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 data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we developed this course: Machine Learning Ops (MLOps1) - Deploying AI & ML Models in Production using Microsoft Azure Machine Learning (AML).
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This is the second of three courses in the Machine Learning Operations using AML program.
Data Science, AI, and Machine Learning projects can deliver an amazing return on investment. But, in practice, most projects that look great in the lab (and would work if implemented!) never see the light of day. They could save or make the organization millions of dollars but never make it all the way into production. What’s going on? It turns out that making decisions in a whole new way is a big challenge to implement--for many technical, business andhuman-naturereasons. After decades of experience though, our team has learned how to turn this around and actually get working models into production the great majority of the time. A key part of deployment is excellence in data engineering, and is why we developed this course: MLOps1: Deploying AI & ML Models in Production using AML.
You will get hands on experience with topics like data pipelines, data and model “versioning”, model storage, data artifacts, and more.
Most importantly, by the end of this course, you will know...
- What data engineers need to know to work effectively with data scientists
- How to embed a predictive model in a pipeline that takes in data and outputs predictions automatically
- How to moniter the model’s performance and follow best practices
This course is part of the Machine Learning Operations with Microsoft Azure (MLOps with Azure) Professional Certificate.
What you'll learn
- What data engineers need to know in order to work effectively with data scientists
- How to use a machine learning model to make predictions
- How to embed that model in a pipeline that takes in data and outputs predictions automatically
- How to measure the performance of the model and the pipeline, and how to log those metrics
- How to follow best practices for “versioning” the model and the data
- How to track and store model and data artifacts
Syllabus
Week 1: The Machine Learning Pipeline
- AI Engineering Role
- ML pipelin lifecycle
Week 2: The Model in the Pipeline
- Case Study for the Course
- Model Undeerstanding
Week 3: Monitoring Model Performance
- Logging and Metric Selection
- Model and Data Versioning
Week 4: Training Artifacts and Model Store
Prerequisites:
Predictive Analytics: Basic Modeling Techniques
Participants should be comfortable working with Python in a cloud-based environment, and will gain maximum benefit if they have some familiarity with software development, including git, logging, testing, debugging, code optimization and security.
