Machine Learning Model Lifecycle Orchestrator

Automates and enforces best practices for machine learning model productionization and maintenance.

The Cognizant Machine Learning Model Lifecycle Orchestrator solution provides streamlined and enforced architecture best practices for machine learning (ML) model productionization. This solution is an extendable framework that provides a standard interface for managing ML pipelines for AWS ML services and third-party services using a no code/low code approach ensuring continuous improvement across the ML model management lifecycle.
 
The Cognizant MLOps Model Lifecycle Orchestrator solution promotes collaboration between data scientists and IT operations professionals and streamlines the building, testing, deployment, and governance of AI/ML models.
 
The solution orchestrates the deployment & prediction processes with a serverless function, including a dynamic ML library & Code commit repository.

Key Solution Features and Functionality include:

  • MLops pipeline for deploying ML model to production at scale with Configuration file driven and server-less architecture.
  • CI/CD approach to automate the deployment of finalized training models from development to UAT, to Production, through to ongoing maintenance and continous improvement.
  • End-to-end integration of GitLab repository with MLOps pipeline and efficiently move the artifacts to production.
  • Model Monitoring for Data Drift with custom built drift comparison dashboard.
  • Continuous re-training of ML model through MLOps pipeline.
  • Extensive capture of logging and monitoring through the execution for MLOps pipeline.
  • Alerts for failure with error step and success email for all the steps. Provides model monitoring metrics for data drift and model health.
Cognizant

AWS Partner Network | Competency

Availability

Australia, Austria, Belgium, Brazil, Canada, Denmark, Finland, France, Germany, India, Ireland, Italy, Japan, Jordan, Luxembourg, Netherlands, New Zealand, Portugal, Poland, Saudi Arabia, Singapore, South Africa, Spain, Sweden, Switzerland, United Arab Emirates, United Kingdom, United States

Benefits

Reduce Time to Deploy Models

Reduced deployment of ML model from DS team development environment to production.

Automate Model Build & Deployment

Standardized end to end process, including automation for ML model build and deployment.

Model Monitoring

Calculate data drift and model health metrics to maintain highest level of model accuracy over time.

Lower Cost

Up to 35-40% productivity improvement and Up to 80% lower cost by year 3.

  • How it works
  • The Cognizant MLOps Model Lifecycle Orchestrator solution delivers a focused Machine Learning Life Cycle Management process that covers the effective orchestration and automation of the following capabilities:

    • Model Management
    • Model Deployment
    • Model Serving
    • Model Governance
    • Model Monitoring

    The Cognizant ML Model Lifecycle Orchestrator solution is a best in class end to end Model Lifecycle management for any ecosystem leveraging AWS platform and services. The solution benefits an organization in operationalizing and scaling machine learning models to drive business value.
     
    Cognizant ML Model Lifecycle Orchestrator is initiated by engaging with MLOPS Engineers to productionize models by publishing the requests through a no code web interface for Model Deployment, with an integrated orchestration flow for provisioning of infrastructure with detailed AWS architecture oriented towards security along with monitoring and tracing capabilities for all aspects of model lifecycle.

    The data scientist engages with the MLOPS platform to infer the Model performance and quality metrics from the Model Monitoring Dashboards with detailed insights in guiding necessary impact to business from the analytical model, and to evaluate changes required to Model Engineering over time.

    The model registry manages the versions of models, and the platform enables redeployment of new model versions published by the data scientists.

    The Cognizant's ML Model Lifecycle Orchestrator solution also provides business users who own the overall analytical use case to understand the business impact from model outcomes through Model Quality Dashboards and as well as cost impact to business by monitoring the isolated production ecosystem, which by itself is self-contained.

    The Cognizant solution leverages AWS MLOPS Workload Orchestrator, from gathering requirements, to productionizing a model, through to operationalizing it. The solution's capabilities are delivered through the following design considerations.

    • A no-code MLOPS accelerator built on Serverless architecture with minimal and on demand cost to the ecosystem.
    • Enforced Security best practices from AWS guidelines to each production ecosystem delivered through the MLOPS Accelerator.
    • Implemented on AWS PAAS native services without any dependency on COTS tool or licensing.
    • An Angular Web Interface to capture requests from MLOPS Engineers, and to deliver Deployment Status & operational insights.
    • Cognizant Team support to customize build and deployment of custom blueprints for complex model production scenario's.

