Productionzing ML Model Using MLflow Model Serving

May 27, 2021 11:00 AM (PT)

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Productionzing ML Models are needs to ensure model integrity while it efficiently replicate runtime environments across servers besides it keep track of how each of our models were created. It helps us better trace the root cause of changes and issues over time as we acquire new data and update our model. We have greater accountability over our models and the results they generate.

MLflow Model Serving delivers cost-effective and on-click deployment of model for real-time inferences. Also the Model Version deployed in the Model Serving can also be conveniently managed with MLflow Model Registry. We will going to cover following topics Deployment, Consumption and Monitoring. For deployment, we will demo the different version deployment and validate the deployment. For consumption, we demo connecting power bi and generate prediction report using ML Model deployed in MLflow serving. Lastly will wrap up with managing the MLflow serving like, access rights and monitoring capabilities.

In this session watch:
Nitin Raj Soundararajan, Senior Associate, Cognizant Worldwide Limited
Nagaraj Sengodan, Senior Technical Manager, HCL Technologies


Nitin Raj Soundararajan

Nitin Raj Soundararajan is a technical architect focusing on advanced data analytics, data engineering, cloud scale analytics and data science to solve real business problems in multiple domains. He ...
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Nagaraj Sengodan

Nagaraj Sengodan is a Senior Technical Manager in Data and Analytics Practice at HCL Technologies, where he brings over 15 years of industry experience in data engineering and analytics. He has arc...
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