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Full-Stack Machine Learning with Metaflow

This course presents a hands-on introduction to production-grade tools that bridge the gap between laptop data science and production ML workflows. It covers a wide range of applications, including business-critical ML and data pipelines of today, as well as state-of-the-art generative AI and LLM use cases of tomorrow.

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Ville Tuulos
CEO, Outerbounds
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Hugo Bowne-Anderson
Head of Developer Relations at Outerbounds
US$ 400
or included with membership
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Course taught by expert instructors

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Ville Tuulos

CEO, Outerbounds

Ville Tuulos has been developing infrastructure for machine learning for over two decades. He has worked as an ML researcher in academia and as a leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for data science infrastructure. He is the co-founder and CEO of Outerbounds, a company developing modern human-centric ML. He is also the author of Effective Data Science Infrastructure, published by Manning.

Instructor Photo
Affiliation logo

Hugo Bowne-Anderson

Head of Developer Relations at Outerbounds

Hugo Bowne-Anderson is Head of Developer Relations at Outerbounds. He is also the host of the industry podcast Vanishing Gradients. Hugo is a data scientist, educator, evangelist, content marketer, and data strategy consultant, with extensive experience at Coiled, a company that makes it simple for organizations to scale their data science seamlessly, and DataCamp, the online education platform for all things data. He also has experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and ODSC and with organizations such as Data Carpentry.

The course

Learn and apply skills with real-world projects.

Who is it for?
  • ML Engineers and Data Scientists who want to take common machine learning models and productionize them with Metaflow.

  • Software engineers who want to build production systems that integrate ML.

  • Programming fundamentals and Python basics - variables, for loops (as covered in Uplimit Python Crash Course or elsewhere)

  • Foundation in numpy, pandas, scikit-learn (as covered in CoRise Intermediate Python for Data Science or elsewhere)

  • Familiarity with terminal/shell and Jupyter Notebooks

Not ready?

Try these prep courses first

  • Understand the problem space: business, organizational/cultural, and technical concerns
  • The dataflow paradigm and DAGs
  • The basics of Metaflow
  • Product Manager POV: Project Scope & Measuring Success
  • Baseline model workflows for common application (RecSys, NLP, or CV)
  • Thinking from Engineer POV: what does it mean to build this application from an engineering context?
  • How to build ML workflows with Metaflow
  • Versioning, model reporting, and notebooks
  • Iterative approach to building ML workflows
  • Engineer POV: De-risking & Contingency Planning
  • Hands-on experience building a reproducible ML workflow
  • Thinking from data engineering POV: what if we have 100GB of potentially messy data? Questions around ETL and interacting with data warehouses
  • How to interact with real-world size datasets in a variety of common formats
  • Sending particular steps in your ML workflow to the cloud using Metaflow (e.g. large training steps)
  • How to handle failures with Metaflow
  • Sending entire workflows to the cloud
  • Issues of dependency management
  • Data Engineering POV: Case Study/Scenario
  • Large-scale parallel training utilizing cloud compute
  • Production ML is a spectrum involving all layers of the stack (e.g. data, compute, versioning, and so on): create MVP production deployments early on
  • Understand production can mean many things: e.g. model hosting, writing results to a DB, building a report as a doc/email/slide deck.
  • How to deploy a baseline model, build a challenger model, and promote it to production using Metaflow
  • This will involve understanding how to measure success and how to report on it.
  • Build and deploy an end-to-end ML model

A course you'll actually complete. AI-powered learning that drives results.

AI-powered learning

Transform your learning programs with personalized learning. Real-time feedback, hints at just the right moment, and the support for learners when they need it, driving 15x engagement.

Live courses by leading experts

Our instructors are renowned experts in AI, data, engineering, product, and business. Deep dive through always-current live sessions and round-the-clock support.

Practice on the cutting edge

Accelerate your learning with projects that mirror the work done at industry-leading tech companies. Put your skills to the test and start applying them today.

Flexible schedule for busy professionals

We know you’re busy, so we made it flexible. Attend live events or review the materials at your own pace. Our course team and global community will support you every step of the way.


Completion certificates

Each course comes with a certificate for learners to add to their resume.

Best-in-class outcomes

15-20x engagement compared to async courses

Support & accountability

You are never alone, we provide support throughout the course.

Get reimbursed by your company

More than half of learners get their Courses and Memberships reimbursed by their company.

Hundreds of companies have dedicated L&D and education budgets that have covered the costs.


Frequently Asked Questions

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