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 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning

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 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning. 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.

Week 1 – Drift and Feedback Loops

Module 1: Training Versus Inference Pipelines

Module 2: Drift & Feedback Loops

Week 2 – Triggers, Alarms & Model Stability

Module 3: Triggers & Alarms

Module 4: Model Stability

Week 3 – CI/CD (Continuous Integration & Continuous Deployment/Delivery)

Module 5: CI/CD

Week 4 – Model Security and Responsible AI

Module 6: Responsible AI

Institution: StatisticscomX

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

MLOps 1 (Azure): Deploying AI and ML Models in Production using Microsoft Azure Machine Learning

Language: English

Video Transcript: English

Associated programs:

Professional Certificate in Machine Learning Operations with Microsoft Azure (MLOps with Azure)

Associated skills: Microsoft Azure, Data Science, Data Pipeline, Forecasting, Addressing Ethical Concerns, Machine Learning, Automation, Mathematical Optimization

What you\'ll learn

You will learn how to set up automated monitoring of your data pipeline for prediction and get hands on experience with topics like data pipelines, drift and feedback loops, model stability, triggers & alarms, model security, responsible AI and much more.

But most importantly, by the end of this course, you will know…

How to meet the differing requirements of model training versus model inference in your pipeline

How to check for model drift, data drift, and feedback loops

How to apply the principles of Continuous Integration (CI), Continuous Delivery (CDE) and Continuous Deployment (CD)