MLOps, or DevOps for machine learning, enables Data Science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine
MLOps empowers data scientists and app developers to help bring ML models to production. MLOps enables you to track / version / audit / certify / re-use every asset in your ML lifecycle and provides orchestration services to streamline managing this lifecycle.
Azure ML contains many assets management and orchestration services to help you manage the lifecycle of your model training & deployment workflows.
With Azure ML + Azure DevOps you can effectively and cohesively manage your datasets, experiments, models, and ML-infused applications.
Implementation of entire framework takes approximately 8week including technical documentation and training, we follow Agile sprint methodology to implement MLOPs in 4 sprints which includes Testing, QA and productization of MLOPs framework.
Features Of MLOPs : -
Training reproducibility with advanced tracking of datasets, code, experiments, and environments in a rich model registry.
Autoscaling, powerful managed compute, no-code deploy and tools for easy model training and deployment.
Efficient workflows with scheduling and management capabilities to build and deploy with continuous integration/continuous deployment (CI/CD).
Advanced capabilities to meet governance, control objectives, and promote model transparency and fairness.
Build reproducible workflows and models
Reduce variations in model iterations and provide fault tolerance for enterprise-grade scenarios through reproducible training and models. Use datasets and rich model registries to track assets. Enable enhanced traceability with tracking for code, data, and metrics in run history. Build machine learning pipelines to design, deploy, and manage reproducible model workflows for consistent model delivery.
Easily deploy highly accurate models anywhere
Deploy rapidly with confidence. Use autoscaling, managed CPU, and GPU clusters with distributed training in the cloud. Package models quickly and ensure high quality at every step using model profiling and validation tools. Use controlled rollout to promote models into production.
Efficiently manage the entire machine learning lifecycle
Use built-in integration with Azure DevOps and GitHub Actions for seamlessly scheduling, managing, and automating workflows. Optimise model training and deployment pipelines, build for CI/CD to facilitate retraining, and easily fit machine learning into your existing release processes. Use advanced data-drift analysis to improve model performance over time.
Achieve governance across assets
Track model version history and lineage for auditability. Set compute quotas on resources and apply policies to ensure adherence to security, privacy, and compliance standards. Build audit trails to meet regulatory requirements as you tag machine learning assets, and automatically track experiments for CI/CD. Use the advanced capabilities to meet governance and control objectives and to promote model transparency and fairness.
Benefit from interoperability with MLFlow
Build flexible and more secure end-to-end machine learning workflows using MLflow and Azure Machine Learning. Seamlessly scale your existing workloads from local execution to the intelligent cloud and edge. Store your MLflow experiments, run metrics, parameters, and model artefacts in the centralised Azure Machine Learning workspace.
Key Challenges solved by MLOPs.
Model reproducibility & versioning
Track, snapshot & manage assets used to create the model
Enable collaboration and sharing of ML pipelines
Model auditability & explainability
Maintain asset integrity & persist access control logs
Certify model behaviour meets regulatory & adversarial standards
Model packaging & validation
Support model portability across a variety of platforms
Certify model performance meets functional and latency requirements
Model deployment & monitoring
Release models with confidence
Monitor & know when to retrain by analysing signals such as data drift