Core components of an ML platform
An ML platform is a complex system encompassing multiple environments for running distinct tasks and orchestrating complex workflow processes. Furthermore, an ML platform needs to cater to a multitude of roles, including data scientists, ML engineers, infrastructure engineers, operations teams, and security and compliance stakeholders. To construct an ML platform, several components come into play.
These components include:
- Data science environment: The data science environment provides data analysis and ML tools, such as Jupyter notebooks, data sources and storage, code repositories, and ML frameworks. Data scientists and ML engineers use the data science environment to perform data analysis, run data science experiments, and build and tune models. The data science environment also provides collaboration capabilities, allowing data scientists to share and collaborate on code, data, experiments, and models.
- Model training environment...
