In machine learning, inferencing refers to the use of a trained model to predict labels for new data on which the model has not been trained. Often, the model is deployed as part of a service that enables applications to request immediate, or real-time, predictions for individual, or small numbers of data observations. In this session you will learn how to deploy a real time inferencing pipeline.

The session will focus on Azure services and related products like Azure Machine Learning Servic, Azure Machine Learning SDK,Azure Kubernetes Service &Azure Container Instance.

What will you learn from the session :

a) Deploy a model as a real-time inferencing service.
b) Consume a real-time inferencing service.
c) Troubleshoot service deployment

Further Learning : https://aka.ms/MachineLearningServices

Speaker : Shivam Sharma

Speaker BIO- Shivam is an author, cloud architect, speaker, and Co-Founder at TechScalable. Being passionate about ever evolving technology he works on Azure, GCP, Machine Learning, Kubernetes & DevOps. He is also a Microsoft Certified Trainer. He architects’ solutions on Cloud as well on-premises using wide array of platforms/technologies.

Social Handle

LinkedIn - https://www.linkedin.com/in/shivam-sharma-9828a536/
Twitter - https://twitter.com/ShivamSharma_TS
Facebook - https://www.facebook.com/TSshivamsharma/