AI for Professional developers: Inteligencia Artificial para desarrolladores profesionales con Team Data Science Process, Azure Batch AI, Azure Container Services y Azure Machine Learning Workbench



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we will focus on hands-on activities that develop proficiency in AI-oriented workflows leveraging Azure Machine Learning Workbench and Services, the Team Data Science Process, Visual Studio Team Services, Azure Batch AI, and Azure Container Services. These labs assume a introductory to intermediate knowledge of these services, and if this is not the case, then you should spend the time working through the pre-requisites.

About the course

- Understand and use the Team Data Science Process (TDSP) to clearly define business goals and success criteria

- Use a code-repository system with the Azure Machine Learning Workbench using the TDSP structure

- Create an example environment

- Use the TDSP and AMLS for data acquisition and understanding

- Use the TDSP and AMLS for creating an experiment with a model and evaluation of models

- Use the TDSP and AMLS for deployment

- Use the TDSP and AMLS for project close-out and customer acceptance

- Execute Data preparation workflows and train your models on remote Data Science Virtual Machines (with or without GPUs) and HDInsight Clusters running Spark

- Manage and compare models with Azure Machine Learning

- Explore hyper-parameters on Spark using Azure Machine Learning

- Leverage Batch AI training for parallel training on GPUs

- Deploy and Consume a scoring service on Azure Container Service

- Collect and Analyze data from a scoring service in production to progress the data science lifecycle.

Content sourced by Microsoft


Important! You must complete the following installations and requirements before attending the boot camp:


Introduction and Context
Lab 3.1: Introduction to Team Data Science Process with Azure Machine Learning
Lab 3.2: Comparing and Managing Models with Azure Machine Learning
 Lab 3.3: Deploying a data engineering or model training workflow to a remote execution environment
 Lab 3.4: Managing conda environments for Azure Machine Learning workflows
 Summary and White-board Discussion
Introduction and Context.
 Lab 4.1: Explore hyper-parameters on Spark using Azure Machine Learning
 Lab 4.2: Leverage Batch AI Training for parallel training on GPUs
 Lab 4.3: Deploying a scoring service to Azure Container Service
Lab 4.4: Consuming the final service
- 3:00-3:50: Lab 4.5: Collect and Analyzing Data from a scoring service

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