What you'll learn in Practical ML Ops for Data Scientists
What you'll leave with
- 1A complete capstone ML Ops pipeline published on GitHub
- 2Practical skills to deploy ML models in production
- 3Certificate of completion (4.8 stars, 4 reviews)
- 420+ curated resources and 10+ real-world ML deployment case studies
Course Curriculum
Week 1ML Ops Fundamentals and Project Ideation
ML Ops Fundamentals and Project Ideation
- Session 1 (2 hours live)
- ML Ops Fundamentals and Project Ideation (5 modules)
Week 2Dockerizing ML Training/Inference
Dockerizing ML Training/Inference
- Session 2 (2 hours live)
- Dockerizing ML Training/Inference and Productionising Notebooks (3 modules)
Week 3Git, CI/CD & Model Deployment
Git, CI/CD & Model Deployment
- Optional Office Hours
- Session 3 (2 hours live)
- Git and CI/CD with GitHub Actions, Model Deployment, and SageMaker Integration (3 modules)
Week 4Feature Stores, Monitoring & Capstone
Feature Stores, Monitoring & Capstone
- Optional Office Hours
- Session 4 (2 hours live)
- Feature Stores and Monitoring (2 modules)
- Capstone Project Review
- Bonus: Kubernetes and KubeFlow
- Post-course: MLOps Case Studies from Industry
Who Practical ML Ops for Data Scientists is designed for
Prerequisites
Your Instructors
Akshika Wijesundara, PhD
ML Lead at MainStreet Partners | Ex-ML Engineer at MIT-IDC | ML PhD
Leading AI innovation in financial services at MainStreet Partners, while advising Tilli Kids as CTO. PhD 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.
What Students Say
“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
Quant Researcher and Portfolio Manager at J.P. Morgan
“Not only is Akshika 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
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
PhD Candidate at Icahn School of Medicine at Mount Sinai
“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
Engineering Lead at OpenMRS
Frequently Asked Questions about Practical ML Ops for Data Scientists
What will I learn in Practical ML Ops for Data Scientists?
Understand ML Ops fundamentals and overcome challenges in deploying models. Structure your ML Ops pipeline for reproducibility, scalability, and automation. Dockerize ML training and inference processes for portability. Set up Git and CI/CD pipelines with GitHub Actions. Deploy models using AWS SageMaker for batch and real-time inference. Implement feature stores and monitoring for production ML systems
Who is Practical ML Ops for Data Scientists designed for?
Data scientists lacking formal software engineering backgrounds seeking to deploy models to production. Academics aspiring to transition into an industry role with practical ML knowledge. NOT intended for software engineers aiming to enter ML operations
What are the prerequisites for Practical ML Ops for Data Scientists?
Foundational machine learning knowledge. Basic Python familiarity. Interest in productionizing ML models
How long does Practical ML Ops for Data Scientists take?
4 weeks. Format: Cohort-based.
What will I leave with after completing Practical ML Ops for Data Scientists?
A complete capstone ML Ops pipeline published on GitHub. Practical skills to deploy ML models in production. Certificate of completion (4.8 stars, 4 reviews). 20+ curated resources and 10+ real-world ML deployment case studies
Is Practical ML Ops for Data Scientists available online?
Yes, Practical ML Ops for Data Scientists is delivered entirely online as a cohort-based.
Who teaches Practical ML Ops for Data Scientists?
Akshika Wijesundara, PhD — ML Lead at MainStreet Partners | Ex-ML Engineer at MIT-IDC | ML PhD
How do I enroll in Practical ML Ops for Data Scientists?
You can enroll via Maven at https://maven.com/theaiinternship/mlops-for-ds. Click the "Enroll on Maven" button on this page.
Topics covered
Corporate Training & Team Upskilling
Train your entire team on Practical ML Ops for Data Scientists. We offer corporate group training, custom cohorts, and enterprise licensing. Trusted by teams at Google, Meta, Oracle, and more.