MLOps: From Models to Production
US$ 400
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MLOps: From Models to Production

Acquire the skills to build effective real-world ML systems (bootstrapping datasets, improving label quality, experimentation, model evaluation, deployment and observability) with hands-on projects. This course will help you bridge the gap between state-of-the-art ML modeling, and building real-world ML systems.

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Nihit Desai
CTO of Refuel.AI (ex-Facebook, Stanford)
US$ 400
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Nihit Desai

CTO of Refuel.AI (ex-Facebook, Stanford)

Nihit Desai is the CTO and co-founder of Refuel.AI, an early stage ML infrastructure startup. Prior to this, he was a Staff Engineer at Facebook where he led ML efforts for content moderation. In prior roles, he has worked on large scale recommender systems at Instagram, and on search quality at LinkedIn. He holds a Masters degree in Computer Science from Stanford, specializing in Artificial Intelligence.

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The course

Learn the skills and best practices for building effective real-world ML systems. Some examples include: bootstrapping data for new ML projects, improving data & label quality, experiment tracking, model evaluation, deployment and observability. The goal of this course is to help bridge the gap between state-of-the-art ML modeling, and building real-world ML systems.

Week by week, we will develop an understanding of the role of MLOps in building and maintaining real-world ML systems with hands-on projects using Python (e.g. Sklearn, Numpy and Pytorch) and industry-standard infrastructure tools (e.g. Docker). Position yourself to capitalize on this growing area as a member of our beta cohort.

Week 1
Data & Modeling
  • Archetypes of real-world ML applications
  • The production ML lifecycle
  • Why data quality and quantity are critical for real-world ML success
A machine learning model to predict news categories from news article text.
  • Exploratory data analysis
  • Model training & hyperparameter optimization
  • Fine-tuning state-of-the-art pretrained transformer models for NLP tasks
Week 2
Model Evaluation
  • Designing good model evaluation metrics
  • Model underfitting and overfitting: what are they, and how to address them
  • Behavioral testing for ML models
Test and evaluate the news classification from Week 1, and conduct error analysis.
  • Establish bounds on model performance with human annotation baseline
  • Behavioral testing for ML models
  • Testing for statistical properties of datasets
Week 3
Model Deployment & Inference
  • Options for deploying models online: common scenarios & tradeoffs
  • Feature Stores
  • Good practices to ensure production stability: gated rollouts, shadow mode deployment, online experimentation, and easy rollbacks
Wrap the trained and tested model from week 2 in a lightweight web service. Deploy the service and test it online.
  • Wrap the model and data pipeline in a python FastAPI web service
  • Containerize the service using Docker
  • Basic integration testing for containerized service
  • Deploy the service and test it online
Week 4
Model Monitoring & Maintenance Post-Deployment
  • How MLOps practices evolve as a function of team and company maturity
  • Logging and monitoring infrastructure for ML applications
  • Data and concept drift in Machine Learning
  • CI/CD for ML models
Monitoring and online performance tracking in ML systems.
  • Statistical data and concept drift measures
  • Model performance measurement
  • Outlier detection
Poorna KumarSenior Manager, Machine Learning @ Upstart; prev: ML, Statistics @ Stanford

Nihit has a rare set of skills and experiences - building large-scale ML production systems at top companies, along with a solid and rigorous research background. Along with that, he is great at distilling and passing on his hard-won insights and knowledge. I've learned a lot from his newsletter and the talks he's given to large audiences at Upstart - so I know first-hand how valuable and practical this class will be, and can't think of a better instructor!

Neil DhruvaMachine Learning Engineer @ Glean; ex-Facebook

Nihit has extensive experience building ML systems for recommendations, ranking and integrity problems at Facebook and LinkedIn. His expertise lies not only in developing and improving deep learning techniques but also in working with large scale systems that scale to billions of users. It’s a combination of both these skill sets that makes him a great fit to teach an MLOps course that requires an in-depth understanding of ML fundamentals and the ability to build out scalable systems that deal with constantly growing and ever-changing datasets in the real-world.

Rishabh BhargavaCo-Founder and CEO @ ML infra startup; co-editor of MLOpsRoundup

Nihit combines a deep theoretical understanding of ML with hands-on practical knowledge from having built large-scale search, recommender, and decisioning ML systems at the most impactful Internet companies. If I had to learn how to go from an idea to a working, scalable ML system, there would be no better instructor than Nihit!

This course is for...

Software engineers who want to build production systems that integrate ML

Data scientists who want to get hands on experience with the production ML lifecycle

Students/recent college grads who want to learn about building and shipping ML applications


Knowledge of basic machine learning concepts.

Familiarity with software development in Python.

Recommended: Familiarity with Docker, cloud ecosystems such as AWS.

Course experience

Live Sessions with Experts

Top industry leaders teach you everything you need in only 4 weeks

Interactive Learning

Real-world projects put your learning into immediate action

Professional Communities

Grow your network by learning with an intimate cohort of peers from top companies
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