Dev Ops and ML Ops | Tiger Analytics

Seamless model and app deployment, scaling, and maintenance

Faster development cycles, improved performance of applications and models, and cost-efficiency are all essential to any AI-driven enterprise. To achieve this, organizations should have robust processes supported by the right choice of tools and capable people.

Tiger Analytics’ Dev Ops and ML Ops offerings follow software engineering best practices to help build, test, deploy, and manage data products, ML models, and applications consistently to drive innovation and growth.

What we do

DevOps as a service

Tiger Analytics’ Dev Ops-as-a-Service offerings help in the rapid on-boarding of applications and infrastructure by automating end-to-end delivery pipeline and facilitating continuous integration and development across… leading cloud platforms.
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MLOps strategy

Our expertise in shaping ML Ops strategy for clients help you pick the right technology platform, set up the right processes, and structure the teams right to ensure faster time-to-market on your AI model deployments, and… ongoing maintenance.
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MLOps services

Our ML Ops services help operationalize ML solutions through efficient deployment and monitoring. Our ML Ops team is equipped with expertise, delivering speedy ML solution deployment from development to production in… a short span of time.
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ML engineering

Our ML engineering offerings help businesses build AI/ML solutions to production standards. Our ML applications help deliver enterprise-level ML solutions to business users, leveraging fully-integrated, custom-designed, GUI-based… applications, our ML apps enable end-users to experience the transformative potential of AI and ML initiatives.
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