MLOps with SageMaker AI

Without automation, Data Scientists are stuck in operations instead of generating insights:

At Caylent, we recognize that the key to successfully using AI to transform and scale your business is automating the operational aspects through modern ML Operations (MLOps) workflows. MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the machine learning development lifecycle. Without this level of automation, Artificial Intelligence projects can be slow to market, cost-prohibitive, and resource-intensive.

Fast Track Your Time to Savings by Cutting Months Off Your Modernization:

Let Amazon SageMaker AI’s MLOps toolset help reduce your time-to-market, streamline administrative tasks, lower your operational costs, and free up valuable time for data scientists and engineers to focus on innovation and differentiation.

A Solution That Scales to Meet Customers Where They Are:

• Small: MLOps Starter Kit: The MLOps Starter Kit is designed for small Data Science teams managing just a few models. It helps you migrate existing models and data to Amazon SageMaker, enabling real-time endpoints or batch predictions deployed through infrastructure as code (IaC) and CI/CD pipelines.

• Medium: MLOps for Prod: MLOps for Prod is built for Data Science teams managing several high-usage production models. It delivers automated ML pipelines, supports promoting real-time endpoints across dev, test, and prod, and enables feature engineering automation with monitoring and drift detection.

• Large: MLOps for Scale: MLOps for Scale is designed for organizations with multiple Data Science teams managing a wide range of models. It is fully customizable to your unique needs, supports multi-region deployment for high availability, latency, or data residency, and provides the governance and leadership required for enterprise scale.

Our Approach:

• Discover & Planning: Through a series of discovery workshops, we review your current processes, technology landscape, and industry best practices for data engineering, model engineering, and runtime operations.
• Design & Implementation: With your input, we design the architecture and process flows for operationalizing your AI models. With that plan, we implement an end-to end pipeline to manage versioning, deployment, and monitoring.
• Launch & Enablement: We educate your team on using the MLOps pipeline for proper change management of AI models, setting you up for increased productivity, repeatability, reliability, auditability, and quality monitoring.