DevOps School devops-school

Hands-On Lab: Dependency Management with Go Modules

Goal: Experience the complete dependency-management lifecycle in a real Go project: initialize a module, add dependencies, inspect go.mod and go.sum, pin and change versions, understand direct and indirect dependencies, clean the module graph, verify downloads, use the module cache, explore vendoring, and configure private-module behavior.

Lab Overview

By the end of this lab, you will be able to:

Explain the difference between a package, module, and dependency.
Create a new Go module with go mod init.

How to use this guide

Each topic explains What, Why, When, Where, and How.
Each topic has one complete, self-contained example.
Unless a topic says otherwise, save the example as main.go and run:

go run main.go

Note - You need to do the project without much trainer help.

I’ve packaged a ready-to-use beginner lab with working code and a simple README: [Download the starter project ZIP] - https://github.com/devopsschool-assignment-projects/datadog-starter-lab-python-app [View the README] - https://github.com/devopsschool-assignment-projects/datadog-starter-lab-python-app [View the Flask app]- https://github.com/devopsschool-assignment-projects/datadog-starter-lab-python-app

What students will build

Absolutely. Here is a notebook-first, fresh-workspace, self-contained Databricks AI/ML tutorial built around Serverless notebook compute. I based it on the current Databricks docs as of late March 2026, including serverless notebooks, the official ML quickstart, Unity Catalog model lifecycle, Model Serving, AI Playground, and the current retrieval-agent tutorial. The key fit for your setup is that serverless notebooks are the right place for Python, MLflow, training, and experiments, while sample data is already available in Databricks through samples and /databricks-datasets. ([Databricks Documentation][1])

What this tutorial will show

This lab lets a student do the full flow in one workspace: create a notebook, load built-in sample data, write Python, train a model, track experiments with MLflow, register the model in Unity Catalog, deploy it with Mosaic AI Model Serving, then move into the GenAI side with AI Playground and a Databricks-provided retrieval-agent notebook that is explicit

Yes. Here is the cleanest one-stop, fresh-workspace Databricks tutorial for what you want.

One important correction first: a Serverless Starter Warehouse alone cannot do the full AI/ML lifecycle. A notebook attached to a SQL warehouse can run only SQL and Markdown, not Python. So the right end-to-end design is:

Serverless Starter Warehouse for SQL exploration and validation
Serverless notebook compute for Python, MLflow, training, and experiments
Model Serving for deployment
AI Playground / Agent tooling for agent prototyping

That split matches the current Databricks product model. ([Databricks Documentation][1])

Databricks AI/ML End-to-End Lab

Fresh workspace, self-contained, step by step

This lab is designed for a fresh Databricks environment with no preexisting catalog, schema, table, or custom data. It uses only Databricks-provided sample data and an official Databricks agent notebook, so the student does not need to generate any sample dataset manually. Databricks provides sample data in the samples catalog and the /databricks-datasets directory, and its official retrieval-agent tutorial notebook is described as standalone and ready to run with no setup or data required. ([Databricks Documentation][1])

This tutorial intentionally uses two compute paths because that is the correct Databricks design today:

Serverless Starter Warehouse for SQL exploration
Serverless notebook compute for Python, MLflow, training, and experiments

0) Install PyTorch

Option A: pip (most common)

CPU-only:

1) Install TensorFlow inside Jupyter (macOS)

Run this in a notebook code cell:

%pip install --upgrade pip
%pip install "tensorflow>=2.15,<3"

	{
	"__inputs": [
	{
	"name": "DS_GRAPHITE",
	"label": "Graphite",
	"description": "Graphite datasource",
	"type": "datasource",
	"pluginId": "graphite",
	"pluginName": "Graphite"
	}

	You are a world-class AI product analyst + technical writer + SEO editor. Create a publish-ready, long-form blog post in Markdown about the AI tool category below. The content must be current, practical, and trustworthy.

	CURRENT DATE (set automatically if you can): [YYYY-MM-DD]
	AI CATEGORY (replace): [XXXXXXXXXXX] (example: “LLMOps Platforms”, “AI Code Assistants”, “RAG Frameworks”, “AI Governance Tools”)
	TARGET READER (optional): [e.g., CTO, AI engineer, IT manager, marketing lead]
	REGION (optional): [Global / US / EU / India / APAC]
	SEED TOOL LIST (optional): [Tool1, Tool2, ...] (If provided, prioritize these; fill remaining slots with best-known tools.)

	CRITICAL RULES (DO NOT BREAK)
	1) Output MUST be clean Markdown only (no HTML), ready to copy-paste into a blog CMS.