From building pipelines and deploying models to understanding cloud ML platforms.
Automating the workflow from data preparation to model training.
Making your trained model available to make predictions on new data.
Learning from real-world examples of successful ML implementation.
An overview of services like AWS SageMaker, Google Vertex AI, and Azure ML.