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Implement MLOps to Accelerate Your Machine Learning Design-to-Production Journey

As more and more organizations adopt Artificial Intelligence (AI) and Machine Learning (ML), they find that along with seizing the significant opportunities created by these technologies, they need to manage what is perhaps their greatest challenge: scalability.

With the constant acceleration of change in data and analytics, organizations need more from their ML models than exciting use cases and cutting-edge innovation; they need the ability to automate and operationalize the production of AI solutions. Unfortunately, organizations increasingly struggle to turn ML capabilities into viable applications due to an inadequate model development layer.

In this whitepaper, we explore a framework that organizations can use to build a model development layer that makes the process of taking AI capabilities from design to delivering business outcomes far more efficient.

Read this whitepaper to understand:

  • The game-changing business outcomes made possible by ML
  • Common model development challenges faced by all organizations as they ramp their ML development efforts
  • How leading organizations use MLOps to overcome those challenges while streamlining and scaling the entire model development lifecycle
  • How LTI Mosaic AI provides an integrated, turn-key MLOps solution that can be deployed on any infrastructure

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    Meet the Author

    Shivanand Pawar

    Product Head for Mosaic AI

    Shivanand Pawar is Product Head for Mosaic AI, a proprietary AI Platform by LTI. With 10+ years of experience, he is a hands-on expert in Data Science, Big Data, Kubernetes, and Application development. Shiva is actively involved in consulting with clients across various domains in adopting Big data and Machine learning and operationalizing AI / ML at scale. He brings in immense knowledge around the challenges of machine learning adoption and how a systematic approach simplifies the journey & assures ROI.