MLOps: Operationalize Your Machine Learning Models – PoC 4 Weeks

Enterprises that have seen initial success with Machine Learning often face challenges as they develop more models and want to replicate the success across other business functions. Scaling up ML across an enterprise needs to address collaboration, reusability, model management, monitoring, tuning, and deployment.

WinWire, with its expertise in helping enterprises adopt ML, has defined an offering WinMLOps, powered by Azure and Databricks services.

WinMLOps enables rapid adoption of MLOps at an optimized cost through pre-built code templates for monitoring, metrics dashboards, model patterns, and best practices. WinMLOps includes options of pickle, wheel file deployment based on Azure DevOps, MLflow, Databricks, and Azure ML with Python.

MLOps: Proof of Concept Methodology

• Scope – Understand the data landscape, environment, and ML models
• Architect – MLOps environment setup and validation
• Pilot - For the identified model, set up the process flow for data ingestion, model training, registration, deployment, monitoring, and feedback mechanism
• Demonstrate – Deploy the MLOps code and test run with the model, handover for validation, and develop a plan for further rollouts

Key deliverables

• Production-ready MLOps solution
• Assessment and review of the existing ML landscape
• Rollout plan and approach

Value delivered

• Improved collaboration between Business, IT, and Data Scientists with end-to-end model governance
• Increase in productionized ML use cases due to higher velocity in model development and automation
• Optimized integration with Azure services and cost management