AI for Professional developers: Artificial Intelligence for professional developers with Team Data Science Process, Azure Batch AI, Azure Container Services and 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 an 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

Information related to training

Soporte siempre a tu lado

Training support: Always by your side

Formación presencial y telepresencial

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Bonuses for companies