys in a one-hour webinar. The full recording is available here - https://youtu.be/4gC3WpbetKs?t=2s
James has spent the last few years neck-deep in the world of DevOps transformation, helping a wide range of organizations optimize the way they collaborate to deliver better software, faster. James was joined by Elizabeth Ayer, Portfolio Manager, from Redgate Software. Elizabeth looks after a range of Redgate products that help teams extend their DevOps practices to SQL Server databases.
For more information visit www.devopsguys.com and www.red-gate.com
Getting started with kubernetes
Join us to learn how to deploy your first containerized application on the most popular orchestration engine. You will understand the basic concepts of Kubernetes along with the terminology and the deployment architecture. We will show you everything from building a Docker image to going live with your application. Each attendee gets $300 credit to start using Google Container Engine!
Managing the Complete Machine Learning Lifecycle with MLflow
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.
To solve for these challenges, Databricks unveiled last year MLflow, an open source project that aims at simplifying the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
In the past year, the MLflow community has grown quickly: over 120 contributors from over 40 companies have contributed code to the project, and over 200 companies are using MLflow.
In this tutorial, we will show you how using MLflow can help you:
Keep track of experiments runs and results across frameworks.
Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker.
We will demo the building blocks of MLflow as well as the most recent additions since the 1.0 release.
What you will learn:
Understand the three main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
How to use MLflow Tracking to record and query experiments: code, data, config, and results.
How to use MLflow Projects packaging format to reproduce runs on any platform.
How to use MLflow Models general format to send models to diverse deployment tools.
Prerequisites:
A fully-charged laptop (8-16GB memory) with Chrome or Firefox
Python 3 and pip pre-installed
Pre-Register for a Databricks Standard Trial
Basic knowledge of Python programming language
Basic understanding of Machine Learning Concepts
Serverless Kafka on AWS as Part of a Cloud-native Data Lake Architecture
AWS Data Lake / Lake House + Confluent Cloud for Serverless Apache Kafka. Learn about use cases, architectures, and features.
Data must be continuously collected, processed, and reactively used in applications across the entire enterprise - some in real time, some in batch mode. In other words: As an enterprise becomes increasingly software-defined, it needs a data platform designed primarily for "data in motion" rather than "data at rest."
Apache Kafka is now mainstream when it comes to data in motion! The Kafka API has become the de facto standard for event-driven architectures and event streaming. Unfortunately, the cost of running it yourself is very often too expensive when you add factors like scaling, administration, support, security, creating connectors...and everything else that goes with it. Resources in enterprises are scarce: this applies to both the best team members and the budget.
The cloud - as we all know - offers the perfect solution to such challenges.
Most likely, fully-managed cloud services such as AWS S3, DynamoDB or Redshift are already in use. Now it is time to implement "fully-managed" for Kafka as well - with Confluent Cloud on AWS.
Building a central integration layer that doesn't care where or how much data is coming from.
Implementing scalable data stream processing to gain real-time insights
Leveraging fully managed connectors (like S3, Redshift, Kinesis, MongoDB Atlas & more) to quickly access data
Confluent Cloud in action? Let's show how ao.com made it happen!
Translated with www.DeepL.com/Translator (free version)
Introduction to Kibana
The document discusses various components of the ELK stack including Elasticsearch, Logstash, Kibana, and how they work together. It provides descriptions of each component, what they are used for, and key features of Kibana such as its user interface, visualization capabilities, and why it is used.
What's hot (20)
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
DevOps, Common use cases, Architectures, Best Practices
DevOps, Common use cases, Architectures, Best Practices
End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
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Continuous Delivery of Deep Transformer-Based NLP Models Using MLflow and AWS...
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Similar to Kubeflow at Spotify (For the Kubeflow Summit)
Elyra - a set of AI-centric extensions to JupyterLab Notebooks.
In this session Luciano will explore the different projects that compose the Jupyter ecosystem; including Jupyter Notebooks, JupyterLab, JupyterHub and Jupyter Enterprise Gateway. Jupyter Notebooks are the current open standard for data science and AI model development, and IBM is dedicated to contributing to their success and adoption. Continuing the trend of building out the Jupyter ecosystem, Luciano will introduce Elyra. It's a project built to extend JupyterLab with AI-centric capabilities. He'll showcase the extensions that allow you to build Notebook Pipelines, execute notebooks as batch jobs, navigate and execute Python scripts, and tie neatly into Notebook versioning.
