AI guides
Practical playbooks for choosing and using AI tools.
Step-by-step AI tool guides for prompts, privacy, pilots, pricing, workflows, and practical team adoption.
39Guides
55Topic tags
2Active months
Git Workflow: Create a Branch, Commit, Push, and Create a Pull Request
A practical Git workflow guide for creating a feature branch, reviewing changes, staging files, committing safely, pushing to remote, opening a pull request, and merging back to develop.
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All AI tool guides
July 2026
1 guidesJune 2026
38 guides
Vertex AI Pipelines and ML Artifacts Guide
A practical beginner guide to Vertex AI Pipelines, ML workflow orchestration, pipeline components, artifact lineage, metadata, evaluation gates, and model artifact organization.
Vertex AI Prediction and Model Monitoring Guide
A practical guide to Vertex AI prediction and model monitoring, including batch prediction, online prediction, model serving, skew, drift, alert thresholds, and production review workflows.
Hyperparameter Tuning with Vertex Vizier: Beginner Guide
A beginner guide to hyperparameter tuning with Vertex Vizier, including search strategies, objective metrics, search spaces, trial budgets, tuning jobs, and practical mistakes to avoid.
Vertex AI Custom Training Guide for Beginners
A practical beginner guide to Vertex AI custom training, including when to use it, code structure, containers, dependencies, Cloud Storage, training jobs, artifacts, and production workflow checks.
Data Preprocessing Options for Enterprise Machine Learning
A beginner-friendly guide to enterprise ML data preprocessing options, including BigQuery, Dataflow, Dataproc, TensorFlow Transform, visual data preparation, data quality checks, and production workflow choices.
Enterprise Machine Learning Workflow Guide
A practical guide to the enterprise machine learning workflow, from problem definition, data preparation, experimentation, training, validation, model registry, deployment, monitoring, ownership, and governance.
BigQuery ML Beginner Guide: Build Models Where Your Data Lives
Learn how BigQuery ML helps analysts and data teams train, evaluate, and use machine learning models directly with SQL, including workflow, model choice, evaluation, governance, and practical use cases.
Data Quality And EDA For Machine Learning
A practical guide to improving data quality and using exploratory data analysis before training machine learning models, including missing values, outliers, leakage, labels, EDA workflows, and production checks.
Launching Into Machine Learning: A Practical Learning Path
A beginner-friendly learning path for launching into machine learning, covering workflow thinking, data quality, EDA, supervised learning, AutoML, BigQuery ML, evaluation, sampling, and practical study checkpoints.
Model Evaluation, Generalization, And Sampling Guide
A practical guide to model evaluation, overfitting, validation, test sets, cross-validation, benchmarks, metrics, segment review, and repeatable sampling for machine learning.
Supervised Learning: Regression And Classification Guide
Learn how supervised machine learning works and how to choose between regression and classification problems, including labels, metrics, thresholds, examples, and beginner mistakes.
Vertex AI AutoML Regression Guide For Beginners
A practical beginner guide to training and evaluating regression models with Vertex AI AutoML, including numeric labels, dataset preparation, metrics, baselines, deployment checks, and real-world examples.
Feature Engineering for Machine Learning: A Practical Learning Guide
A beginner-friendly guide to feature engineering for machine learning, covering feature types, leakage, availability, transformations, evaluation, feature reuse, and practical workflow checks.
Feature Engineering With Keras and BigQuery ML
A practical guide to feature engineering with Keras preprocessing layers and BigQuery ML, including normalization, encoding, bucketization, feature crosses, TRANSFORM, training-serving consistency, and workflow choices.
How to Choose Good Machine Learning Features
A practical checklist for choosing good machine learning features, avoiding leakage, checking prediction-time availability, reviewing ethics, testing feature value, and improving model quality.
How to Reduce Shadow AI Risk Without Blocking Useful Work
A practical guide to reducing shadow AI risk with approved tools, clear data rules, fast review paths, workflow ownership, employee education, and AI governance controls.
