Shivam Sharma
TechScalable
LEARN, CONNECT, BUILD
Microsoft Reactor
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Ready to get started with AI and the latest technologies? Microsoft Reactor provides events, training, and community resources to help developers, entrepreneurs and startups build on AI technology and more. Join us!
LEARN, CONNECT, BUILD
Microsoft Reactor
Join Microsoft Reactor and engage with developers, entrepreneurs, and startups live
Ready to get started with AI and the latest technologies? Microsoft Reactor provides events, training, and community resources to help developers, entrepreneurs and startups build on AI technology and more. Join us!
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Build and Operate Machine Learning Solutions with Azure Databricks
29 March, 2022 | 12:30 PM - 1:30 PM (UTC) Coordinated Universal Time
- Format:
Livestream
Topic: Cloud Development
Language: English
Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this workshop, you'll learn how to use Azure Databricks to explore, prepare, and model data and integrate with Azure Machine Learning.
The session will focus on Azure services and related products like Azure Machine Learning Service,Azure Databricks,Spark ML & MLflow
You will automate the process of hyperparameter tuning to identify the best model more easily.
Further Learning : https://aka.ms/AzureDatabricks-LM-29/3
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/
Prerequisites
This session assumes that you have experience of using Python to explore data and train machine learning models with common open source frameworks, like Scikit-Learn, PyTorch, and TensorFlow. Consider completing the Create machine learning models learning path before starting this one.
Speakers