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.



  • Work with data
  • Build ML models
  • Use a variety of tools, languages, and platforms


  • Ensure production safety
  • Follow policy and processes
  • Operate and manage the solution


  • Want positive results
  • Wish to avoid biases
  • Need a trustworthy solution for their companies and customers

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

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

Create models with reusable ML pipelines, with code being automatically integrated into the MLOps pipeline
Automate MLOps rollout using services for DevOps and Machine Learning to manage data, version models, handle events, etc.
Create an automated audit trail to ensure that all artifacts in the MLOps pipeline can be checked for integrity and compliance
Deploy models and monitor their performance using dashboards and alerts, to know when to roll back and re-deploy models
Observe data concept drifts, and use collected feedback to retrain models and improve their performance

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