Leverage Amazon SageMaker AI's MLOps toolset to 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.
Caylent Catalysts™
Plan and implement an MLOps strategy unique to your team's needs, capabilities, and current state, unlocking the next steps in tactical execution by offloading the infrastructure, data, operations, and automation work from data scientists.
Caylent Catalysts™
Rapidly implement a foundational low-code data lake with Caylent's data engineering experts who will also enable your teams for no-code exploratory data analysis.
Caylent Catalysts™
From implementing data lakes & migrating off commercial databases to optimizing data flows between systems, turn your data into insights with AWS cloud native data services.
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 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.
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.
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.
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.
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