Bridging the gap between the development and production setup is key
There is a visible gap between developing an ML application in an experimental setup and deploying it in production.
That’s because the difference between experimental or development setup and the production setup does not merely stem from the size of the dataset or data recency. Additional factors such as production performance requirements, security, access control, and stable libraries must also be considered. Successful transfer from the development to the production environment also demands dedicated roles, depending on factors such as ML team size, number of ML applications, and release frequency. Many ML organizations task their data scientists with the additional responsibility of production release management. This takes precious time away from data scientists and severely limits their ability to work on developing more value-creating solutions. Other organizations may require their data engineers to take on this responsibility. But most data engineers lack a complete understanding of the ML application lifecycle and DevOps.
Successful operationalization of ML applications requires not only a well-defined ML operationalization process and dedicated roles but also an enabling toolset. Relying on existing DevOps toolset will not suffice as they are not fully equipped to address unique ML requirements such as data-dependent algorithms, data lineage tracking, model versioning, model performance monitoring, drift detection, model explainability and so on.
The good news is several open-source and commercial MLOps (machine learning operations) tools are emerging that can assist businesses in creating a suitable MLOps stack – if the organization’s unique requirements are clearly articulated. Using these new tech stacks, organizations can simplify data management, model development, and deployment to scale easily across the full ML workflow cycle.