Caylent Catalysts™

MLOps Strategy

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.​

What is it?

Quickly design the supporting ecosystem for model engineering that contributes to successful business outcomes when adopting machine learning.

The practice of MLOps recognizes the advancements that have been achieved in application development by aligning developers and operations teams (DevOps). It also acknowledges the reality that model engineering is one component within a complex ecosystem that is required to successfully apply machine learning to achieving business objectives.

Caylent's expertise with DevOps, data engineering, machine learning, and production operations combine to provide the multi-disciplinary breadth and depth necessary to successfully plan your MLOps strategy and help implement any or all of the individual components.

While MLOps has proven its worth for early adopters, experienced practitioners are still rare and difficult to attract. Creating a strategy based on our experience and unique to your team's needs, capabilities, and current state unlocks the next steps in tactical execution by offloading the infrastructure, data, operations, and automation work from data scientists.

Key Activities

01 — Discovery and 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

Based on your input, we’ll design data and process flows as well as architecture and implementation plans for infrastructure, data lake, feature store, data pipelines, analysis tools, model development environments, and monitoring.

03 — Implementation

Following our design collaboration, we'll summarize your data landscape, identify the top priority business cases to address, and craft an implementation plan to begin adopting MLOps.

Engagement Details

MLOps Strategy

  • Take advantage of AWS managed services in the Amazon SageMaker family and infrastructure innovations like Inferentia and Tranium to greatly reduce time to market and runtime costs
  • Leverage the AWS' Well-Architected Machine Learning Lens to define your MLOps strategy and roadmap with cloud and technology best practices
  • MLOps strategy, data engineering, model engineering, and operations workshops
  • Data landscape summary of data sources, transformations, complexity, stages, and destinations
  • Implementation plan with initial business cases and models
  • All diagrams and documentation developed during the engagement including source code, scripts, templates, and other technical artifacts

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