3 Weeks
·Cohort-based Course
Master machine learning operations to effortlessly transition models from lab to production.
Previous affiliations
Course overview
📚 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.
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.
5 interactive live sessions
Lifetime access to course materials
7 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.
Practical ML Ops for Data Scientists
Week 1
Jun 15—Jun 16
Events
Sat, Jun 15, 4:00 PM - 6:00 PM UTC
Modules
Week 2
Jun 17—Jun 23
Events
Sat, Jun 22, 4:00 PM - 6:00 PM UTC
Modules
Week 3
Jun 24—Jun 30
Events
Fri, Jun 28, 8:00 PM - 9:00 PM UTC
Sat, Jun 29, 4:00 PM - 6:00 PM UTC
Modules
Week 4
Jul 1—Jul 6
Events
Sat, Jul 6, 4:00 PM - 5:00 PM UTC
Post-Course
Modules
Bonus
Modules
Heshan Andrews
Jayasanka Weerasinghe
🚀 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.
Cohort 1
$499 USD
Dates
Payment Deadline
1:00pm - 3:00pm EST
The live sessions will be held on Saturdays at the same time.
2 hours per course
4:00 PM EST 19th July & 1:00 PM EST 27th July
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.
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
Cohort 1
$499 USD
Dates
Payment Deadline