Vertex AI Feature Store Guide: Concepts, Benefits, and Workflow
A practical guide to Vertex AI Feature Store concepts, feature reuse, entity design, ingestion, batch serving, online serving, lineage, governance, and training-serving consistency.
How to Control AI Tool Costs Without Slowing Teams
A practical guide to controlling AI tool costs with usage visibility, seat reviews, workflow value tracking, model routing, budget alerts, renewal reviews, and governance rules.
How to Create an AI Agent Governance Checklist
A practical checklist for governing AI agents before they call tools, access data, update systems, send messages, spend money, or automate business workflows.
How to Build Generative AI Apps in Azure with Microsoft Foundry
A practical guide to building generative AI apps in Azure with Microsoft Foundry, covering project setup, model selection, SDK development, RAG, fine-tuning, responsible AI, and evaluation.
How to Set Up an AI Prompt Library
A practical guide to building an AI prompt library with workflow owners, reusable templates, examples, quality checks, versioning, review rules, and prompt governance.
How to Review AI Outputs Before Publishing
A practical review workflow for checking AI-generated content, summaries, recommendations, customer-facing messages, sources, privacy, brand voice, and human approval before publishing.
How to Write Better AI Prompts for Research: Practical Templates and Examples
Learn how to write better AI research prompts with practical templates, examples, source-checking instructions, comparison prompts, and follow-up questions.
How to Evaluate AI Tool Privacy Before Your Team Uses It
A practical privacy checklist for reviewing AI tools before sharing documents, customer data, code, financial records, prompts, files, or internal business information.
How to Build an AI Tool Stack for Small Teams
A practical guide to building a small AI tool stack without unnecessary cost, tool overlap, privacy risk, governance gaps, or workflow complexity.
How to Choose the Right AI Model
A practical guide to choosing the right AI model based on task complexity, context length, cost, speed, privacy, reliability, and workflow risk.
How to Use ChatGPT for Content Writing
A practical workflow for using ChatGPT to plan, draft, edit, optimize, and review content without losing originality, accuracy, brand voice, or human judgment.
How to Choose the Right AI Tool
A practical framework for choosing the right AI tool based on workflow fit, output quality, budget, privacy, integrations, team adoption, governance, and long-term value.
Prompt Engineering for Beginners
A beginner-friendly guide to writing better AI prompts with clear roles, tasks, context, examples, constraints, output formats, and review steps.
Research Better with Perplexity
A practical Perplexity workflow for source-backed research, citation review, comparison notes, follow-up questions, and trustworthy summaries for reports, content, and decisions.
Automate Repetitive Work with Zapier AI
A practical guide to building useful Zapier AI automations for operations, marketing, sales, support, and small teams without creating noisy or risky workflows.
Cursor Setup Guide for Developers
A practical Cursor setup guide for developers: install, configure settings, open projects correctly, use AI modes, protect private code, and build a safer AI-assisted coding workflow.
How to Measure AI Tool ROI
A practical framework for measuring AI tool ROI using workflow baselines, time saved, quality gains, adoption, tool cost, review effort, risk reduction, and business impact.
How to Create an AI Usage Policy
A practical guide to creating an AI usage policy for teams, including approved tools, data rules, review steps, risk levels, ownership, incidents, and accountability.
How to Compare AI Tool Pricing
A practical guide to comparing AI tool pricing across seats, usage limits, credits, model access, add-ons, admin controls, hidden operating costs, and renewal risk.
How to Pilot AI Tools With a Team
A practical pilot plan for testing AI tools with a small team before rollout, including workflow scope, success metrics, data rules, review steps, feedback, and adoption decisions.
How to Build an AI Research Workflow
A practical guide to building an AI research workflow with clear questions, source collection, citation review, synthesis, verification, reusable notes, and decision-ready briefs.
How to Keep AI Outputs On Brand
A practical workflow for keeping AI writing, images, summaries, support replies, and customer-facing content aligned with brand voice, accuracy, review rules, and quality standards.