End-to-end MLOps with Databricks

4.9 (15)

·

7 Weeks

·

Cohort-based Course

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

This course is popular

15 people enrolled last week.

Course overview

Learn the right way to implement MLOps best practices on Databricks

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.



Who is this course for

01

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

02

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

03

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

Topics covered

MLOps principles and components

  • MLOps toolbelt
  • Principles behind MLOps
  • Databricks MLOps components

Developing on Databricks

  • Developing in Python: best software development principles
  • Dbconnect & VS code extension
  • Databricks Folders
  • From a notebook to production-ready code

Databricks asset bundles (DAB)

  • What is DAB?
  • Asset bundles components
  • Defining complex workflow in asset bundles
  • Using private packages in asset bundles

Git branching strategy & Databricks environments

  • Databricks'recommended approach
  • CI/CD pipeline with GitHub actions and Asset Bundles

MLflow experiment tracking & registering models in Unity Catalog

  • MLflow components
  • Track experiments & search for experiments
  • Custom models in MLflow
  • Registering models in Unity Catalog

Model serving architectures

  • Overview of architectures and use cases
  • Feature serving
  • Model serving (with automatic feature lookup)

Inference tables and lakehouse monitoring

  • What are inference tables
  • Setting up model evaluation pipeline
  • Data/model drift detection and lakehouse monitoring

This course includes

7 interactive live sessions

Lifetime access to course materials

24 in-depth lessons

Direct access to instructor

8 projects to apply learnings

Guided feedback & reflection

Private community of peers

Course certificate upon completion

Maven Satisfaction Guarantee

This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.

Course syllabus

Week 1

Jan 27—Feb 2

    MLOps Principles and Components

    4 items

    Developing on Databricks

    4 items

    Jan

    29

    MLOps principles & developing on Databricks

    Wed 1/293:00 PM—5:00 PM (UTC)

Week 2

Feb 3—Feb 9

    MLflow: getting started

    6 items

    Custom models + feature Engineering on Databricks (Feature Store)

    4 items

    Feb

    5

    MLflow: getting started & custom models

    Wed 2/53:00 PM—5:00 PM (UTC)

Week 3

Feb 10—Feb 16

    Model Serving Architectures

    1 item

    Feature Serving

    2 items

    Model serving

    2 items

    Feb

    12

    Model serving architectures

    Wed 2/123:00 PM—5:00 PM (UTC)

Week 4

Feb 17—Feb 23

    Databricks Asset Bundles

    4 items

    Databricks catalogs, workspaces, and deployment patterns

    3 items

    Feb

    19

    DABs

    Wed 2/193:00 PM—5:00 PM (UTC)

Week 5

Feb 24—Mar 2

    Inference Tables & Lakehouse Monitoring

    1 item

    Feb

    26

    Inference tables & lakehouse monitoring

    Wed 2/263:00 PM—5:00 PM (UTC)

Week 6

Mar 3—Mar 9

    Self-study week + Q&A

    0 items

    Mar

    5

    Q&A/ Guest lecture

    Wed 3/53:00 PM—5:00 PM (UTC)

Week 7

Mar 10—Mar 16

    Capstone: Bringing All Learnings Together in One Place

    1 item

    Mar

    12

    Demo day

    Wed 3/123:00 PM—5:00 PM (UTC)

4.9 (15 ratings)

What students are saying

Excellent course, way better than any course I've ever taken. - Relevant course material for real world applications, many things I've learned I will apply at my company in the foreseeable future. - Nice structure with a weekly lecture / deliverable, making it convenient to plan it around work obligations - Having to hand in the deliverables through pull requests forces you to write quality code, simulating deliverables that you make at work - Great support through Discord The only suggestion I would give is to communicate the workload of the course on the registration page. Then again, the only way to learn MLOps properly is by spending the hours on it.

Martijn

Live cohort

This was the best MLOps course that I have ever followed. Maria and Basak are both super knowledgeable and passionate about the topic and are excited to pass it on to others. The content and lectures were extremely informative, the instructors were always helpful and engaging, the use case really helped put theory into practice, and the effort to create a lasting community outside of the course so that people can connect and continue to grow is unparalleled. I highly recommend this course for anyone who is interested in MLOps with Databricks or just MLOps in general.

Kevin

Live cohort

Machine Learning Engineer

Paula's Choice

Amazing course with amazing instructors. By far the most engaging and practical learning course I have ever done.

Garett

Live cohort

Senior Data Scientist

Heineken

Hands down the best course I have ever taken. Maria and Basak held weekly classes where students could participate in and get real time feedback. The weekly lessons had a direct effort on my day-to-day work life and actually increased my productivity level with Databricks. If you were thinking of taking an MLOps course this is the one!

Jake

Live cohort

Senior Business Analyst

Spencer's

I can’t recommend this course enough! Maria and Başak have put together the best MLOps course that covers everything you need to know from best practices and experiment tracking to deployment strategies and monitoring. With weekly hands-on deliverables you build your project step by step in the Databricks ecosystem. The lectures are a mix of theory and practical application, making them both engaging and interactive. On top of that, Maria and Başak are super supportive and always active in the community, which provides guidance through the whole course. Top!

Benito

Live cohort

Founder

Martin Data Solutions

Excellent and very well structured course covering all the features of Databricks in ML space. Maria and Basak are both very knowledgeable and experienced in Databricks ML engineering area.

Som

Live cohort

Technology Architect

KPMG

Meet your instructor

Maria Vechtomova

Maria Vechtomova

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

MLOps Tech Lead with 10+ years of experience, bridging the gap between data scientists, infra, and IT teams.


For the last 7 years, Maria has been focusing on MLOps (before it became a thing!) and has built MLOps frameworks multiple times with different sets of tools.

Başak Eskili

Başak Eskili

Senior ML engineer | 5+ years in Data & AI

Senior Machine Learning Engineer with 5+ years of experience across diverse industries including banking, retail, and travel.

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Join an upcoming cohort

End-to-end MLOps with Databricks

Live cohort 2

€750

Dates

Jan 26—Mar 15, 2025

Payment Deadline

Jan 28, 2025

Don't miss out! Enrollment closes in 6 days

Get reimbursed

Course schedule

4-6 hours per week

  • Wednesdays

    16:00-18:00 CET

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

  • Weekly projects

    2 hours per week


Learning is better with cohorts

Learning is better with cohorts

Active hands-on learning

This course builds on live workshops and hands-on projects

Interactive and project-based

You’ll be interacting with other learners through breakout rooms and project teams

Learn with a cohort of peers

Join a community of like-minded people who want to learn and grow alongside you

Frequently Asked Questions

A pattern of wavy dots

Join an upcoming cohort

End-to-end MLOps with Databricks

Live cohort 2

€750

Dates

Jan 26—Mar 15, 2025

Payment Deadline

Jan 28, 2025

Don't miss out! Enrollment closes in 6 days

Get reimbursed

€750

4.9 (15)

·

6 days left to enroll