It recently dawned upon me that training and serving of ML models can be viewed as a supply chain across companies/part providers. This is a random musing, but if you are into the ML development, then hang on for the metaphor in the end.

Up until a few years (10?) ago, before the widespread use of cloud compute, many ML models would be trained completely in-house on-prem resources (the history of aws says AWS was launched in 2003 as a bid to manage infrastructure scaling for engineering needs). Nowadays, many in the industry use some form of cloud resources to train (/serve) their ML models salably and reliably. An analogy would be developers renting out factory equipment to perform their production deployments.

Beyond cloud compute and scaling solutions, there are several great third party tools, such as those for tracking of ML experiments, data versioning, ETL, creating pipelines from ML components, automating pipelines, and even auto-hyperparameter tuning!

The new kid on the block is 'data-centric' AI. We now have multiple players promising to provide solutions/tools to improve trained ML models by improving datasets, and not just tweaking the model architecture (Data-centric AI vs Model-centric AI).

What then is the core competency of a company that outsources several parts of the ML process? The core competency a company looking to develop an ML model in such an outsourced manner is perhaps that it owns the data, possibly the closed source code, the integration logic for various components and the right product/ market reach. We have seen similar business models where different components are outsourced, but the design, assembly, testing and improving lie with the final goods-services seller. Example: An automobile manufacturer might have different car parts sourced, but they are recognised for their ability to manufacture the car as a whole. Is that similar to the future of ML model producing entities?

A possible metaphor: companies own the data but outsource the other components before assembling all parts into one final product. This is partly like owning the soil and land, but outsourcing all other duties for tending to and growing plants to others.