MLops

Machine Learning Operations, is the practice of managing and automating the entire machine learning lifecycle, from data preparation and model training to deployment and monitoring. Our expertise in MLOps ensures efficient and scalable deployment of machine learning models, enabling businesses to derive valuable insights and make data-driven decisions.

Enhance model optimization, track and trace costs, optimize resource utilization, and seamlessly integrate with the latest ML tools. With robust features for version control, collaboration, and real-time monitoring, this solution empowers data scientists to accelerate model performance, maximize efficiency, and leverage cutting-edge ML capabilities effortlessly.

Below are the technical capabilities broken down into capabilities:

ML Pipeline Development

Orchestrating ML training and prediction workflows, coordinating the different stages (e.g. data preparation, model training, evaluation, etc.) — and making it easy to automate deployment of repeatable pipelines.

 

Experiment & Metadata Tracking

Track ML experiments to collect information about what data went into which model that produced what performance result, including model parameters and artifacts.

 

Model Deployment

Enable packaging and deploying trained model to a target serving environment.

 

Automation

Extends traditional CI/CD to support Continuous Training (CT) in addition to automating the build, test, and release of ML pipelines.

 

Model Serving

Allows the deployed model to accept prediction requests for inferencing by taking input data and providing responses with predicted results.

 

Monitoring

Enables the tracking and reporting of deployed model in production to identify performance degradation and inform further actions.