MLOps Consulting
Our MLOps team creates a self-service environment for data scientists and engineers to automate model/code development and deployment with AWS and GCP
Model Management
Model management tools and processes make it easier to govern your AI inventory. They provide the ability to: Catalog what models you own
Track when, where, how, and why models were updated
Provide lineage, in the form of training artifacts and parameters
Promote reproducibility, by being a centralized, trusted artifact store
Experiment Tracking
Experiment tracking is an essential tool to help data scientists be more productive. You’ll be able to:
Submit multiple jobs at the same time, increasing research throughput
Compare performance between runs
Time travel back to previous experiments
Unify how your data scientists instantiate pipelines
Scalable Pipelines
Scale your data ingestion or training pipelines to meet your demanding needs. With pipelines you can: Train massive models, using pipelines that scale up to meet demand, and down to save costs Make model training more repeatable and resilient Take advantage of modern computing hardware like GPUs and high memory instances Run your pipelines on-premise, in the cloud, or both!
Model Deployment
Packaging models can be tricky. But we’ve worked with range of solutions that are able to simplify the deployment process. In this phase you can:
Automatically deploy models into production, with minimal human intervention
Bake models into containers for ultimate provenance and scalability
Serve models in a variety of protocols, like REST or GRPC
Automatically scale up deployments to meet demand, or scale down to save costs
Monitoring & Alerting
Monitoring models and pipelines in production is essential to maintain availability of your services. With MLOps you can: Watch for concept or data drift, which could invalidate your models Provide analytics into the health of your AI system, with data healthchecks, deployment monitoring, user satisfaction and more Implement continuous learning, to automatically retrain your models via your pipelines to deploy new suitable models into production, automagically Alert before catastrophe, upon events like broken pipelines, endpoints, and data quality checks
Your MLOps Partner
We have helped companies as large as Shell, with hundreds of data scientists and ML Engineers to achieve scale for lifecycle management of ML models. Our flexibility allows us to collaborate closely with our clients