madewithml.com

A free hosted REST API like ReqRes gives you a real HTTP endpoint you can call immediately—no signup, no local server, no database setup. You get predictable JSON responses for users, resources, login, and registration, which makes it useful for front-end demos, integration tests, learning HTTP clients, and prototyping. What it is not is a production backend for your app: the data is shared, resets periodically, and you don't control the schema. If you need persistent, private data with auth and logs, that's where an account-based backend or a commercial licence comes in.

What "free REST API for testing and prototyping" actually means

The phrase sounds vague, so it helps to separate two things people often conflate:

• A mock/sample API — a public, hosted service with fixed or semi-fixed endpoints that return realistic-looking JSON. You don't own the data. It exists so you can point code at a URL and get a response.
• A real backend you configure — a service where you define collections, schemas, authentication, and logging, and where your data persists and belongs to you.

ReqRes's landing page describes both: a free REST API for testing and prototyping with real responses and no signup, plus an option to build your own backend with collections, auth, and logs at app.reqres.in. Those are different products with different trade-offs. The free public endpoints are the "point and go" part; the account-based backend is the "own your data" part.

What you can do with the no-signup public endpoints

1. Front-end demos without a backend

If you're building a UI and need data to render, you can fetch from a public endpoint instead of hardcoding arrays. This keeps your demo code closer to real fetch logic:

async function loadUsers(page = 1) {
  const res = await fetch(`https://reqres.in/api/users?page=${page}`);
  if (!res.ok) throw new Error(`HTTP ${res.status}`);
  const { data, total, page: current } = await res.json();
  return { users: data, total, page: current };
}

You get pagination fields, a data array, and support metadata—enough to build list views, loading states, and empty states.

2. Integration and contract tests

You can assert that your HTTP layer handles status codes, headers, and JSON shapes correctly. Typical checks:

• GET /api/users/2 returns 200 with a data object.
• GET /api/users/23 returns 404 (a non-existent user).
• POST /api/login with valid credentials returns a token; with missing fields returns 400.

This is useful for testing your client wrapper, retry logic, error handling, and serialization—without spinning up your own server.

3. Learning HTTP clients and tooling

If you're new to fetch, Axios, curl, Postman, or HTTPie, a hosted API is a low-friction target. You can practice:

• Sending query parameters (?page=2, ?delay=3).
• Setting headers and reading response headers.
• Handling POST, PUT, PATCH, DELETE.
• Observing status codes for success and failure.

4. Deliberate failure and latency testing

Endpoints that return 404 on purpose, or that accept a delay parameter, let you test how your app behaves when things go wrong or slow down. That's hard to do reliably against a happy-path local mock.

What the public endpoints are not good for

The key limitation: you don't own the data, and other people are hitting the same endpoints. Treat responses as illustrative, not authoritative.

When you'd move to an account-based backend

Consider app.reqres.in (collections, auth, logs) when any of these are true:

• You need your own collections and fields, not the fixed demo schema.
• You need data to persist between sessions and belong only to you.
• You need real authentication flows you can rely on in a demo or internal tool.
• You need request logs to debug what your client actually sent.
• You're working with a team and need shared, stable endpoints.

The trade-off is setup and, eventually, cost. The public endpoints require none; the backend requires an account and configuration.

Where pricing and licensing become relevant

The site signals a commercial licence and an upgrade path (with Stripe as the payment platform), but specific prices, plan tiers, and limits aren't stated here—so don't assume numbers. What you can reason about:

• Prototyping and learning → free public endpoints are usually enough.
• Internal tools, demos for clients, or anything you don't want reset → an account-based backend is the natural next step.
• Production or commercial use → check the licence terms and any paid plan, because "free for testing" and "free for commercial production" are not the same thing.

Before committing, read the current terms on the site rather than relying on secondhand summaries, since pricing and licence scope change.

