MLOps Challenges and How to Face Them - neptune.ai

#artificialintelligence 

Somewhere around 2018, enterprise organizations started experimenting with Machine Learning (ML) features to build add-ons to their pre-existing solutions, or to create brand new solutions for their clients. If you threw in a few sci-fi-sounding ML features in your offer, you could attract more clients who were interested in trying out the newest tech. In current MLOps trends, the narrative has changed almost completely. Every year, Artificial Intelligence (AI) sees exponential advancements compared to technology from any other era. The field is evolving extremely quickly, and people are more aware of its limitations and opportunities. Overall, we can say that AI/ML solutions are becoming equivalent to regular software solutions in terms of how companies use them, so it's no surprise that they need a well-planned framework for production just like DevOps. This well-planned framework is MLOps.

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