Effective MLOps: Model Development

Effective MLOps: Model Development
Bringing machine learning models to production is challenging, with a continuous iterative lifecycle that consists of many complex components. Having a disciplined, flexible and collaborative process - an effective MLOps system - is crucial to enabling velocity and rigor, and building an end-to-end machine learning pipeline that continually delivers production-ready ML models and services.
4 Hours
Free

Curriculum

  • Welcome to the course!
  • Building an End-to-End Prototype
  • Moving Beyond Baseline
  • Model Evaluation

Your goals

  • Accelerate and scale your model development
  • Improve your productivity
  • Ensure the reproducibility of your results
  • Iterate and train better models, faster
Reviews
Recommended to everyone! By giving a systematic approach and best practices when tackling MLOps, this course is great both for starters and advanced ML enthusiasts. I really recommend checking out the "MLOps: A Holistic approach" document too. This way you can complement and have always ready the considerations shown in this course with you!
MLOps from 0 to HERO! I would strongly recommend this course to both my ML practitioners working in the industry as well as to my academic colleagues. It can save them time, as the rigorous part comparing and validating models, and let them focus more on the model development side.
I have just completed the two videos in welcome to my course, I am so excited to found that I would be building a model and optimize for better performance
Wonderful! An absolute must-have course for every modern machine learning professional
Excellent course! I recommend for everyone who wants to learn MLOps and is starting with W&B
Big fan of fastAI, kaggle, W&B and this course helps me become better ML engineer and make my best contributions in FOSS.
Course instructors

Hamel Husain

Hamel is currently a founder at Parlance Labs, a research lab and consultancy focused on LLMs. Previously he was an entrepreneur in residence at fast.ai, where he built tools for data scientists and machine learning engineers. He also worked at Airbnb, DataRobot, and GitHub where he built a wide array of machine learning products and infrastructure. Hamel has contributed to data and infrastructure tools in open source such as Metaflow, Kubeflow, Jupyter, and Great Expectations. Hamel was also a consultant for over 10 years, and used data science to improve business outcomes in the restaurant, entertainment, telecommunications, and retail industries.
Founder Parlance Labs

Darek Kłeczek

Darek Kłeczek is a Machine Learning Engineer at Weights & Biases, where he leads the W&B education program. Previously, he applied machine learning across supply chain, manufacturing, legal, and commercial use cases. He also worked on operationalizing machine learning at P&G. Darek contributed the first Polish versions of BERT and GPT language models and is a Kaggle Competition Grandmaster.
MLE Weights & Biases

Thomas Capelle

Thomas Capelle is a Machine Learning Engineer at Weights & Biases working on the Growth Team. He’s a contributor to fastai library and a maintainer of wandb/examples repository. His focus is on MLOps, wandb applications in industry and fun deep learning in general. Previously he was using deep learning to solve short term forecasting for solar energy at SteadySun. He has a background in Urban Planning, Combinatorial Optimization, Transportation Economics and Applied Math.
AI Engineer Weights & Biases
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