MLOps1 (Azure): Deploying AI & ML Models in Production Using Microsoft Azure Machine Learning
This is the second of three courses in the Machine Learning Operations Program using Azure Machine Learning.
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 (Azure): Deploying AI & ML Models in Production using Microsoft Azure Machine Learning.
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
Upcoming start dates
- Self-paced Online
Who should attend?
- 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.
- 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
Course delivery details
This course is offered through Statistics.com, a partner institute of EdX.
5-7 hours per week
- Verified Track -$149
- Audit Track - Free
Certification / Credits
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
Contact this provider
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