Shortening the Feedback Loop: How Spotify’s Big Data Ecosystem has evolved to...
https://www.bigdataspain.org/2016/program/thu-shortening-feedback-loop-how-spotify-big-data-ecosystem-has-evolved-produce-real-time-insights.html
https://www.youtube.com/watch?v=qNuy3JZsFPM&index=18&list=PL6O3g23-p8Tr5eqnIIPdBD_8eE5JBDBik&t=28s
Find out what's new at Puppet - products, programs, and more!
This document provides an overview and agenda for an event on automation for the modern enterprise from Puppet. It discusses Puppet's products like Puppet Discovery, Puppet Enterprise, and Puppet Pipelines which help customers manage infrastructure at scale across development and operations teams. It also mentions new partnerships, integrations, and opportunities for partners through training and increased payouts for registering deals.
Application Migration: How to Start, Scale and Succeed
Undergoing the application migration journey can be cumbersome and challenging, especially when you have a complex application portfolio that consists of both legacy and newer apps on outdated systems. You are hindered by managing and operating manual processes to address security concerns, regulatory change and policy compliance.
You know embarking on the cloud journey is inevitable and deciding where to start is overwhelming. Let us show you how.
Join Matt Russell to hear how Pivotal helps large organizations plan and execute their application transformation initiatives by using a set of proven techniques and approaches that help you get started quickly and scale continuously.
We use simple tools and start small to redefine current systems, and achieve cloud-native speed and resiliency. Let us show you how Pivotal can help you navigate your journey while instilling confidence along the way.
Presenter : Matt Russell, Senior Director, Application Transformation at Pivotal
Dev Ops for systems of record - Talk at Agile Australia 2015
Mirco Hering is a DevOps and Agile lead with over 10 years of experience improving IT delivery through automation, methodology, and innovation. The document discusses applying DevOps principles to systems of record like packaged software, non-custom code, and older custom code. It outlines challenges in automating builds, deployments, testing and integrating these systems. It provides examples of automating merges and traceability for a Siebel system to help systems of record adopt DevOps practices and continuous delivery.
ESEconf2011 - Guckenheimer Sam: "Agile in the Very Large"
This document summarizes experiences from Microsoft's Developer Division in moving to more agile practices for their very large Visual Studio product. Some key points:
1) They shifted from 3-4 year release cycles to focusing on continuous delivery of high quality customer previews every 4 weeks, with the goal of getting work fully tested and "done" instead of deferring bugs.
2) Practices included establishing a product backlog prioritized by customer value, using feature teams to deliver one feature per iteration, and integrating features regularly into a main branch to catch issues early.
3) Metrics showed a 10x reduction in deferred debt/bugs between VS 2005 and 2008, with 2x faster schedules and much higher
Shortening the feedback loop
How Spotify's big data ecosystem has evolved through Google Cloud Platform building blocks to leverage actionable insights
Continuous Delivery in the Enterprise
Continuous delivery in larger shops can run into people and technological roadblocks related to complex systems and organizational structures. This presentation looks at some of those challenges and how to overcome them.
State of Puppet 2013 - Puppet Camp DC
This document provides an overview of Puppet and Puppet Enterprise. It summarizes the key components and projects that make up Puppet like Puppet, Facter, Hiera, MCollective and PuppetDB. It describes the capabilities of Puppet Enterprise like configuration management, orchestration, discovery, provisioning and reporting. The document also provides community growth metrics and information on training offered by Puppet Labs.
How Azure DevOps can boost your organization's productivity
Azure DevOps can boost productivity through collaboration and automation. DevOps aims to continuously deliver value to users through practices like continuous integration, delivery, and deployment. Microsoft tools like Azure Boards, Pipelines, and Repos support the DevOps process. Azure Pipelines automates building, testing, and deploying code. Branching workflows and pull requests enable collaboration. Automation reduces errors and speeds up the release process. DevOps has helped organizations like Fidelity and Amica reduce costs and deployment times.
How to improve Developer Documentations ?
The document proposes solutions to improve the Teams Dev Docs by addressing developer pain points. It identifies key pain points such as difficulty finding relevant content and understanding errors. Possible solutions include a chatbot that can quickly answer questions and documentation that allows coding in native languages. User research is cited to validate the identified pain points. A phased go-to-market strategy is proposed starting with targeted launches of the chatbot and native language support to capture early adopters. The solutions aim to reduce time spent on documentation and increase new developer adoption.
SEP_Poster
This document summarizes a forum application called FoBot that was built using the MVC architecture with Apache Tiles and Bootstrap. Various testing tools like JUnit, Selenium, and code quality tools were used to ensure high product and code quality. The goal of FoBot is to provide an easy to administer forum for participants of a robotics competition to interact.
