Bringing ML to Maturity

As the possibilities of machine learning grow, enterprises are experiencing growing pains. ML Ops offers a way forward.

Machine learning is in its artisan phase. Data scientists lavish time and attention on models, then face a disjointed, IT-intensive effort to get them into production.

Enter ML Ops. Combining the tried and tested practices of DevOps with automation of the data pipeline, ML Ops provides reliable, repeatable frameworks and infrastructure for deploying ML models at scale.

With more time-saving automation and less human error, the appeal is obvious. But creating and maintaining ML Ops infrastructure requires holistic, wide-ranging change – not to mention new skills and knowledge – that can be hard to create from scratch. 

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Deploying MLOps at Speed

MLOps Pipeline Architecting and Building

ML Ops Pipeline Architecting and Building

The data and DevOps pipeline is the backbone of any ML solution, Pactera EDGE automates the pipeline from development to production, leveraging cloud environments and infrastructure as code.
MLOps Framework Deployment

ML Ops Framework Deployment

Like DevOps, ML Ops is about people and process, not just tech. Pactera EDGE deploys robust operational frameworks, training data scientists and ML engineers in the practices that power ML Ops. 
Productionalizing ML Models

Productionalizing ML Models

With a robust MLOps framework, ML models can be productionalized at a rapid pace, with new models brought into use faster than ever before.
Engineering Support

Engineering Support

Rely on Pactera EDGE to provide ongoing support to enable growing capabilities and continuing success.
Capability Co-Creation

Capability Co-Creation

When the time comes to add to your strengths in machine learning, partner with our seasoned enterprise AI/ML teams to co-create new capabilities.

Why Pactera EDGE?

Hands-on Experience

What others talk about, we’ve implemented in Fortune 500 settings, driving change and profitability.

Speed to Market

Drawing on our experience and infrastructure as code, we complete a typical project in 8-12 weeks.

Practical Consultation

An expert team combines mastery of craft with the ability to ask the right questions at the outset.
Vasudevan Sundarababu

ML Ops is DevOps for building and commercializing ML models.  It embodies a shift from an “ARTISAN-LIKE” model building and deployment to an institutionalized way of infusing Intelligence into the enterprise.

Vasudevan Sundarababu, SVP, Head of Digital Engineering Practice