With data volumes rising exponentially over the past decade, the need for enterprises to gain data-backed business insights and make data-driven decisions has also increased. While Business Intelligence (BI) tools have helped analyze historical data and improved reporting within organizations, Artificial Intelligence (AI) and Machine Learning (ML) have driven efficiencies across existing processes and transformed enterprises by providing them with a data-backed competitive edge.
However, embedding AI and ML into an organization comes with its challenges. Some of these challenges are developmental, and next-generation low-code/no-code platforms or data scientist teams that develop specific use cases can help address them. Other challenges are operational, and enterprises need to deal with them in a structured manner and ease operationalization across multiple IT systems to make it easier for individual teams to use ML models.
This research focuses on the life-cycle management of ML initiatives and how AI technology vendors can help enterprises adopt a structured approach to scaling ML across their organizations by using Machine Learning Operations (MLOps).
Scope
All industries and geographies
Contents
In this report, we:
Explain various ML applications
Examine ML tools and their advantages
Identify the key capabilities required to succeed in adopting MLOps
Determine enterprise considerations when initiating ML journeys
