Cette formation vous permet de vous préparer à la certification de Microsoft pour acquérir une expertise technique dans l’application des approches de la Data Science et du Machine Learning pour implémenter et exécuter des charges de travail sur Microsoft Azure.
Cette formation vous prépare au rôle de Data Scientist avec les connaissances nécessaires et requises pour débuter dans un poste prenant en charge la création et la mise en œuvre de modèles de Machine Learning en apprentissage supervisé et non supervisé.
La manipulation du Cloud Azure vous permet aussi de prendre en charge la planification et la création d’un environnement de travail approprié pour les charges de travail de science des données sur Azure. Cela vous permet de devenir à l'aise avec les expériences de données et effectuer l’apprentissage de modèles prédictifs et aussi gérer, optimiser et déployer des modèles Machine Learning en production.
Module 1: Getting Started with Azure Machine Learning
In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.
Module 2: No-Code Machine Learning
This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.
Module 3: Running Experiments and Training Models
In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.
Module 4: Working with Data
Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
Module 5: Working with Compute
One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
Module 6: Orchestrating Operations with Pipelines
Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.
Module 7: Deploying and Consuming Models
Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
Module 8: Training Optimal Models
By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.
Module 9: Responsible Machine Learning
Data scientists have a duty to ensure they analyze data and train machine learning models responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine learning principles.
Module 10: Monitoring Models
After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.
Caractéristiques de l'examen:
Les candidats à cette certification doivent posséder des connaissances et de l’expérience minimales dans la science des données comprenant la maitrise des différents types d'apprentissages, les techniques de mise en place d'un pipeline Data science et l’utilisation d’Azure Machine Learning et d’Azure Databricks.
Les objectifs de la formations sont :
Cette formation se déroule selon les modalités suivantes:
Azure Machine Learning Studio
Automated Azure Machine Learning
Cette formation est destinée à:
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