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Practical ML Ops for Data Scientists

4.7 (3)

·

3 Weeks

·

Cohort-based Course

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

Previous Affiliations

Google
MIT research group
SUTD Singapore
King's College London | London
WSO2

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, which are 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

01

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

02

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

03

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

Week 1

Oct 26—Oct 27

    Oct

    26

    Session 1

    Sat 10/264:00 PM—6:00 PM (UTC)

Week 2

Oct 28—Nov 3

    Nov

    2

    Session 2

    Sat 11/25:00 PM—7:00 PM (UTC)

Week 3

Nov 4—Nov 10

    Nov

    8

    Optional: Office hours with Akshika

    Fri 11/89:00 PM—10:00 PM (UTC)
    Optional

    Nov

    9

    Session 3

    Sat 11/95:00 PM—7:00 PM (UTC)

Week 4

Nov 11—Nov 16

    Nov

    15

    Optional: Office hours with Akshika

    Fri 11/159:00 PM—10:00 PM (UTC)
    Optional

Post-course

    MLOps Case Studies from Industry

    1 item

    ML Ops Fundamentals and Project Ideation

    5 items

    Dockerizing ML Training/Inference and Productionising Notebooks

    3 items

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

    3 items

    Capstone Project Review

    1 item

4.7 (3 ratings)

What students are saying

You're in excellent company with alums of

You're in excellent company with alums of

What people are saying

        Akshika's ML Ops course is outstanding. His clear explanations and practical insights made complex topics accessible and immediately applicable. Highly recommend his course for anyone looking to deepen their ML Ops knowledge.
Manu Jayawardana

Manu Jayawardana

Quant Researcher and Portfolio Manager at J.P. Morgan
        Not only is Akshika is an expert in his field, he's also incredibly skilled at breaking down complicated concepts so his students can grasp them quickly. Anyone who's interested in building foundational knowledge of ML Ops should take the opportunity to learn from Akshika -- it will be worth the investment.
Elizabeth Creighton

Elizabeth Creighton

Founder & Principal - Brazen
        I enjoy learning from Dr. Wijesundara. He helps break down ML model development and clearly has plenty of experience to help others learn about complex concepts like infrastructure set up.
Alissa Valentine

Alissa Valentine

PhD Candidate at Icahn School of Medicine at Mount Sinai
         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.

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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 8th Nov & 1:00 PM EST 19th Nov

  • 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

Stay in the loop

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

Frequently Asked Questions

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Practical ML Ops for Data Scientists