    Deployment Strategy Cognizant MLOPS Platform will be deployed to an AWS Account using CloudFormation Template available in the Marketplace.
    The solutions deployment can take around 15 – 20 minutes, and the deployment would comprise of the following:

    • Cognizant Model Lifecycle Orchestrator solution
    • AWS MLOPS Workload Orchestrator
    • Model Package Group to maintain models in AWS Sagemaker Model Registry
    • Elastic Container Registry for Custom Algorithms

    Feature Set available - Model Deployment on AWS Sagemaker covering:

    • Realtime Inference Pipelines for built-in algorithms in Sagemaker and Custom Algorithms available in ECR.
    • Batch Inference Pipelines for built-in algorithms in Sagemaker and Custom Algorithms available in ECR.
    • Register Docker images for custom algorithms that can be used for model deployment on an Amazon SageMaker endpoint.
    • Automatically deploys a trained model and provides an inference endpoint.
      Register models in Amazon SageMaker Model Registry to deploy versioned models.
    • View Deployment Status across all production models, along with capability to redeploy models with recent version from Model Registry.
    • Govern models across organization with standard productionisation process, which demands a no code, minimal effort approach with best practices enforced for AI & ML services in AWS.
    • Monitors deployed machine learning models and detects any deviation in data quality and model quality.
    • Insight driven dashboards for Model Monitoring.
  • Key activities
  • 1) Model Management

    Centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of an AI/ML Model.

    2) Model Deployment

    One-touch deployment, Auto-scaling for pods, services, clusters, and right-sizing.

    3) Model Serving

    Validated models deployed to target environment to serve predictions.

    4) Model Governance

    Simplified view of inferencing decisions captured and availability of audit log of ML pipelines.

    5) Model Monitoring

    Visualization of model inferencing & audit logs, dynamic risk management, and automated error handling alerts.

  • Customer contribution
  • Current State Analysis

    Walk-through the current data and machine learning pipeline architecture & granular view of processes.

    Enterprise-Wide Accepted Tool Chain

    Provide details of enterprise-wide accepted tools.

    Business Motivator Goal

    Identify the business motivator to move towards MLOps.

    Access to Services and Tools

    Provide licenses and access to third party tools.

    Security Policies and Compliance

    Provide access to security policies, procedures, and compliance.

    Release and Infrastructure Capacity

    Provide guidance on release velocity, automated solutions for capacity planning, and setup environment.

    Security Policy Enforcement

    Convey organizationional security standards for solution alignment.

  • About this consultant
  • Cognizant works with global enterprises to build robust, modern, and secure digital platforms on the AWS Cloud, enabling them to accelerate innovation, scale business services, and improve operational agility. With corporate headquarters in the United States and offices across more than 40 countries worldwide, Cognizant's global presence extends its delivery capability and amplifies its impact. With approximately 300,000 employees globally with 100+ transformational blueprints, 4,500+ certified AWS professionals, 12,000+ trained AWS practitioners, and 300+ customers on AWS, Cognizant is ready to help with all your AWS Cloud needs.

    As a premier AWS Partner, Cognizant can advance your digital transformation journey by modernizing your core to promote innovation. Cognizant optimizes operations, drives efficiencies, unlocks new business opportunities. Cognizant is an AWS Premier Partner offering consulting services, an AWS Managed Services Provider Partner, and an AWS Well-Architected Partner. Cognizant has achieved the AWS SAP, AWS Migration, AWS Financial Services, AWS Healthcare, and AWS Life Sciences Competencies and the AWS CloudFormation Service Delivery designation, among others.

  • Architecture diagram

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AWS Partner Highlights

Cognizant’s AWS validated qualifications, customer references, and office locations.

AWS Machine Learning Competency

Cognizant has demonstrated deep AWS technical expertise and proven customer success.

AWS Marketplace Details

View and procure Cognizant's solution in AWS Marketplace.

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