Why MLOps? As companies gain more experience and business value from AI and ML, the adoption of AI and ML is gathering pace that leads to new challenges. The key ones are around an ability to deploy quality models into production at high velocity, to identify the right time to retrain the models and to enable teams with the right mix of skills and tools to work efficiently ensuring that the cost of ML capability is affordable to the business and delivers the right level of ROI.

Companies that see machine learning as strategic are developing their MLOps capability - a set principles, practices, processes and technologies to streamline and automate the ML workflow based on DevOps discipline, to enable the use of AI ML at scale

Data Reply MLOps Assessment Approach
Data Reply MLOps Assessment Framework evaluates customers’ Skills, Processes, Tools & Technology deployed in the ML Lifecycle against MLOps best practice and the MLOPs AWS Reference Architecture

Key Activities

• ML Ops Requirements and Capability Assessment - delivered through workshops, reviewing existing customer documentation and practices
• MLOps Solution Design - understanding key challenges and gaps in the MLOps capability, reviewing technical options, relevant tools, ways of working and defining a target solution architecture and an ML operating model
• Playback and input into an MLOps Solution Roadmap and a Business Case

Deliverables

1. Target MLOps Solution Architecture
2. Documented Recommendations for an ML Target Operating Model
3. MLOps High level Implementation Roadmap and inputs into a Business Case aligned with the business goals and priorities