MLOps1 (Azure): Deploying AI & ML Models in Production 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 data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we developed this course: MLOps1 (Azure) - Deploying AI & ML Models in Production using Microsoft Azure Machine Learning

Monthly Subscription Starting at AED 99 + VAT

Level Awareness
Ratings
4.6
Duration 28 hours
Course by Statistics.com
Total students 2,531 enrolled
Offered by

About

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

What you will 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

Skills you learn

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

Auto Summary

Unlock the potential of your data science projects with **MLOps1 (Azure): Deploying AI & ML Models in Production using Microsoft Azure Machine Learning**. This course offers a deep dive into the critical phase of deployment, a common stumbling block in many data science initiatives. Designed specifically for IT and computer science professionals, this program emphasizes the synergy between data engineers and data scientists to monitor and enhance model performance effectively. Led by industry experts from edX, this 28-hour course equips you with the essential skills to deploy AI and ML models seamlessly using the robust tools provided by Microsoft Azure Machine Learning. Whether you're a seasoned professional or just beginning your journey in machine learning, this course, set at an awareness level, is tailored to meet your needs. Flexible subscription options, including Professional and Starter packages, allow you to choose the plan that best fits your learning goals and budget. Join a community of learners dedicated to overcoming the challenges of model deployment and transform your data science projects into success stories.