Subhro

Introduction to Git — And Why It’s the First Thing You Should Learn in DevOps

Few years back, I was working with a small dev team. All the team members were great. But we had one problem — we didn’t use Git.

Everyone had their own version of the code. Files were flying around in emails, shared drives, even pen drives.

One day, two developers unknowingly changed the same file. One of them spent hours on a bug fix. The other rewrote that part of the code. When we tried to merge their changes, we lost both versions. It was messy, frustrating, and honestly — totally avoidable.

That’s when we switched to Git. And things changed. Forever.

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Azure AI Studio + Azure AI Search — Orchestrating Intelligence with Your Data

Demystifying Azure AI – Article 6 of 6

By now, you’ve seen how Azure helps you plug into prebuilt models (AI Services), harness the power of GPTs (OpenAI), build your own (AI Foundry), and train predictive models (Azure ML). But real-world apps are often more than just a single model.

You need to combine services.
You need to ground GPT with trusted knowledge.
You need to moderate, monitor, and improve — continuously.

That’s where Azure AI Studio and Azure AI Search step in — letting you orchestrate, evaluate, and ground intelligent applications with confidence.

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Azure Machine Learning — The Classical Workhorse

Demystifying Azure AI – Article 5 of 6

While the AI spotlight today is firmly on GPTs and image generators, most real-world AI applications still rely on traditional machine learning.

Whether it’s predicting customer churn, classifying support tickets, or detecting anomalies in time-series data, these are problems solved not by massive language models — but by tabular data, structured features, and well-tested algorithms.

That’s exactly where Azure Machine Learning shines.

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