Machine Learning DevOps Engineer (training)

Colleagues, the Machine Learning DevOps Engineer training program will equip you to streamline the integration of machine-learning models and deploy them to a production environment. Acquire core skills in Clean Code Principles, Building a Reproducible Model Workflow, Deploying a Scalable ML Pipeline in Production plus ML Model Scoring and Monitoring. Training modules include: 1) Introduction to Machine Learning DevOps Engineer - 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. Then you will further expand your git and Github skills to work with teams. 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, 2) Clean Code Principles, 3) Building a Reproducible Model Workflow - become more efficient, effective, and productive in modern, real-world ML projects by adopting best practices around reproducible workflows. Learn 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. It also touches on Kubernetes, Kubeflow, and Great Expectations and how they relate to the content of the class, 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). Continuous Integration and Continuous Deployment is also covered which will be accomplished using GitHub Actions and Heroku, respectively. Finally, students will learn how to write a fast, type-checked, and auto-documented API using FastAPI, and 4) ML Model Scoring and Monitoring - automate the devops processes required to score and re-deploy ML models. Students will automate model training and deployment. Students will learn to diagnose operational issues with models, including data integrity and stability problems, timing problems, and dependency issues, and learn to set up automated reporting with API’s.

Enroll today (teams & executives are welcome): https://tinyurl.com/4358bm9v 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) https://tinyurl.com/mae9ku3b or (Kindle) https://tinyurl.com/27jux34w 

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” audiobook on Audible (https://tinyurl.com/bdfrtyj2) or ebook on Kindle (https://tinyurl.com/jfntsyj2) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Kindle) https://tinyurl.com/4bmmad9k  (Audible - coming soon!)

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