Dev Dives: Automate in full power with the latest Generative AI features in U...
Explore a world of possibilities as we harness the capabilities of Gen AI within UiPath Studio to translate AI smarts into real-world automation solutions at speed.
👉Get the chance to learn how to:
- Generate effortless workflows with AI in Studio Web.
- Enhance your workflows with capabilities like generating expressions, streamlining your automation development process.
- Dive into the Integration Service to create custom connectors.
- Master the art of building custom connectors and seamlessly publish and consume them in Studio Web.
Automation is evolving, and it's time for you to lead the way with Gen AI in UiPath.
🗣️ Speakers:
Alexandru Roman, Principal Product Manager, UiPath
Guy Van Wert, Principal Product Manager, UiPath
🔽 Try Connector Builder > https://bit.ly/47FYaL4
🔥 Explore UiPath Autopilot™. Sign up for the UiPath Insider program > https://bit.ly/40PBIg1
Register for our upcoming Dev Dives December session: Take your coding productivity to new heights with UiPath Autopilot™
👉 EMEA&APJ: https://bit.ly/Dev_Dives_EMEA_12
👉 AMER: https://bit.ly/Dev_Dives_AMER_12
This session was streamed live on November 21, 2023. Check out all our upcoming Dev Dives 2023 sessions at 👉 http://bit.ly/Dev-Dives_2023
Building State-of-the-art Natural Language Processing Projects with Free Soft...
Building Complete State-of-the-art Natural Language Processing Projects with Free Software covers free AI-assisted text annotation tools & no-code model building, a Python library with one-line access to 10,000+ pre-trained models in 250+ languages, and an NLP Server to server NLP models & pipelines as REST API's.
Innovate Better Through Machine data Analytics
This talk was presented at IP Expo Manchester in May, 2016. the themes discussed are:
- how does machine data relate to devops?
- how can tracking this data lead to better outcomes?
- what types of data are important to track?
30 days or less: New Features to Production
Karthik Gaekwad is a member of the cloud team at National Instruments who owns the Canopy user management and licensing platform. He discusses National Instrument's approach to cloud development which includes short monthly iterations to incrementally develop and deploy new features. Key aspects of their approach include modeling the end-to-end system, designing features to be reusable across platforms, extensive testing and monitoring, and getting early user feedback through demos.
DevOps and Build Automation
DevOps aims to bring development and operations teams closer together through automation, shared tools and processes. Automating builds improves consistency, reduces errors and improves productivity. Common issues with builds include them being too long, handling a large volume, or being too complex. Solutions include improving build speed, addressing long/complex builds through techniques like distributed builds, and using build acceleration tools. Automation is a key part of DevOps and enables continuous integration, testing and deployment.
Adrian marinica continuous integration in the visual studio world
This document discusses continuous integration practices in the Visual Studio world. It defines continuous integration as integrating work frequently, usually daily, with automated testing to quickly detect errors. Successful continuous integration requires maintaining a single source repository, automating builds and testing, committing to the mainline daily, and automating deployment. Examples are given of companies that continuously integrate and deploy many times a day. Challenges include the effort required to set up systems to enable continuous integration and ensure sufficient test coverage. Release management with Visual Studio and Team Foundation Server is discussed as a way to facilitate deploying to multiple environments.
NYIT DSC/ Spring 2021 - Introduction to DevOps (CI/CD)
NYIT DSC Spring 2021 workshop: Introduction to DevOps - Part I Fundamental with CI/ CD.
Presenter: Hui (Henry) Chen
Increase the Velocity of Your Software Releases Using GitHub and DeployHub
Increase the velocity of your software releases by using continuous deployment driven by continuous delivery pipeline. After all, the goal of agile is to get code updates into the hands of your users fast and on a high frequency basis. This means installing all the way to production, not just staged for productio.
This webinar will show you an approach to achieving full continuous deployment using GitHub and DeployHub. You will learn how to declare your Application Package from your GitHub repository, manage approvals and deliver updates to environments across the CD pipeline from development through production.
GitHub and DeployHub work together to provide a complete DevOps process that results in a repeatable, consistent software releases process with a full continuous feedback loop.
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Elyra - a set of AI-centric extensions to JupyterLab Notebooks.
Elyra - a set of AI-centric extensions to JupyterLab Notebooks.
Shortening the Feedback Loop: How Spotify’s Big Data Ecosystem has evolved to...
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Find out what's new at Puppet - products, programs, and more!