A quick decision checklist

1. Do you need data that persists and is private? If yes → account-based backend.
2. Do you need a custom schema? If yes → account-based backend.
3. Are you only testing HTTP behavior, UI rendering, or learning a client? If yes → free public endpoints.
4. Will this touch real users or revenue? If yes → review the licence and any paid plan first.
5. Do you need logs and team access? If yes → account-based backend.

If you answer "no" to 1, 2, 4, and 5, the free hosted API is likely all you need. If you answer "yes" to any of them, plan for the account-based path.

Made With ML is a free educational platform for developers who want to learn how to design, develop, deploy, and iterate on production machine learning applications. It combines machine learning concepts with software engineering best practices, and is created by GokuMohandas and associated with Anyscale. The site states it has 40K+ developers learning on the platform. It's best suited for developers who already have some Python and ML familiarity and want to move from notebooks to production-grade systems.

What the platform covers

The curriculum is organized into eight sequential modules:

There are also Foundations lessons covering Toolkit (Notebooks, Python, NumPy, Pandas, PyTorch) and Machine Learning (linear regression, logistic regression, neural networks, data quality), plus Deep Learning (CNNs, Embeddings, RNNs, Attention, Transformers).

How it teaches

The course follows a stated set of principles:

• First principles — every ML concept is developed from the ground up before jumping into code.
• Best practices — software engineering practices are applied throughout model development and deployment.
• Scale — ML workloads (data, train, tune, serve) scale in Python without learning entirely new languages.
• MLOps — components like tracking, testing, serving, and orchestration are connected into an end-to-end system.

The specific task used in the course is fine-tuning an LLM for a supervised task, but the site notes the lessons extend to other applications (NLP, CV, time-series), models (regression through LLMs), data modalities (tabular, text), cloud platforms (AWS, GCP), and scale (local laptop to distributed cluster).

Who it's for

The content is aimed at developers. If you're comfortable with Python and have some ML exposure but want to understand how to responsibly deliver value with ML in production, this is the target audience. Complete beginners may want to work through the Foundations section first.

Access and cost

The site offers a Subscribe option and mentions a live cohort where participants get live lessons, QA, compute (GPUs), and community. The homepage does not list pricing for the cohort or specify what the subscription includes, so check the site directly for current terms. The lesson content itself is presented as viewable on the site.

Where to start

1. Go to madewithml.com and review the course lesson list.
2. If you're new to the tooling, start with Foundations (Toolkit → Machine Learning → Deep Learning).
3. Otherwise begin at Module 1 (Design) and work through the modules in order.
4. The code repository is referenced as GokuMohandas/Made-With-ML for following along.

The main thing to decide up front: whether you want the self-paced lesson path or the live cohort with GPUs and instructor QA. The former costs nothing to browse; the latter requires signing up and checking current availability.

If you're new to Ahrefs and want to run your first SEO audit, the fastest path is: open Site Explorer, enter your target URL, review the Overview for a health snapshot, then dig into Organic Keywords, Top Pages, and Site Audit to find specific problems. From there, build a short prioritized to-do list instead of trying to fix everything at once.

This tutorial walks through that workflow using a realistic starting scenario, explains what the numbers mean, and shows how to turn findings into actions.

Before You Start: Pick a Narrow Scope

A common beginner mistake is auditing an entire large website on day one. The reports become overwhelming, and you can't tell which issues matter.

Instead, choose one of these starting points:

• A single important page (your homepage or a key product/service page)
• A small site (under ~50 pages, e.g., a personal blog or small business site)
• One section of a bigger site (e.g., /blog/)

For this tutorial, assume you're auditing a small business site with about 30 pages. The same steps scale up later.

You'll need an Ahrefs account to follow along. Ahrefs offers paid plans, and pricing and feature limits change over time, so check the current Pricing page for what's included in each tier before committing.

Step 1: Enter Your Target in Site Explorer

Site Explorer is Ahrefs' core tool for analyzing any website or URL.

1. Open Site Explorer from the top navigation.
2. In the search box, paste your domain (e.g., example.com).
3. Choose the Exact URL or Domain mode depending on scope. For a full-site view, use Domain or Prefix; for a single page, use Exact URL.
4. Press Enter.

