Class is in session

Practical ML Ops for Data Scientists

3 Weeks


Cohort-based Course

Master machine learning operations to effortlessly transition models from lab to production.

Previous affiliations

MIT research group
SUTD Singapore
King's College London

Course overview

Machine Learning Operations Mastery for Data Scientists 🚀

📚 Course Overview

In this cohort-based course, you will delve into the practical aspects of Machine Learning Operations (ML Ops) tailored specifically for data scientists. You'll learn the crucial steps involved in transitioning machine learning models from the experimental phase to robust deployment in real-world production environments.

🎯 What to Expect

By the end of the program, you will gain invaluable insights into:

Understanding ML Ops Fundamentals:

Learn about ML Ops' role in integrating ML models into production and overcome challenges in deploying and managing models effectively.

Structuring Your ML Ops Pipeline:

Explore ML Ops workflows for data preprocessing, model training, deployment, and monitoring, focusing on reproducibility, scalability, and automation with real-world case studies.

Practical Implementation with Project Ideation:

Select a dataset aligned with your interests, create an ML Ops pipeline tailored to your project idea, and apply course concepts for practical application.

Dockerizing ML Training/Inference and Productionizing Notebooks:

Gain insight into Docker's role in streamlining ML project development, with hands-on experience in containerizing ML applications for reproducibility and portability.

Model Training and Inference Preparation:

Discover strategies for ML model deployment preparation, covering dependency packaging, inference optimization, and considerations for local versus cloud deployment.

Git and CI/CD with GitHub Actions, Model Deployment, and SageMaker Integration:

Learn Git for collaborative development, set up CI/CD pipelines with GitHub Actions, and explore AWS SageMaker for scalable ML model deployment, including batch processing and real-time inference.

Exploration of MLOps Case Studies and Capstone Project:

Analyze diverse real-world MLOps implementations for insights, then present your capstone project for feedback, with encouragement to share on GitHub and social media.

🏗️ Course Structure

Participate in 3 deep-dive sessions and 2 one-hour office hours for clarification. Develop a capstone project integrating lesson learnings, shareable for skill showcase. Access 20+ curated resources and explore 10+ real-world ML deployment case studies.

🎓 By the end of the course, you will emerge equipped with the knowledge, skills, and confidence to seamlessly transition machine learning models from the lab to production, thus making a tangible impact in your organization's data-driven initiatives.

Who is this course for


Data scientists lacking formal software engineering backgrounds, yet seeking to deploy their models for production use.


Academics aspiring to transition into an industry role by acquiring practical knowledge of data science applications in an industry setting.


This is not intended for software engineers aiming to enter the field of machine learning operations.

What you’ll get out of this course

Foundational Knowledge:
  • Understand the role of ML Ops in the machine learning lifecycle.
  • Learn best practices for structuring ML Ops pipelines.
  • Gain insights from real-world case studies to inform project ideation.

Technical Skills:
  • Dockerize ML training and inference processes.
  • Prepare models for both local and cloud deployments.
  • Implement Docker and related tools to enhance ML projects.
Deployment Proficiency:
  • Set up Git and CI/CD workflows, including versioning strategies.
  • Deploy models using AWS SageMaker integration.
  • Refine ML Ops pipelines using AWS resources for improved deployment.
Practical Experience:
  • Explore real-world MLOps case studies to understand industry applications.
  • Present and receive feedback on capstone projects for skill refinement.
  • Gain confidence in project publication and showcasing on social media platforms.

This course includes

5 interactive live sessions

Lifetime access to course materials

9 in-depth lessons

Direct access to instructor

4 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

Expand all modules
  • Week 1

    Jul 6—Jul 7

    Week dates are set to instructor's time zone


    • Jul


      Session 1

      Sat, Jul 6, 4:00 PM - 6:00 PM UTC


    • ML Ops Fundamentals and Project Ideation

  • Week 2

    Jul 8—Jul 14

    Week dates are set to instructor's time zone


    • Jul


      Session 2

      Sat, Jul 13, 3:00 PM - 5:00 PM UTC


    • Dockerizing ML Training/Inference and Productionising Notebooks

  • Week 3

    Jul 15—Jul 21

    Week dates are set to instructor's time zone


    • Jul


      Optional: Office hours with Akshika

      Fri, Jul 19, 8:00 PM - 9:00 PM UTC

    • Jul


      Session 3

      Sat, Jul 20, 3:00 PM - 5:00 PM UTC


    • Git and CI/CD with GitHub Actions, Model Deployment, and SageMaker Integration

  • Week 4

    Jul 22—Jul 27

    Week dates are set to instructor's time zone


    • Jul


      Optional: Office hours with Akshika

      Sat, Jul 27, 4:00 PM - 5:00 PM UTC

  • Post-Course


    • Capstone Project Review

  • Bonus


    • MLOps Case Studies from Industry

What people are saying

         Akshika, a PhD in ML with leadership experience, excels in both knowledge and mentoring. His initiative-driven nature and track record of project success make him a standout leader in the field. Highly recommended asset for ML.
Heshan Andrews

Heshan Andrews

Software Engineer at Wise
        Akshika's exceptional mentorship during my Google Summer of Code was invaluable. His expertise in ML and software engineering, along with clear articulation of complex concepts, fueled my success. His adept teaching skills fostered my growth as a developer. Highly recommend him as a mentor in machine learning.
Jayasanka Weerasinghe

Jayasanka Weerasinghe

Engineering Lead at OpenMRS

Meet your instructor

Akshika Wijesundara, PhD

Akshika Wijesundara, PhD

🚀 Leading AI innovation in financial services at MainStreet Partners, while advising Tilli Kids as CTO. Ph.D. in Machine Learning and Postdoc at King's College London, Oxford ML summer school alum, and UK global talent visa recipient for exceptional talent in the field of machine learning. Extensive experience in academia and industry across financial services, Large Language Models, ML-Ops, Explainable AI, privacy, and HCI.

A pattern of wavy dots
Be the first to know about upcoming cohorts

Practical ML Ops for Data Scientists


Bulk purchases

Course schedule

4-5 hours per week
  • Saturdays

    1:00pm - 3:00pm EST

    The live sessions will be held on Saturdays at the same time.

  • Office Hours

    2 hours per course

    4:00 PM EST 19th July & 1:00 PM EST 27th July

  • Weekly projects

    1.5 hours per week

    We will conceptualize an ML Ops project that can be developed iteratively over the course, culminating in the publication of your capstone project at the course's conclusion.

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

What happens if I can’t make a live session?
I work full-time, what is the expected time commitment?
What’s the refund policy?
A pattern of wavy dots
Be the first to know about upcoming cohorts

Practical ML Ops for Data Scientists


Bulk purchases