Cauchy

Live cohort

Live cohort 2

Cohort 4

Cohort 3

Cohort 1

Cohort 2

Do you want to know the right way to do MLOps on Databricks? This course is for you!


Maria Vechtomova

MLOps Tech Lead | Databricks Beacon | 10+ years in Data & AI

Başak Eskili

Senior ML Engineer | 7+ years in Data & AI

End-to-end MLOps with Databricks

Customer Insights Analyst

Data scientist

MLOps Engineer

Computer engineer

Sr. Data Engineer

AI Engineer

Data engineer

Senior Manager / Tech Lead Data Science & Analytics

Supply Chain Data Scientist

Data Scientist

This is the last cohort of this course! If you considered to join, this is your last opportunity! Implementing MLOps practices elevates data scientists and speeds up time to production. We've seen it through our careers. MLOps is not about what tools you use, it is about how you use them to follow MLOps principles. For any given machine learning model run/deployment in any environment, it must be possible to look up unambiguously: - corresponding code/commit on git;- infrastructure used for training and serving;- environment used for training and serving;- ML model artifacts;- what data was used to train the model. We teach you how to follow these principles using Databricks and develop on Databricks following the best software engineering practices. We spent the last 3 years working with Databricks and figuring it out with new features appearing all the time (such as Unity catalog, model serving, feature serving, Databricks Asset Bundles). It was not straightforward due to lacking documentation and notebook-first available training materials. In this course, we share all the knowledge we gained during our journey. Prerequisites: Python experience, basic knowledge of git, CI/CD.

Learn the right way to implement MLOps best practices on Databricks


Course syllabus

Live sessions where we walk you through the week's materials.

Wednesdays


Weekly projects

Course schedule

Machine learning engineers who are familiar with MLOps but do not know how to do it on Databricks.

Machine learning engineers who are familiar with Databricks, but not familiar with the latest features.

Data scientists who work with Databricks, and want to know more about MLOps.

Who is this course for

Topics covered


<ul><li>MLOps toolbelt</li><li>Principles behind MLOps</li><li>Databricks MLOps components</li></ul>

MLOps principles and components


<ul><li>Developing in Python: best software development principles</li><li>Dbconnect &amp; VS code extension</li><li>Databricks Folders</li><li>From a notebook to production-ready code</li></ul>

Developing on Databricks


<ul><li>What is DAB?</li><li>Asset bundles components</li><li>Defining complex workflow in asset bundles</li><li>Using private packages in asset bundles</li></ul>

Databricks asset bundles (DAB)


<ul><li>Databricks'recommended approach</li><li>CI/CD pipeline with GitHub actions and Asset Bundles</li></ul>

Git branching strategy & Databricks environments


<ul><li>MLflow components</li><li>Track experiments &amp; search for experiments</li><li>Custom models in MLflow</li><li>Registering models in Unity Catalog</li></ul>

MLflow experiment tracking & registering models in Unity Catalog


<ul><li>Overview of architectures and use cases</li><li>Feature serving</li><li>Model serving (with automatic feature lookup)</li></ul>

Model serving architectures


<ul><li>What are inference tables</li><li>Setting up model evaluation pipeline</li><li>Data/model drift detection and lakehouse monitoring</li></ul>

Inference tables and lakehouse monitoring


Do you want to know the right way to do LLMOps on Databricks? This course is for you!


MLOps Tech lead. O'Reilly book author

Co-founder @Cauchy, MLOps Tech Lead, Databricks MVP. Writing a book for O'Reilly

ML engineer @booking.com

ML engineer at booking.com

Why This Course? Our highly rated MLOps course has helped hundreds of practitioners learn how to take ML models from prototype to production with confidence. Many of these principles apply to LLM applications. LLMOps is not just about prompt engineering. It’s about applying proven MLOps principles to the new challenges of LLM applications:- Reproducibility and traceability of LLM applications.- Reliable deployment &amp; monitoring.- Managing cost, performance, and safety in production. What You’ll LearnWe’ll teach you how to bring LLMs into production on Databricks while following modern software engineering and LLMOps best practices. In the course, we will:✅ Use MLflow Tracing and Evaluation to evaluate and trace LLM applications.✅ Log agents using MLflow and register them in Unity catalog.✅ Use MLflow Prompt optimization and Prompt Registry.✅ Utilize Databricks Vector Search Index.✅ Deploy agents using Mosaic AI Model Serving✅ Deep dive into managed Databricks MCP, and custom applications✅ Define and deploy resources using Databricks Asset Bundles✅ Apply CI/CD and DevOps patterns to LLM development.

Deploy LLM applications on Databricks


Deep understanding of LLMOps

Deploy end-to-end AI application on Databricks

This course is very hands-on, so basic Python knowledge is required.

Python experience

You are familiar with AI/GenAI applications and use cases. No deep knowledge is required.

Familiarity with AI/ GenAI

This is a highly practical course where you apply what you learned right away!

Be ready to roll up your sleeves and code!

Software Engineers who want to build and ship LLM applications into production

AI engineers who deploy AI applications but not familiar with Databricks

Engineers and data scientists who want to learn about the Ops side of LLM applications.

Building Reliable AI Systems with MLflow

MLOps Tech Lead, Databricks MVP

ML Engineer

Daniel Liden

Senior Developer Advocate at Databricks

Understand how you can use MLflow tracing and evaluation to build a reliable AI application.

Prototype and evaluate your AI application with MLflow

Understand how you can use a custom model to log and register your AI application, and then deploy it on Databricks.

Build and deploy your application on Databricks

Developing on Databricks: guide for AI engineers

Understand what compute configurations you need to choose for your use case.

Choose the right Databricks compute configuration

Understand different options Databricks developers have: Databricks CLI, dbconnect, VS code extension, asset bundles.

Understand Databricks Developer tools

Understand how to develop a Python project, package it, and execute it on a Databricks environment.

Learn an optimal way to use the developer tools

Navigating MLOps with Databricks

Understand main MLOps components and why you need them

MLOps components and principles

Understand how MLOps principles can be followed on Databricks

MLOps on Databricks

Deep-dive into various model serving architectures on Databricks and how those can be implemented

Learn directly from Cauchy

Cauchy

Courses

End-to-end MLOps with Databricks

LLMOps with Databricks

Lightning Lessons

Building Reliable AI Systems with MLflow

Developing on Databricks: guide for AI engineers

Navigating MLOps with Databricks