Staging environment

Research to Deployment: Get AI into the Real World

Cohort-based Course

Learn how to manage data science teams and get models into production.

Course overview

Go from model requirements to production in 6 weeks

Data Science projects are full of uncertainty, making it impossible to plan everything without first doing something.


In this course, you will learn how to first target risk and benefit from having a fixed timeline and a flexible scope to finish significant data science projects in 6-week cycles.


Why should you trust us?

This course is based on the handbook we (Hakim and Wojtek) used at NannyML, a machine learning monitoring company with solid research foundations, to lead our research data science team. Over the last four years, we've refined it to address some of the most challenging research questions, resulting in the development of four novel ML monitoring algorithms that are now in production.


NannyML has been downloaded by over 500,000 people and is trusted by dozens of Fortune 500 companies. Our software stands out for its research-driven foundation, which is key to our ability to transition effectively from research to production.


Who should take this course?

— Lead Data Scientists, Data Science Managers, Heads of Data Science, or anyone leading a team that aims to put ML/AI models into production.

—Anyone who has struggled to put models into production in the past.


You should NOT take this course if:

— Your projects don't need any data science or machine learning.

— You are very early in your data science career.


If you're still interested and want to learn from people who have successfully deployed novel algorithms in 6-week cycles, read on.

This Course Is For You If You Are

01

A data science leader looking to put models into production faster.

02

Tired of getting stuck in the POC phase.

03

Ready to lead your team in building a research process that enables them to deliver novel results.


What you’ll get out of this course

Design and implement a six-week plan to get Data Science projects into production, focusing on sustainable and scalable practices.


Tackle Data Science management challenges like vague requirements, data leaks, and models getting stuck in the prototyping phase forever.


Understand the key differences between software engineering and data science projects, and explain why agile doesn't work for data science.


Adapt the "Shape Up" methodology for Data Science projects to ship models to production more frequently and efficiently.


Kick off your organization’s first project using a "Shape Up—like" methodology, and demonstrate its value through a successful pilot run.

This course includes

Interactive live sessions

Lifetime access to course materials

20 in-depth lessons

Direct access to instructor

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

Bonus

    Data science vs Engineering

    2 items

    Shape Up for Data Science

    3 items

    Shape Up Principles

    1 item

    How to Shape Data Science Projects — Part 1

    4 items

    How to Shape Data Science Projects — Part 2

    3 items

    Day-to-day Operations

    4 items

    Implementing Shape Up for Data Science at your Company

    3 items

A view of everything we will cover in this course

A view of everything we will cover in this course

What people are saying

        We had a great workshop from NannyML experts on monitoring ML models in production. They have a very extensive product related to all kinds of possible drift for a ML model. They have done various research, looking at hundreds of models and datasets across industries and are very knowledgeable on this topic.
Tuba Dikici

Tuba Dikici

Senior Machine Learning Engineer at Nike

Meet your instructor

Hakim Elakhrass

Hakim Elakhrass

CEO at NannyML

Co-founder At NannyML, one of the most popular ML monitoring tools. He has almost a decade of data science experience. Hakim holds a Masters Degree in Bioinformatics from the KU Leuven.

Wojtek Kuberski

Wojtek Kuberski

CTO at NannyML

Wojtek is an AI professional and entrepreneur with a master's degree in AI from KU Leuven. He co-founded NannyML, an OSS Python library for ML monitoring and post-deployment data science. As the CTO, he leads the research and product teams, contributing to the development of novel algorithms in model monitoring.


  • Defined the strategy and managed the execution of NannyML OSS and Cloud
  • Scoped, managed and delivered multiple 7-figure+ Data Science projects
  • Successfully delivered Data Science solutions in Finance, Manufacturing, Marketplace, Legal, Energy, Advertising and many other fields
  • 8 years in Data Science and 5 Years managing Data Science and Product teams
  • Hired and managed 30+ Senior and Lead data scientists and Engineers


A pattern of wavy dots

Be the first to know about upcoming cohorts

Research to Deployment: Get AI into the Real World

Course schedule

2-3 hours per week

  • Weekly sessions

    5:00pm - 6:30pm CET

    We meet every Thursday from October 24 to December 12.

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

Stay in the loop

Sign up to be the first to know about course updates.

A pattern of wavy dots

Be the first to know about upcoming cohorts

Research to Deployment: Get AI into the Real World