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Driving success with real-time data

Cognizant & AWS Machine Learning (ML)/MLOps extract real-time data and deliver compelling insights that improve decision making, increase competitive edge and enhance customer experience.
Realizing the full benefits of ML and MLOps requires not only a cost-effective, scalable infrastructure, but also the ability to manage the entire lifecycle of machine learning through proven best practices. Successfully implementing both of these requirements enables you to deploy machine learning quickly and generate sustainable ML and MLOps integrated models that can be orchestrated and governed effectively.  
Our MLOps practices increase automation and ensure the best quality of production models running on AWS ML infrastructure, while also focusing on business and regulatory requirements which alleviate complexities and increase efficiencies. 

Offerings

Maximize ML insights on AWS

As an AWS partner with deep industry and technical expertise, Cognizant offers the knowledge, frameworks, accelerators and support to ensure that our ML and MLOps solutions provide maximum value to every developer, data scientist and practitioner.

Cognizant delivers ML/MLOps services through 23,000 data, analytics and AI consultants, 7000 specialists, 800 data scientists, unique AI/ML patents and hundreds of ML engineers. 

Identify use cases and assess data readiness

Although customers are expected to invest more than $77B in ML by 2022, only 10% of models are actually deployed and only 7% of enterprises can scale models across their businesses. As a result, business and IT leaders are attempting to embrace machine learning, value delivery and how best to scale technologies to realize the full potential of benefits.

Cognizant advisory services and AI Maturity Assessment Framework maintain the prioritization of intelligent business-driven outcomes and the deployment of AWS-powered models beyond the test lab, ensuring that intelligence is scaled and embedded throughout the organization effectively.

Peapare, calculate and promote

Cognizant’s set of MLOps engineering, maintenance and management practices deploy and maintain machine learning models in production reliably and efficiently. We test and develop machine learning models in isolated experimental systems until algorithms are ready to be transitioned to production environments. Backed by Cognizant’s unique assets—Quick Start Model Builder, LEAFTM, RAMP, the RO “AI” Calculator and Insights Marketplace—we support projects throughout the complete engineering lifecycle.

  • Go beyond predictions with LEAF, an evolutionary approach to analyze and improve models over time. LEAF enables the creation of new learning models using past conclusions and outcomes, new prescriptions for decision making can be applied to new contexts, and future model generations can achieve multiple objectives.
  • Rapid Analytics & ML Platform (RAMP) helps customers rapidly apply predictive analytics to their data. This platform shortens the time needed to deploy a data discovery environment, while protecting and securing the data being onboarded even in high volumes. And RAMP is a cloud-ready platform certified to operate in the AWS Cloud.
  • The RO “AI” calculator makes it possible to calculate the impact of machine learning models as well as promote sharing and collaboration across the AWS intelligence landscape.

Build, integrate & govern

The continuous deployment of machine learning models naturally results in continuous integration challenges. As the number of models deployed grows, so does the need to support the daily growing volume of models while simultaneously keeping prediction services highly available. ML conditions such as model loading and downloading and traffic pattern restarts to older models, must coexist alongside governance and ethical requirements.  

Cognizant’s MLOps Accelerator is a persona-driven, UI-based tool designed to facilitate the industrialization of models with ease, covering all aspects of model lifecycle management. 

  • Rapidly build, train, monitor and deploy models at scale on optimal AWS infrastructure and SageMaker.
  • Deliver explainable and zero-biased predictions, while ensuring repeatable and consistent performance.
  • Take advantage of comprehensive monitoring and automated retaining capabilities.
  • Provide all users—data scientists, business users, ML engineers and data engineers—with persona-driven dashboards and metrics.
Machine learning as a service (MLaaS) with automated feature engineering

Learn more about our approach to machine learning projects that solve the challenges associated with continuously evolving data, the convolution of both global and local data, and the sharing of data features across numerous models and lines of business. The illustration on page 4 features a model lifecycle applied to an AWS SageMaker project.

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