Find out what's new at Puppet - products, programs, and more!
Application Migration: How to Start, Scale and Succeed
Application Migration: How to Start, Scale and Succeed
Dev Ops for systems of record - Talk at Agile Australia 2015
Dev Ops for systems of record - Talk at Agile Australia 2015
ESEconf2011 - Guckenheimer Sam: "Agile in the Very Large"
ESEconf2011 - Guckenheimer Sam: "Agile in the Very Large"
How Azure DevOps can boost your organization's productivity
How Azure DevOps can boost your organization's productivity
Dev Dives: Automate in full power with the latest Generative AI features in U...
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Building State-of-the-art Natural Language Processing Projects with Free Soft...
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Adrian marinica continuous integration in the visual studio world
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NYIT DSC/ Spring 2021 - Introduction to DevOps (CI/CD)
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- 1. Kubeflow at Spotify How Kubeflow Pipelines fits into our Machine Learning ecosystem
- 2. Josh Baer ML Platform Product Lead Twitter: @j6aer
- 3. Music Streaming Service Launched in 2008 230M Active Users 50M Tracks 79 Countries
- 5. Machine Learning Journey - Overview
- 6. Most teams spend 1-3 “sprints” getting an initial prototype out How many product teams will wait this long to get initial learnings? How long does it take to build an ML prototype?
- 7. Over 30% of ML practitioners spend more than a quarter turning an idea into production software How long does it take to go from prototype -> production-grade solution?
- 8. 01 02 03 04Measurement, Experimentation and Tweaking ML Productionization Problem Definition Tweak Evaluat e Train Develop Model Prototypin g Machine Learning Journey - Overview
- 9. Problem Definition Prototype Productionize Measure 4w 2w 1w 2w 1w 2w 2w Many iterations per phase
- 10. Problem Definition Prototype Productionize Measure 4w 2w 1w 2w 1w 2w 2w 14 weeks to go from a defined problem to a production solution!
- 11. Difficult to Collaborate Keeping track of projects, artifacts and lineage was difficult No common way of building workflows Teams using N different frameworks in different ways. No shared learnings. Slow feedback loops Data analysis was separate from model training and model analysis. Each step is custom Other Challenges
- 12. ! Started discussing it with Google in early 2018 ! Aligned our infra tooling with their direction ! Product launched in late 2018 ! Promising early results! Kubeflow Pipelines
- 13. ! Evaluated in mid 2018 ! Decided to replace our scala-based ML tooling with TFX Tensorflow Extended
- 14. ! Launched a team to make Kubeflow Pipelines work for Spotify ! Thin internal layer to help development speed and integrate with Spotify ecosystem Kubeflow + TFX at Spotify
- 15. Test Cluster Internal development cluster to test upgrades, run integration tests Development Cluster For running ad-hoc jobs, developing new workflows Production Cluster For regularly scheduled workloads Higher availability SLA “Spotify” Kubeflow Setup
- 16. Caching Quicker resumption of failed tasks Other Spotify Kubeflow Features Central Metadata Keep track of what’s being built and run Spotify-wide Command Line Tooling Allows for scheduling and execution of jobs via luigi (Spotify orchestration Shared-VPC Integration Connect with other Spotify services Common TFX Components Easily run tfx-based pipelines
- 18. Problem Definition Prototype Productionize Measure 4w 1w 2d 1d 1d 2d 1d Shorter iteration cycles => faster time to production => better ML in our products Kubeflow Pipelines Machine Learning Journey - Updated
- 19. Mention Hack week last week: ● Nearly 1000 runs ● (Maybe add a quote) Recent Progress ! During hack week, over 1000 runs of pipeline experiments ! Developers are loving the integration of data validation, training and model analysis
- 20. Augus t 2019 “Spotify” Kubeflow Pipeline Platform launched in alpha. Jan 2020 Launch beta - open it up to the entire Spotify community Aug 2018 Kubeflow Pipeline Launches Jan 2019 First teams trying out Kubeflow. Start focusing infra efforts We’re here Our Kubeflow Timeline
- 21. Our Vision for Kubeflow Pipelines D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, M. Young, and J.-F. Crespo. Hidden technical debt in machine learning systems. In Neural Information Processing Systems (NIPS). 2015.
- 22. KubeflowComponents Our Vision for Kubeflow Pipelines
- 23. Kubeflow Our Vision for Kubeflow Pipelines
- 24. For building this community...
- 25. Check our Keshi and Ryan’s Talk at Kubecon: “Building and Managing a Centralized Kubeflow Platform at Spotify” Want to hear more?