Why the World Struggles To Productionalize ML-Driven Solutions
The data scientists working on versioning of the models, deploying, operating, monitoring, or in other words, doing MLOps practices take it as a challenge as it is a complex and laborious process. With the development of the technologies, the machine learning lifecycle has been developed as well. Now the application's logic is no longer captured in the code created by the software developer but reproduced by the ML model trained by the data scientist. And as the same problems occur repeatedly, the engineers working with AI-powered products have built an array of frameworks and tools for developing each new product based on machine learning.