MLOPS : 6-Wk Production Implementation $50k

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