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Azure

DP-100: Designing and Implementing a Data Science solution on Azure

Become an Azure Data Scientist in just 4 instructor-led days. This hands-on course teaches you to ingest, prepare, train, deploy and monitor machine-learning models at cloud scale – preparing you for the Microsoft Certified: Azure Data Scientist Associate (Exam DP-100).

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Why choose this course?

  • End-to-end coverage of Azure Machine Learning – workspaces, data stores, compute clusters, AutoML, MLflow tracking, pipelines and Responsible AI dashboards.
  • Builds real-world MLOps skills: CI/CD for models, online & batch endpoints, monitoring and automated retraining.
  • Maps directly to the Azure Data Scientist Associate certification – highly sought-after across industries.
  • Hybrid delivery lets you attend on-campus or virtually from anywhere in South Africa.
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This course is ideal for:

  • Data scientists or ML engineers with Python & ML framework experience (Scikit-Learn, PyTorch, TensorFlow).
  • Analytics or AI teams moving on-prem model workflows to Azure.
  • Developers who need to productionise ML models with MLOps best practices.
  • Anyone preparing for Exam DP-100 on their Microsoft certification journey.

Prerequisites

Ability to create Azure resources, explore data with Python, and train/validate models in common ML frameworks. Experience with containers is recommended; AI-900 or equivalent knowledge is helpful.

Course Content

  • Explore & Configure the Azure ML Workspace – create workspaces, manage resource authentication & networking, enable responsible AI settings
  • Manage Data & Compute Resources – datastores & data assets, compute clusters/instances, environments & dependencies
  • Experiment with Azure Machine Learning – notebooks, SDK & CLI workflows, Azure ML designer, job runs, run metrics
  • Optimize Model Training – AutoML, hyper-parameter sweeps, distributed training, caching & re-use of datasets
  • Manage & Review Models – MLflow tracking, model registry, versioning, Responsible AI dashboards & model evaluation
  • Build Reusable Pipelines – components, pipeline jobs, data/parameter passing, orchestration for reproducible ML ops
  • Deploy & Consume Models – real-time (online) & batch endpoints, managed vs Kubernetes targets, traffic splitting, monitoring & alerts
  • Implement MLOps Practices – CI/CD with Azure DevOps/GitHub Actions, automated retraining, model governance, rollback strategies
  • Develop Generative AI Apps in Azure – prompt engineering, fine-tuning small & large language models, grounding with Azure AI Search, safety & compliance features
  • Secure & Govern ML Workloads – network isolation, private link, encryption, managed identities, workspace role-based access
  • Capstone Project – end-to-end build: ingest data ➡ train & track model ➡ deploy endpoint ➡ monitor & automatically retrain

Hardware Requirements

Interested?

Enquire today and one of our consultants will be in touch.