Building Your First Data Science Project in Microsoft Azure
Cloud platforms are where data science happens in production—and Azure is where enterprises are investing. This 85-minute course walks you through architecting and deploying a real data science project on Azure, moving beyond notebooks into deployable solutions.
AIU.ac Verdict: Ideal for data scientists ready to shift from local experimentation to cloud-native workflows, or engineers wanting to understand Azure’s ML capabilities. The course is practical but assumes basic data science familiarity; pure beginners may need foundational Python first.
What This Course Covers
You’ll work through the full project lifecycle: setting up Azure Machine Learning workspaces, preparing datasets in the cloud, training models at scale, and deploying them as consumable endpoints. Jared Rhodes guides you through real decisions—choosing compute resources, managing experiment tracking, and integrating with Azure’s broader ecosystem. Expect hands-on labs in Pluralsight’s sandbox environment, so you’re building actual infrastructure, not just watching demos.
The course emphasises practical patterns: containerising models, versioning datasets, and automating retraining pipelines. You’ll see how Azure’s managed services (AutoML, Designer, Notebooks) fit together, and when to use each. By the end, you’ll have deployed a working model and understand the operational mindset required for production data science.
Who Is This Course For?
Ideal for:
- Data scientists moving to cloud: You’ve built models locally but haven’t deployed to production. Azure’s ML platform is your next step, and this course compresses the learning curve.
- ML engineers or platform engineers: You’re supporting data teams on Azure and need hands-on familiarity with the toolchain, workflows, and deployment patterns.
- Enterprise data professionals: Your organisation uses Azure, and you need to understand how to operationalise models within that ecosystem rather than fighting it.
May not suit:
- Python/ML beginners: This assumes you understand model training and evaluation. Start with foundational data science courses first.
- AWS or GCP specialists: Azure-specific services won’t transfer directly. You’ll spend time learning Azure’s naming conventions and UI rather than deepening cloud ML expertise.
Frequently Asked Questions
How long does Building Your First Data Science Project in Microsoft Azure take?
1 hour 25 minutes of video content. Plan 2–3 hours total including hands-on lab work in the sandbox environment.
Do I need an Azure subscription?
No. Pluralsight provides sandbox labs, so you can complete the course without creating your own Azure account. However, having one lets you extend the project beyond the course.
What if I’ve never used Azure before?
The course assumes no prior Azure experience but does assume you understand data science fundamentals (train/test splits, model evaluation, etc.). Jared walks through the Azure-specific parts clearly.
Will this teach me AutoML, or is it code-first?
Both. You’ll see Azure’s low-code AutoML Designer and also work with Python notebooks. The course shows when each approach makes sense.
Course by Jared Rhodes on Pluralsight. Duration: 1h 25m. Last verified by AIU.ac: March 2026.
