AI Technology Focus: Machine Learning Operations (MLOps)

AI Technology Focus: Machine Learning Operations (MLOps)


For businesses leveraging machine learning (ML) to create innovative products and services, getting those ML models successfully deployed and working seamlessly for customers is often the biggest challenge. This critical “last mile” of ML delivery can be the bottleneck that stymies value from ever reaching end users.

That’s where Machine Learning Operations (MLOps) comes in. MLOps establishes proven practices and processes for deploying ML models into production reliably and efficiently. It brings operations discipline to a space that has often been adhoc and disjointed.

The Challenges of the Last Mile
After data scientists build and validate an ML model (which is non-trivial, experimental and must be managed), getting it working seamlessly in an application used by customers requires overcoming some major hurdles:

Transitioning the model from an experimental notebook to a production-grade application
Scaling computing resources to serve model predictions in real-time
Monitoring model performance and data drift over time
Managing testing, versioning, and redeployment of updated models
Ensuring secure access management and governance
This “Last Mile” deployment and operations is often manual, fragmented across teams, and a constant firefight. ML models may show amazing accuracy in the lab, but fail to provide value once deployed.

How MLOps Bridges the Last Mile Gap
MLOps systematizes and automates the processes around deploying, monitoring, and managing the lifecycle of ML models at scale. Key capabilities include:

Automated workflows for packaging, testing, and deploying models
Provisioning of scalable computing infrastructure on cloud/on-prem
Monitoring of model performance, data drift, and resource usage
Support for A/B tests, canary rollouts, versioning, and rollbacks
Centralized dashboards with full auditability and governance controls
With MLOps in place, ML teams can rapidly iterate on models and push updates with confidence. They get reproducibility, reliability, and agility to innovate. Organizations can finally cross the “Last Mile” to realize the transformative potential of ML for customers.

The Future is Automated ML Delivery
As ML becomes more critical for digital products and services, MLOps will be essential for any company seeking competitive advantages through machine learning and AI. Implementing MLOps best practices allows the rapid, secure, and scalable delivery of ML-powered software to customers.

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