You'll land on the Overview report. Don't try to absorb everything — focus on four numbers first.

Reading the Overview Snapshot

Important caveat: Ahrefs' traffic and keyword numbers are estimates based on its own data. They won't match Google Search Console or your analytics exactly. Treat them as directional, not absolute.

Step 2: See What You Already Rank For

Go to Organic Keywords in the left sidebar. This shows queries where your site appears in search results.

Sort by Traffic (descending) to see which pages bring the most estimated visitors. Then look for:

• Keywords ranking in positions 4–15 — these are often the easiest wins. A small content or on-page improvement can push them onto page one.
• Keywords with high volume but low position — potential opportunities if the topic is relevant.
• Irrelevant keywords — if you rank for something off-topic, it may signal thin or mismatched content.

Write down 5–10 of the position 4–15 keywords. These become your first optimization targets.

Step 3: Find Your Best and Weakest Pages

Open Top Pages. This ranks your URLs by estimated organic traffic.

Look for two things:

1. Your top performers — understand what topics and formats work. Can you create more content like this?
2. Pages with traffic but poor rankings — these may need on-page fixes (title, headings, internal links).

If a page gets zero traffic and targets a topic you care about, it's a candidate for a rewrite or consolidation.

Step 4: Run a Technical Site Audit

Now move to Site Audit. This crawls your site and flags technical and on-page issues.

1. Click Site Audit → New project.
2. Enter your domain and set crawl settings (default is usually fine for a small site).
3. Start the crawl and wait for it to finish.

Once complete, you'll see a Health Score and a list of issues grouped by category.

Which Issues to Fix First

Not all issues are equal. Prioritize in this order:

Ignore low-impact warnings (like minor meta description length) until the big items are handled.

Step 5: Turn Findings Into a To-Do List

You now have raw data. Convert it into a short, actionable list. Example:

1. Fix 3 broken links found in Site Audit.
2. Rewrite title tags on 5 pages with duplicate titles.
3. Improve 4 pages ranking in positions 6–12 by adding missing subtopics and internal links.
4. Remove or update 2 thin pages with no traffic.

Keep the list to 5–10 items max for your first audit. Finishing a short list beats starting a long one.

Common Beginner Mistakes

• Chasing every red flag. Site Audit flags many minor issues. Fix what affects rankings and users first.
• Trusting estimates as exact numbers. Ahrefs data is modeled, not measured from your analytics.
• Auditing a huge site too early. Start small to learn the interface.
• Ignoring search intent. A page can be technically perfect but still fail if it doesn't match what searchers want.
• Forgetting to re-crawl. After fixes, run Site Audit again to confirm improvements.

Where to Go Next

Once your first audit is done:

• Compare with competitors using Site Explorer's Competing Domains and Content Gap reports.
• Track keyword rankings over time with Rank Tracker.
• Explore backlink opportunities in the Backlinks and Link Intersect reports.
• Set up recurring Site Audit crawls so new issues surface automatically.

Your first audit isn't about perfection — it's about building a repeatable habit: enter a target, read the key reports, pick the highest-impact fixes, and act. Do that once a month and your site's health compounds.

Made With ML by Anyscale is a free-to-browse course site where you can start reading lessons immediately, and you have three main access paths: View lessons on the site, Subscribe to join the 40K+ developer community, and sign up for a live cohort that adds live lessons, QA, GPUs, and community. The GitHub repository GokuMohandas/Made-With-ML is also linked directly from the homepage. The site lists a "Subscribe" action but does not publish pricing or login requirements, so treat subscription terms as something to confirm on the page itself.

The three ways to access the material

The homepage presents these as parallel options rather than a funnel — you can read lessons without subscribing, and the cohort is a separate sign-up ("Learn more").

