Core components of an ML platform

An ML platform is a complex system as it consists of multiple environments for running different tasks and has complex workflow processes to orchestrate. In addition, an ML platform needs to support many roles, such as data scientists, ML engineers, infrastructure engineers, and operation team members. The following are the core components of an ML platform:

• Data science environment: The data science environment provides data analysis tools, such as Jupyter notebooks, code repositories, and ML frameworks. Data scientists and ML engineers mainly use the data science environment to perform data analysis and data science experiments, and also to build and tune models.
• Model training environment: The model training environment provides a separate infrastructure for different model training requirements. While data scientists and ML engineers can run small-scale model training directly inside their local Jupyter environment, they need a separate...