Recently, AI has become the bedrock of business transformation for enterprises. The technology is increasingly being seen as a business enabler and pertinent investment in helping firms maneuver and reverse the COVID-19 pandemic’s impact. However, challenges such as the lack of skilled AI talent, increasing time and effort in scaling AI implementation, and rising privacy concerns and regulatory impositions, are impeding organizations’ AI vision.
Machine Learning Operations (MLOps), a confluence of machine learning and IT operations based on the concept of DevOps, is emerging as a panacea for enterprises in this scenario. MLOps, a set of practices aimed at streamlining ML life-cycle management, aims to enhance collaboration among data scientists and operations teams, thereby accelerating the scaling of AI. To address enterprise concerns around MLOps, multiple tools and platforms have also emerged across data management, modeling, and model deployment and monitoring.
As enterprises embark on their MLOps journeys, it will be imperative for them to assess their existing maturity levels, develop hybrid teams, determine the KPIs required to assess the model, and ensure governance and compliance with industry regulations.
Scope
All industries and geographies
Contents:
This report studies:
- Challenges faced by enterprises in scaling their AI implementations
- Introduction to Machine Learning Operations (MLOps)
- Key benefits of MLOps
- The MLOps ecosystem, including tools and platforms across the value chain
- Key imperatives for enterprises in their MLOps journeys
Membership(s)
Digital Services
Sourcing and Vendor Management