What you'll actually be working through

The course is structured as eight numbered stages, each with sub-topics:

1. 🎨 Design — Setup, Product, Systems
2. 🔢 Data — Preparation, Exploration, Preprocessing, Distributed
3. 🤖 Model — Training, Tracking, Tuning, Evaluation, Serving
4. 💻 Develop — Scripting, Command-line
5. 📦 Utilities — Logging, Documentation, Styling, Pre-commit
6. 🧪 Test — Code, Data, Models
7. ♻️ Reproducibility — Versioning
8. 🚀 Production — Jobs & Services, CI/CD workflows, Monitoring, Data engineering

There are also Foundations lessons (Toolkit: Notebooks, Python, NumPy, Pandas, PyTorch; Machine Learning: linear regression, logistic regression, neural networks, data quality; Deep Learning: CNNs, embeddings, RNNs, attention, transformers) and a Utilities section.

The stated goal is to combine machine learning with software engineering to "design, develop, deploy and iterate on production ML applications."

Before you start: what the course assumes

The site frames the content as "ML for Developers" and says the specific task involves fine-tuning an LLM for a supervised task — but explicitly notes this extends to other applications (NLP, CV, time-series), models (regression → LLMs), data modalities (tabular, text), cloud platforms (AWS, GCP), and scale (local laptop → distributed cluster).

Three design principles shape how you should approach it:

• First principles — concepts are explained before the code, so don't skip to the implementation.
• Best practices — software engineering practices are applied throughout model development and deployment.
• Scale — ML workloads (data, train, tune, serve) are scaled in Python without learning new languages.

If you're choosing between the self-paced lessons and the cohort, the deciding factor is compute and scheduling: the cohort bundles GPUs and live QA into one day, while the lessons let you supply your own environment and pace.

Made With ML uses one concrete task as its teaching vehicle — fine-tuning an LLM for a supervised task — but the site states that everything taught extends to other applications (NLP, CV, time-series), other models (regression through LLMs), other data modalities (tabular, text), other cloud platforms (AWS, GCP), and other scales (local laptop to distributed cluster). So if you want transferable ML engineering skills rather than a single-task recipe, the course is designed for that; if you only want a narrow tutorial on one model type, the framing will feel broader than you need.

Why a single task is used at all

Teaching production ML requires a concrete example, because abstract lessons about tracking, testing, and serving don't stick without something to run. Made With ML picks fine-tuning an LLM for a supervised task as that example, then builds the full lifecycle around it.

The site is explicit that this choice is pedagogical, not a limitation. The stated goal is that the concepts "easily extend" beyond the example task.

What actually transfers

The course is organized around a lifecycle rather than a model:

None of these stages are specific to LLMs. Versioning, CI/CD, monitoring, and data testing apply whether you ship a regression model or a transformer.

The site also lists separate foundations and toolkit sections (notebooks, Python, NumPy, Pandas, PyTorch; linear regression, logistic regression, neural networks, data quality) plus deep learning topics (CNNs, embeddings, RNNs, attention, transformers). That range is itself evidence the material isn't scoped to one task.

Who this fits, and who it doesn't

Fits you if: you want to learn to design, develop, deploy, and iterate on production ML applications, and you're willing to learn through one worked example that you then generalize.

May not fit you if: you want a task-specific recipe you can copy without understanding the surrounding engineering, or you're looking only for LLM fine-tuning mechanics with no interest in testing, reproducibility, or deployment.

The site describes its audience as developers — "Join 40K+ developers in learning how to responsibly deliver value with ML" — and the instructor is listed as Goku Mohandas.

Practical way to check before committing

1. Open the course lessons list and scan the eight lifecycle stages above.
2. Ask whether the stage you care about (say, monitoring or data versioning) is something you'd want to learn independent of the example task.
3. If yes for most stages, the general-skills framing matches your goal.
4. If you only want the fine-tuning steps, note that the course deliberately wraps them in the full lifecycle.

One caveat worth flagging: the site mentions a "Subscribe" action and a live cohort with "live lessons + QA, compute (GPUs) and community," but the provided page evidence does not state prices, access terms, or whether any content is free. Check the site directly for current terms before planning around cost or login requirements.