Kursus
DP-100
Designing and Implementing a Data Science Solution on Azure
Lær at udvikle, træne, optimere og implementere machine learning-modeller i Azure Machine Learning. Få hands-on erfaring med dataforberedelse, AutoML, MLOps og model deployment i cloud-miljøet.
Hillerød
Januar
6/1
Uge 2
6-9/1 2026
Februar
Uge
X
Marts
Uge
X
Aarhus
26/1
Uge 5
26-29/1 2026
Uge
X
Uge
X
VideoLink
6/1
Uge 2
6-9/1 2026
26/1
Uge 5
26-29/1 2026
Uge
X
Uge
X
Hillerød
Januar
6/1
Uge 2
6-9/1 2026
Februar
Uge
X
Marts
Uge
X
April
7/4
Uge 15
7-10/4 2026
Aarhus
26/1
Uge 5
26-29/1 2026
Uge
X
Uge
X
Uge
X
VideoLink
6/1
Uge 2
6-9/1 2026
26/1
Uge 5
26-29/1 2026
Uge
X
Uge
X
7/4
Uge 15
7-10/4 2026
Hillerød
Januar
6/1
Uge 2
6-9/1 2026
Februar
Uge
X
Marts
Uge
X
April
7/4
Uge 15
7-10/4 2026
Maj
Uge
X
Juni
29/6
Uge 27
29/6
2/7 2026
Juli
Uge
X
August
Uge
X
Aarhus
26/1
Uge 5
26-29/1 2026
Uge
X
Uge
X
Uge
X
26/5
Uge 22
26-29/5 2026
Uge
X
Uge
X
Uge
X
VideoLink
6/1
Uge 2
6-9/1 2026
26/1
Uge 5
26-29/1 2026
Uge
X
Uge
X
7/4
Uge 15
7-10/4 2026
26/5
Uge 22
26-29/5 2026
29/6
Uge 27
29/6
2/7 2026
Uge
X
Uge
X
Datoerne er startdatoer
Mulighed for virtual deltagelse
Afholdelsesgaranti
Beskrivelse
Dette kursus giver dig den nødvendige viden og praktiske erfaring til at arbejde professionelt med machine learning i Azure Machine Learning (Azure ML). Gennem en række praksisnære moduler lærer du at opsætte et Azure ML workspace, forberede data, udvikle modeller og automatisere processen med AutoML og hyperparameter tuning.
Du arbejder med hele den livscyklus, en datavidenskabelig løsning kræver – fra data ingestion og modeludvikling til deployment og MLOps. Du lærer at deploye modeller til real-time og batch endpoints, overvåge performance og sikre en stabil drift med moderne pipeline-strukturer og CI/CD-principper.
Kurset dækker også centrale aspekter af Responsible AI, herunder bias detection, fairness, model explainability og brugen af det visuelle Responsible AI dashboard. Det giver dig et stærkt udgangspunkt for at bygge gennemsigtige, pålidelige og etisk forsvarlige AI-løsninger i et produktionsklart miljø.
I den nyeste del af kurset introduceres du til generative AI i Azure AI Foundry – her arbejder du med prompt flow, RAG (Retrieval-Augmented Generation) og fine-tuning af language models.
Det arbejder vi med på kurset:
- Opsætning og konfiguration af Azure ML workspaces, compute targets og environments
- Dataforberedelse og data asset management i Azure ML
- Modeltræning i notebooks og scripts med MLflow og command jobs
- Brug af AutoML til at finde de bedste klassifikationsmodeller
- Kørsel og styring af træningsforløb via pipelines og komponenter
- Hyperparameter tuning for performanceforbedring
- Modelregistrering og governance med MLflow tracking og model registry
- Deployment til managed online og batch endpoints
- Introduktion til generative AI med Azure AI Foundry: prompt flow, RAG-agenter og evaluering
- Udvikling af ansvarlige modeller med Responsible AI tools og dashboards
Certificeringspakker
Listepris 42.400 DKK
Din pris 37.800 DKK
(Eksl. moms)
Moduloversigt
- Modul 1Explore 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 2Experiment 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 3Optimize 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 4Manage 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 5Deploy 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
- Modul 6Develop generative AI apps in Azure
Generative Artificial Intelligence (AI) is becoming more accessible through comprehensive development platforms like Azure AI Foundry. Learn how to build generative AI applications that use language models to chat with your users.
Lessons:
- Plan and prepare to develop AI solutions on Azure
- Choose and deploy models from the model catalog in Azure AI Foundry portal
- Develop an AI app with the Azure AI Foundry SDK
- Get started with prompt flow to develop language model apps in the Azure AI Foundry
- Develop a RAG-based solution with your own data using Azure AI Foundry
- Fine-tune a language model with Azure AI Foundry
- Implement a responsible generative AI solution in Azure AI Foundry
- Evaluate generative AI performance in Azure AI Foundry portal
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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!