Start bringing your data and ml lifecycles to the next level with an end-to-end view.
ML(Ops) Lifecycle Cross-functional Teams Data, Model & Code Pipelines Continuous Delivery Site Reliability Engineering (SRE)Next Generation Data & AI Journey powered by MLOps
Your path to becoming data- and ML-driven, including successful product and service operationalization.
Realizing the full potential of data, machine learning & artificial intelligence (AI) is challenging.
Let's go on a journey and learn about reliable and continuous machine learning together!
The following journey addresses engineers, product owners, decision-makers, and anyone who wants an overview to learn more about the best practices for implementing end-to-end data pipelines and machine learning development and operations (MLOps) in their organization. Throughout the journey, you will learn about the various roles and stages of the data and ML lifecycle, including data engineering, ml engineering, data, model and code pipelining, testing and quality assurance (QA), deployment, reliability engineering, as well as monitoring and observability. You will also learn about the "Digital Highway for ML Systems," our blueprint for continuous delivery and sustainable value of data and AI products and services.
Intro
Reliability, Roles & Collaboration for Data & ML Lifecycles
Webinar: 09 March 2023
Introduction to Reliability, Roles & Collaboration for Data & ML(Ops) Lifecycles
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Module 1
Data Reliability Engineering & Unified Analytics
Leverage reliability engineering best practices for data pipelines and successful DataOps, and think holistically about unified data analytics platforms.
Data Engineering Data Pipelines Data Platforms Data Quality Assurance DataOpsWebinar: 30 March 2023
Data Reliability Engineering & Unified Platforms
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Module 2
Effective Data Science & Machine Learning Pipelines
How to go from data science and model development to ML engineering, and making sure that models are easy to understand, deploy, and reproducible.
Data Science Model Pipelines ML Engineering Continuous Integration (CI) Continuous Deployment (CD)
Module 3
Testing & QA for Machine Learning
Consider testing and quality assurance (QA) best practices for all components of ML systems (data, model, and code), including both functional and non-functional requirements.
Application Pipelines Model Testing Software Testing Performance Testing Quality Assurance
Module 4
System Design & Continuous Delivery for Machine Learning
Learn about the different components (not only the model) and how to design ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.
System Design Deployment Strategies Model Serving Architecture & Infrastructure Continuous Delivery
Module 5
Reliability Engineering for Data & ML
Apply a Site Reliability Engineering (SRE) mindset to data products and machine learning, including best practices on how to run and establish ML reliably, effectively, and accountably within your organization.
To be released in May/June 2023
Reliability Engineering Deployment Strategies Stability SLAs, SLIs & SLOs SLO Assurance
Module 6
Monitoring & Observability for Data & ML
Understand observability and why it matters, including the unique issues for observability imposed by data and model pipelines. And find out how to start addressing these challenges.
Data Quality Assurance Error Rates & Response Times User Experience Model & System Performance Monitoring & Observability
Module 7
The Digital Highway for Machine Learning Systems
Discover the blueprint of a digital highway for machine learning systems, which incorporates all aspects above and visualizes an end-to-end approach for reliable and continuous machine learning delivery and operations.
ML(Ops) Lifecycle Quality Gates & Automation Incident & Change Management Data, Model & Code Pipelines Operational AI SystemsWebinar: 06 July 2023
The Digital Highway for End-to-End Machine Learning & Effective MLOps
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