El camino a las Cloud Native Apps - Azure AI
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This document discusses different Azure AI services: - Cognitive Services which provide pre-built machine learning algorithms to solve AI problems with little development needed. It highlights Computer Vision, Text Analytics, and other services. - Azure Databricks which is an Apache Spark-based analytics platform optimized for Azure and designed for collaboration between data teams. It emphasizes easy infrastructure for big data and full Azure connectivity. - Azure ML Workspace which is a tool to ease the entire machine learning process with experiment tracking, model versioning, predictive image creation and deployment.
El camino a las Cloud Native Apps - Azure AI
- 1. Azure AI
- 5. Agenda Introduction: AI, ML, DL & Azure Cognitive Services • Catalog • Demo • Real case Azure Databricks • Main features • Demo Azure ML Workspace • Introduction • Portal • Demo
- 6. What are we going to talk about today? Artificial Inteligence is more than an experiment. Introduction – AI, ML & DL
- 7. Integration with the rest of the portfolio and growing Different projects different solutions Introduction - Azure
- 8. Cognitive Services are a set of machine learning algorithms that Microsoft has developed to solve problems in the field of Artificial Intelligence (AI). When to use Cognitive Services? Whenever the project can be solved using one of these solutions. Very good results in exchange of very little development Cognitive Services
- 10. Different tasks related with image analysis. Such as: • Objects and people detection • Image tagging • Content moderator • Faces, age and emotions recognition Try it out! Computer Vision
- 11. Text Analytics A wide rango of tasks related with text analysis, such as: sentiment analysis, language detection, keywords, entity recognition… Try it out!
- 12. • Cognitive Search • LUIS (Language Understanding) • Anomaly detector • Custom Vision Other Services
- 13. Real Case - JFK Files JFK Files takes complex files including photos, handwriting, government documents, and more—then uses Cognitive Search to extract information.
- 14. -Text Analytics API Cognitive Services Demo
- 15. Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Designed with the founders of Apache Spark, Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. Azure Databricks
- 16. Main features: - Easy infrastructure for Big Data - Collaboration - Automated Jobs - Fully connected with Azure When to use Azure Databricks? Big Data projects, Experiments, Pipelines Azure Databricks
- 17. -ETL + job -NLP Model -AutoML Azure Databricks Demo
- 18. Azure ML workspace is a tool designed to ease the whole process of a ML/DL Project. When to use Azure ML Workspace? Practically always Azure Machine Learning Workspace
- 19. • Experiment tracking and remote execution on demand • Model version registry • Predictive Image creation and registry • Deployment of the image Azure Machine Learning Workspace main advantages
- 20. -Experiment -Predictive Image & Deploy -Pipeline Demo Azure ML Workspace