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.

Reliability, Roles & Collaboration for Data & ML Lifecycles

Start bringing your data and ml lifecycles to the next level with an end-to-end view.

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ML(Ops) Lifecycle Cross-functional Teams Data, Model & Code Pipelines Continuous Delivery Site Reliability Engineering (SRE)

Introduction to Reliability, Roles & Collaboration for Data & ML(Ops) Lifecycles

Data Reliability Engineering & Unified Analytics

Leverage reliability engineering best practices for data pipelines and successful DataOps, and think holistically about unified data analytics platforms.

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Data Engineering Data Pipelines Data Platforms Data Quality Assurance DataOps

Data Reliability Engineering & Unified Platforms

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.

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Data Science Model Pipelines ML Engineering Continuous Integration (CI) Continuous Deployment (CD)

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.

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Application Pipelines Model Testing Software Testing Performance Testing Quality Assurance

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.

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System Design Deployment Strategies Model Serving Architecture & Infrastructure Continuous Delivery

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.

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Data Quality Assurance Error Rates & Response Times User Experience Model & System Performance Monitoring & Observability

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.

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ML(Ops) Lifecycle Quality Gates & Automation Incident & Change Management Data, Model & Code Pipelines Operational AI Systems

The Digital Highway for End-to-End Machine Learning & Effective MLOps

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