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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: MLOps1 (AWS) - Deploying AI & ML Models in Production using Amazon Web Services.
This is the second of three courses in the Machine Learning Operations Program using Amazon Web Services (AWS).
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 and human-nature reasons. 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(AWS): Deploying AI & ML Models in Production.
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 monitor the model’s performance and follow best practices
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
Week 1: The Machine Learning Pipeline
AI Engineering Role
ML pipeline lifecycle
Week 2: The Model in the Pipeline
Case Study for the Course
Model Understanding
Week 3: Monitoring Model Performance
Logging and Metric Selection
Model and Data Versioning
Week 4: Training Artifacts and Model Store
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
