Machine Learning DevOps Engineer (training)

Colleagues, time to up your game. The new “Machine Learning DevOps Engineer” training program will equip you to streamline the integration of machine-learning models and deploy them to a production-level environment. You will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring via four training modules - each with a hands-on project: 1) Clean Code Principles - Develop skills that are essential for deploying production machine learning models. First, you will put your coding best practices on auto-pilot by learning how to use PyLint and AutoPEP8. Finally, you will learn best practices associated with testing and logging used in production settings in order to ensure your models can stand the test of time (Project: Predict Customer Churn with Clean Code), 2) Building a Reproducible Model Workflow - Become more efficient, effective, and productive in modern, real-world ML projects by adopting best practices around reproducible workflows. In particular, it teaches the fundamentals of MLops and how to: a) create a clean, organized, reproducible, end-to-end machine learning pipeline from scratch using MLflow b) clean and validate the data using pytest c) track experiments, code, and results using GitHub and Weights & Biases d) select the best-performing model for production and e) deploy a model using MLflow (Project: Build an ML Pipeline for Short-term Rental Prices in NYC), 3) Deploying a Scalable ML Pipeline in Production - Deploy a machine learning model into production. En route to that goal students will learn how to put the finishing touches on a model by taking a fine grained approach to model performance, checking bias, and ultimately writing a model card. Students will also learn how to version control their data and models using Data Version Control (DVC). The last piece in preparation for deployment will be learning Continuous Integration and Continuous Deployment which will be accomplished using GitHub Actions and Heroku, respectively (Project: Deploying a Machine Learning Model on Heroku with FastAPI), and 4) Automated model scoring and monitoring - Automate the devops processes required to score and re-deploy ML models. Students will automate model training and deployment. They will set up regular scoring processes to be performed after model deployment, and also learn to reason carefully about model drift, and whether models need to be retrained and re-deployed. Students will learn to diagnose operational issues with models, including data integrity and stability problems, timing problems, and dependency issues. Finally, students will learn to set up automated reporting with API’s (Project: A Dynamic Risk Assessment System).

Enroll today (teams & execs are welcome): https://tinyurl.com/yc39fdst 

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Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share with your team)