Designing and Implementing a Data Science Solution on Azure
Beskrivelse
På kurset vil du lære, hvordan man stiller et Azure Machine Learning udviklingsmiljø til rådighed. Herunder får du viden og praktisk erfaring med relevante værktøjer, du skal kende for at arbejde effektivt med Azure Machine Learning.
Kurset giver et overblik over de services i Azure, som understøtter Data Science. Her præsenteres Data Science Service og Machine Learning Service, som benyttes til automatisering af Data Science Pipelines.
Du lærer at træne og eksperimentere med Machine Learning models i Azure samt at deploye pipeline services til automatisering af Machine Learning workflows.
I den forbindelse får du en indføring i, hvordan Automated Machine Learning samt hyperparameter tuning igennem cloud-scale computing kan assistere til at finde den optimale datamodel for dine data.
Efterfølgende vil du tilegne dig redskaber og mindset til at vurdere en Machine Learning model, herunder forstå de parametre den kunstige intelligens baserer sine modeller på.
Certificeringspakker
Moduloversigt
- Modul 1Design a machine learning solution
There are many options on Azure to train and consume machine learning models. Which service best fits your scenario can depend on a myriad of factors. Learn how to identify important requirements and when to use which service when you want to use machine learning models.
Lessons:
- Design a data ingestion strategy for machine learning projects
- Design a machine learning model training solution
- Design a model deployment solution
- Design a machine learning operations solution
- Modul 2Explore and configure the Azure Machine Learning workspace
Throughout this learning path you explore and configure the Azure Machine Learning workspace. Learn how you can create a workspace and what you can do with it. Explore the various developer tools you can use to interact with the workspace. Configure the workspace for machine learning workloads by creating data assets and compute resources.
Lessons:
- Explore Azure Machine Learning workspace resources and assets
- Explore developer tools for workspace interaction
- Make data available in Azure Machine Learning
- Work with compute targets in Azure Machine Learning
- Work with environments in Azure Machine Learning
- Modul 3Experiment with Azure Machine Learning
Learn how to find the best model with automated machine learning (AutoML) and by experimenting in notebooks.
Lessons:
- Find the best classification model with Automated Machine Learning
- Track model training in Jupyter notebooks with MLflow
- Modul 4Optimize model training with Azure Machine Learning
Learn how to optimize model training in Azure Machine Learning by using scripts, jobs, components and pipelines.
Lessons:
- Run a training script as a command job in Azure Machine Learning
- Track model training with MLflow in jobs
- Perform hyperparameter tuning with Azure Machine Learning
- Run pipelines in Azure Machine Learning
- Modul 5Manage and review models in Azure Machine Learning
Learn how to manage and review models in Azure Machine Learning by using MLflow to store your model files and using responsible AI features to evaluate your models.
Lessons:
- Register an MLflow model in Azure Machine Learning
- Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
- Modul 6Deploy and consume models with Azure Machine Learning
Learn how to deploy a model to an endpoint. When you deploy a model, you can get real-time or batch predictions by calling the endpoint.
Lessons:
- Deploy a model to a managed online endpoint
- Deploy a model to a batch endpoint
Er du i tvivl?
Det ligger os meget på sinde, at du finder det kursusforløb, der skaber størst værdi for dig og din arbejdsplads. Tag fat i vores kursusrådgivere, de sidder klar til at hjælpe dig!