Many organisations already have data scientists and ML researchers who can build state-of-the-art models, but the process of building, deploying and maintaining, still remains manual. This manual process limits businesses from effectively utilising its data across the data supply chain and rapidly delivering advanced analytics capability, leading to missed opportunities and poor return on investment. Traditionally across industries, DevOps has been widely adopted as methodologies to improve quality and reduce the time to market of software engineering initiatives. With the rapid growth in machine learning (ML) systems and in the context of ML engineering, MLOps provides those needed capabilities to handle the unique complexities of the practical application of ML systems.

PWC's Machine Learning Ops Accelerator is a set of standardized process and technology capabilities, that

• Provides the ability to build, deploy and operationalise ML systems rapidly and reliably at scale
• Empowers business to continuously build, deploy and improve ML models
• Improves collaboration between teams
• Reduces development cycle time and associated maintenance costs
• Accelerates innovation and thereby improve ROI on ML investments

The MLOps solution uses AWS native services such as Amazon Sagemaker, Codebuild, Codecommit, Cloudformation, S3, API Gateway, Lambda etc.