F. The No-Code Toolkit: Getting Started
Use the paper-based route with a notebook and calculator, or add tools for the build-along activities. No-code analysis does not mean installation-free, offline, or suitable for every laptop. A shared teaching environment can help, but only use data approved for that environment.
Choose the smallest setup
| Activity | Build-along tool | What you must supply |
|---|---|---|
| Data exploration and model comparison, Chapters 4 and 10 | Orange Data Mining | Versioned customer data and a documented train/test split |
| Text analysis, Chapter 8 | Orange with relevant text components, or an approved model | Support text, labels, and a disagreement set |
| Image-classifier experiment, Chapter 9 | Teachable Machine | Your own harmless teaching images and separate test images |
| Retrieval comparison, Chapters 7 and 10 | Open WebUI and optionally NotebookLM | Approved source documents and fixed test questions |
| Local model experiment, Chapters 6–7 | Ollama and a compatible local model | Hardware capacity, model download, and setup time |
| Agent design, Chapter 11 | Paper workflow first; optional sandboxed implementation | Explicit permissions, mock inputs, and checkpoints |
Interfaces, access conditions, and hardware requirements change. Record the tool version or access date in your dossier. The chapter’s teaching result is not a benchmark guaranteed to reproduce on another model.
Orange: inspect a data workflow
Start from the official download page. Load an approved CSV into a File widget, connect a Data Table, and inspect column types and missing values before fitting anything. For a predictive task, identify the target and reserve test data before model selection. Keep the same split when comparing candidates.
The full external Tessera CSVs are not bundled. Use the small, explicitly synthetic lab-v1 files from Appendix G for the core exercises; keep external data as a separately versioned extension. Do not confuse either with observed customer outcomes.
Teachable Machine: preview before deployment
Use Teachable Machine for an image project. Train with your development images, then use the preview with separate test images. Chapter 9 supplies the collection plan and an entirely paper-based alternative. Camera access, browser compatibility, and model saving are setup checks, not learning outcomes.
No hosting is required. Earlier editions used Glitch, whose project hosting ended on 8 July 2025. That instruction has been retired. Glitch’s announcement
For an optional exported application, consult Google’s image-model library documentation. Export and hosting involve additional dependencies and deployment checks; they are outside the Core exercise. Do not upload private images or publish a customer-facing app as part of this lab.
NotebookLM: compare observable results
NotebookLM is an optional comparison system for the retrieval activity. Give it only sources approved for that service. Keep the source set and questions consistent with your other candidate.
Check answers and supporting passages, including a question the sources cannot answer. Do not describe an inferred chunking, ranking, or embedding pipeline as if you had inspected its internals. If access is unavailable, leave that candidate untested.
Ollama: local is a configuration to verify
Follow the official FAQ and setup guidance. Choose a local model that fits your available memory; a model tag mentioned in a chapter is an example, not a universal hardware recommendation.
Ollama supports both local and cloud-related capabilities. Confirm that the selected model and configuration actually run locally. Also inspect connected tools, logs, backups, and access: installing a local engine does not make the surrounding workflow private automatically. Record the model tag, configuration, and destination of each data flow.
Open WebUI: an optional configured interface
Use the official quick start. Setup can require an administrator or command-line installation. A facilitator-managed instance is an alternative, but ask who can access its documents and logs.
Connect only the intended model and approved sources. Test whether an answer actually uses the relevant source before calling the workflow retrieval-augmented. Keep action-taking tools disabled for the Chapter 7 injection exercise. Chapter 11 begins with a paper action map; granting an agent real permissions is not a prerequisite.
If setup gets in the way
Record the obstacle and continue with the self-contained audit, privacy, evaluation, or capstone exercise. Your dossier should say what you reasoned through and what you actually executed. Neither a paid subscription nor a successful installation substitutes for that distinction.