Deploy a real-time inferencing model with AML Service, AKS & Container Instance

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 Service, 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/ [eventID:15732]