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Cursus Designing and Implementing a Data Science Solution on Azure
Cursus Designing and Implementing a Data Science Solution on Azure
Leer hoe je machine learning-oplossingen op cloudschaal bedient met behulp van Azure Machine Learning. Deze cursus leert je om je bestaande kennis van Python en machine learning te gebruiken om de opname en voorbereiding van gegevens, modeltraining en -implementatie en monitoring van machine learning-oplossingen in Microsoft Azure te beheren.
Inhoud
Module 1: Introduction to 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.
Lessons
- Getting Started with Azure Machine Learning
- Azure Machine Learning Tools
- Lab : Creating an Azure Machine Learning Workspace
- Lab : Working with Azure Machine Learning Tools
Module 2: No-Code Machine Learning with Designer This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.
Lessons
- Training Models with Designer
- Publishing Models with Designer
- Lab : Creating a Training Pipeline with the Azure ML Designer
- Lab : Deploying a Service with the Azure ML Designer
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.
Lessons
- Introduction to Experiments
- Training and Registering Models
- Lab : Running Experiments
- Lab : Training and Registering 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.
Lessons
- Working with Datastores
- Working with Datasets
- Lab : Working with Datastores
- Lab : Working with Datasets
Module 5: Compute Contexts 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
- Working with Environments
- Working with Compute Targets
- Lab : Working with Environments
- Lab : Working with Compute Targets
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.
Lessons
- Introduction to Pipelines
- Publishing and Running Pipelines
- Lab : Creating a Pipeline
- Lab : Publishing a Pipeline
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.
Lessons
- Real-time Inferencing
- Batch Inferencing
- Lab : Creating a Real-time Inferencing Service
- Lab : Creating a Batch Inferencing Service
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.
Lessons
- Hyperparameter Tuning
- Automated Machine Learning
- Lab : Tuning Hyperparameters
- Lab : Using Automated Machine Learning
Module 9: Interpreting Models Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.
Lessons
- Introduction to Model Interpretation
- using Model Explainers
- Lab : Reviewing Automated Machine Learning Explanations
- Lab : Interpreting Models
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.
Lessons
- Monitoring Models with Application Insights
- Monitoring Data Drift
- Lab : Monitoring a Model with Application Insights
- Lab : Monitoring Data Drift
Trainingsmateriaal
Voor het volgen van deze training ontvang je officieel Microsoft studie en oefenmateriaal. Je ontvangt het e-book dat dienst doet als naslagwerk voor het praktisch uitvoeren van opdrachten in een online leeromgeving. Je krijgt een half jaar toegang tot deze leeromgeving waarvoor je geen software nodig hebt, alleen een computer met internetverbinding. In de online labs werk je door scenario’s waarbij je het geleerde direct in de praktijk kunt toepassen. Door middel van een oefenexamen kun je je goed voorbereiden op het bijbehorende Microsoft examen. Dit oefenexamen volg je ook online. Het bestaat uit vragen vergelijkbaar met de vragen die je op het daadwerkelijke examen kunt verwachten en geeft gedetailleerde uitleg bij zowel correcte als incorrecte antwoorden. De training is volledig Engelstalig en zal in ongeveer 3 dagen af te ronden zijn.
Certificaat
Na afronding van de training ontvang je van Learnit een certificaat. Indien je ook het bijbehorende examen DP-100 boekt en voltooid, krijg je ook een officieel certificaat van Microsoft.
Voorkennis
This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Azure Fundamentals (AZ-900) is niet vereist mist je bovenstaande voorkennis hebt.Opmerking
Tijdens deze e-learning krijg je 6 maanden toegang tot de online leeromgeving waarin je de stof toepast op de praktijk. Als je de training volgt om je te certificeren, kun je bij ons ook direct examens en vervolgtrainingen bijboeken. Bij het bundelen van trainingen geniet je van een bundelkorting, kijk hiervoor op onze Microsoft certificeringspagina. De kosten van een examen bedragen €175 euro (excl. btw). Je kunt het examen online inplannen en volgen op een voor jou geschikt moment. Tijdens het examen word je door een proctor gemonitord via webcam en microfoon. Indien je dit examen wilt volgen kun je dit aangeven in het opmerkingenveld op het inschrijfformulier.
Alle voordelen op een rij:
- 6 maanden toegang tot de leeromgeving
- Een oefentoets
- Voorbereiding op Microsoft Examen DP-100
- Onderdeel van de Designing and Implementing a Data Science Solution on Azure certificering
- Het officiële Microsoft studiemateriaal
- Mogelijkheid om examens en vervolgtrainingen erbij te boeken
Investering
E-learning, per persoon:
Prijs (excl. btw)
€ 600,-
btw 21%
€ 126,-
Totaal incl. btw
€ 726,-