The "Make AI Work" courses cover the following key topics:
Design Stage of the Machine Learning Lifecycle - including understanding business challenges, defining success criteria, exploring initial data landscapes, and designing overall solutions
Build Stage of the Machine Learning Lifecycle - including exploratory data analysis, feature engineering, model experimentation, model performance tuning, model evaluation and testing, and model selection
Productionize Stage of the Machine Learning Lifecycle - including model packaging, model serving, data versioning and feature store, model versioning and model store, implementing MLOps solutions, best practices and ML pipelines
Make AI Work on AWS - design, build, train, tune, and deploy machine learning (ML) models on AWS leveraging services such as AWS SageMaker, AWS S3 and so on. You will have the skills, knowledge and confidence to pass the certificate exam of "AWS Certified Machine Learning - Specialty Certification"
Make AI Work on Azure - design, build, train, tune, and deploy machine learning (ML) models on Azure leveraging services such as Azure Machine Learning Workspace, Azure ADLS Gen2 and so on. You will have the skills, knowledge and confidence to pass the certificate exam of "Azure Data Scientist Associate"
Make AI Work on GCP - design, build, train, tune, and deploy machine learning (ML) models on GCP leveraging services such as Google Cloud AI Platform, Google Cloud Storage and so on. You will have the skills, knowledge and confidence to pass the certificate exam of "GCP Professional ML Engineer"
