ML Operations with SageMaker

What is it?

Let your Data Scientists be Scientists and let automation do the rest

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. 

Let Amazon SageMaker’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.

Key Activities

01 — Discovery & Planning

Through a series of discovery workshops, we’ll review your current processes, technology landscape, and industry best practices for data engineering, model engineering, and runtime operations.

02 — Design & Implementation

Based on your input, we’ll design the architecture and process flows for operationalizing your AI models. With that plan, we’ll implement an end-to-end pipeline to manage versioning, deployment, and monitoring.

03 — Enablement

We will educate your team on using the MLOps pipeline for proper change management of AI models. This sets you up for increased productivity, repeatability, reliability, auditability, and quality monitoring.

Engagement Details

  • Accelerated model development and faster time to market
  • Built with AWS native services to ensure cost effectiveness: SageMaker Studio, Glue, KMS, ECR, S3 and more
  • Provides a foundation for your Gen AI initiatives
  • Leverage the Machine Learning Lens of AWS’ Well-Architected Framework
  • MLOps Pipeline within SageMaker for customer provided models
  • Containerization of existing pre- processing jobs and models as necessary
  • Automated workflows to manage new models from development into production

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