Machine Learning Operations (MLOps)
Deliver ML models from research to production faster and with minimal handoff at scale, to accelerate time-to-value for AI initiatives
The radical shift from
model building to real-world
ML consumption naturally
leads to MLOps
The market for AI is changing. The focus has shifted from companies that have the technical expertise to build models, to enterprises that use AI solutions powered by those models. Model development is no longer the key focus, but an integral part of the process of deployment, maintenance, and governance of models. MLOps is a practice that helps manage development, usage, operationalization, and deployment of models in production. It evolved to streamline the working relationship between data scientists who build models and IT Ops who operate solutions powered by these models, to drive AI initiatives.
What Is MLOps?
MLOps, or Machine Learning Operations, is a set of practices for smooth collaboration and communication between data scientists and operations professionals, to help manage the ML production lifecycle and enable businesses to run AI successfully.
Data
Scientists
- Work with data
- Build ML models
- Use a variety of tools, languages, and platforms
IT
Operations
- Ensure production safety
- Follow policy and processes
- Operate and manage the solution
Businesses
- Want positive results
- Wish to avoid biases
- Need a trustworthy solution for their companies and customers
Benefits
of MLOps
Rapid innovation
through robust ML
lifecycle management
Repeatable workflows
allow for automatic
streamlined changes
Reduced friction
between data science
and operations teams
Easy deployment
of ML models thanks
to automatic scaling
Better governance
and compliance through
model reproducibility
Improved model
monitoring to detect
anomalies
Enabling AI Adoption at Scale
MLOps unifies deployment, monitoring, management, and governance of AI solutions in production, enabling easy consumption of results by all stakeholders.
Model Development
ML innovation requires extensive experimentation with different features, algorithms, model types, parameters, and configurations. Every experiment must be tracked, and its results must be saved, to maintain reproducibility, maximize code reusability, and accelerate time to value.
Model Testing
Software testing is a streamlined process that has improved over the years. ML system testing is not that simple. Data must be properly checked and validated. Trained ML models need to be tested and their quality evaluated on a testing dataset. Finally, an ML model has to be validated before deployment.
Model Deployment
ML models cannot be treated like code by IT teams. Instead of deploying models as a service, they have to deploy a multi-step pipeline for automated model retraining and deployment. Every step completed by data scientists prior to deployment must be automated, to ensure efficiency of model training and validation.
Model Monitoring
Traditional software tools cannot track ML model behavior. Monitoring is key, since not only code but also constantly evolving data can affect models’ performance. Data statistics must be tracked, and performance must be monitored to ensure timely notifications and roll back, in case of quality deviations.
Model Management
ML models have a complex lifecycle. To maintain required performance and accuracy, they need to be frequently updated. Model management allows us to keep track of model use context, and to re-deploy models when they show signs of data drift, concept drift, or any other signs of performance deterioration.
Model Governance
Governance of ML models in production allows us to minimize risk, and ensures compliance with regulations. It also allows us to keep track of changes, making it easier to improve the models and the process of rapid, easily scalable deployment and management of ML applications in production environments.
How It Works
The Core of MLOps
and Reproducible
Experimentation Pipelines
The code of an ML model, ML pipeline, infrastructure, and dependencies (all of which are stored and versioned in Git), as well as a dataset from a centralized Feature Store, are compiled into the model by ML Orchestrator, to generate logs, metrics, alerts, and data for storage and analysis. Continuous Delivery and Experimentation workflows are dynamically integrated, allowing us to use the same pipeline to trigger experiments manually and automatically, triggered from Git hook & CI/CD tools for continuous integration and deployment.
We Follow
MLOps Best
Practices
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