About this course

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course: MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services. In this course you will learn how to set up automated monitoring of your data pipeline for prediction. Data drift, model drift and feedback loops can impair model performance and model stability, and you will learn how to monitor for those phenomena. You will also learn about setting triggers and alarms, so that operators can deal with problems with model instability. You will also cover ethical issues in machine learning and the risks they pose, and learn about the “Responsible Data Science” framework.

At a Glance:
Institution: Statistics.comX
Subject: Computer Science
Level: Intermediate
Prerequisites:
Participants should have taken the first two courses (below) and be comfortable working with Python in a cloud-based environment. Learners will gain maximum benefit if they have some familiarity with software development, including git, logging, testing, debugging, code optimization and security.
Predictive Analytics: Basic Modeling Techniques
MLOps1 (AWS): Deploying AI and ML Models in Production using Amazon Web Services
Language: English
Video Transcript: English
Associated programs:
Professional Certificate in Machine Learning Operations with Amazon Web Services (MLOps with AWS)
Associated skills:Amazon Web Services, Data Science, Forecasting, Mathematical Optimization, Machine Learning, Automation, Amazon Data Pipeline, Addressing Ethical Concerns, Data Pipeline