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
nanforibericawe 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.
