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In dit zelfstudiepakket zit het officiële cursusmateriaal, een labomgeving, MeasureUp-oefenexamen en een Microsoft-examenvoucher. Je hebt 180 dagen toegang tot de labomgeving.
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Pakket aanschaffenIn dit zelfstudiepakket zit het officiële cursusmateriaal, een labomgeving, MeasureUp-oefenexamen en een Microsoft-examenvoucher. Je hebt 180 dagen toegang tot de labomgeving.
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.
Deze training is bedoeld voor data scientists met bestaande kennis van Python en machine learning frameworks zoals Scikit-Learn, PyTorch, en Tensorflow, die machine learning toepassingen willen bouwen en beheren in de cloud.
Succesvolle Azure Data Scientists starten in deze rol met fundamentele kennis van cloudconcepten, en algemene ervaring met data science en machine learning tools en technieken.
Specifiek:
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.
Lessons
Lab : Create an Azure Machine Learning Workspace
After completing this module, you will be able to
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.
Lessons
Lab : Use Automated Machine Learning
Lab : Use Azure Machine Learning Designer
After completing this module, you will be able to
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.
Lessons
Lab : Train Models
Lab : Run Experiments
After completing this module, you will be able to
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.
Lessons
Lab : Work with Data
After completing this module, you will be able to
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.
Lessons
Lab : Work with Compute
After completing this module, you will be able to
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.
Lessons
Lab : Create a Pipeline
After completing this module, you will be able to
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.
Lessons
Lab : Create a Real-time Inferencing Service
Lab : Create a Batch Inferencing Service
After completing this module, you will be able to
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.
Lessons
Lab : Use Automated Machine Learning from the SDK
Lab : Tune Hyperparameters
After completing this module, you will be able to
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.
Lessons
Lab : Explore Differential provacy
Lab : Interpret Models
Lab : Detect and Mitigate Unfairness
After completing this module, you will be able to
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.
Lessons
Lab : Monitor Data Drift
Lab : Monitor a Model with Application Insights
After completing this module, you will be able to
Hieronder is een overzicht te vinden van trainingsmogelijkheden voor de Designing and Implementing a Data Science Solution on Azure (DP-100) Zelfstudiepakket training, met zowel klassikale als virtuele trainingen. Selecteer de best passende optie en start jouw reis naar succes.
Het DP-100 zelfstudiepakket biedt hands-on labs en real-world scenario’s die je voorbereiden op het toepassen van machine learning oplossingen in echte bedrijfsomgevingen. Hierdoor kun je de geleerde concepten direct toepassen op je werk.
Het DP-100 zelfstudiepakket biedt een diepgaande kennis van Azure Machine Learning, wat je voorbereidt op meer geavanceerde certificeringen zoals Azure AI Engineer Associate (AI-102) en Azure Solutions Architect Expert (AZ-303 en AZ-304).
Het DP-100 zelfstudiepakket behandelt tools en technologieën zoals Azure Machine Learning, Azure Databricks, Azure Synapse Analytics, en andere Azure AI-services.
Vul hieronder jouw gegevens in, zodat wij telefonisch contact met je kunnen opnemen.