Shivam Sharma
TechScalable
APPRENDRE, SE CONNECTER, CRÉER
Prêt à bien démarrer avec l’IA et les dernières technologies ? Microsoft Reactor fournit des événements, des formations et des ressources communautaires pour aider les développeurs, les entrepreneurs et les start-ups à se baser sur la technologie IA et bien plus encore. Rejoignez-nous !
APPRENDRE, SE CONNECTER, CRÉER
Prêt à bien démarrer avec l’IA et les dernières technologies ? Microsoft Reactor fournit des événements, des formations et des ressources communautaires pour aider les développeurs, les entrepreneurs et les start-ups à se baser sur la technologie IA et bien plus encore. Rejoignez-nous !
29 mars, 2022 | 12:30 PM - 1:30 PM (UTC) Temps universel coordonné
Thème: Développement du cloud
Langage: À l’aide de la langue anglaise
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/
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.
Intervenants