openml.org

OpenML is an open platform for sharing datasets, algorithms, and experiments, with the stated goal of helping people "learn how to learn better, together." If you want to reuse machine learning resources instead of rebuilding them, or you want your own work to be discoverable and reproducible, OpenML is built for that. The site itself is a JavaScript application, so you need JavaScript enabled in your browser to use it.

What OpenML Is For

OpenML focuses on collaboration and reuse in machine learning. Rather than treating a model result as a one-off file on a laptop, the platform is organized around shared resources that others can find and build on. That makes it relevant if you are:

• Looking for datasets to practice or benchmark on
• Comparing algorithms or experiment results against work others have shared
• Publishing your own datasets, algorithms, or experiment runs so they can be reused
• Studying how machine learning practice itself can improve through shared evidence

What You Can Share

The platform's own description names three resource types:

The common thread is that each item is something another person could pick up and use, rather than a private artifact.

How to Decide If It Fits Your Task

Use OpenML when your goal is shared, reusable machine learning material — for example, you have a dataset you want others to benchmark on, or you want to see how a method performs across tasks others have already published. If your need is purely local experimentation with no intent to share or compare, the platform's collaborative framing adds little.

One practical note: because the site requires JavaScript, plan to access it through a normal browser session rather than expecting a plain static page.

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.

OpenML organizes its shared content into three main resource types: datasets, algorithms, and experiments. To browse or search them, go to openml.org and use the site's navigation to reach the corresponding resource lists. Note that the OpenML web app requires JavaScript — if the page appears blank or shows only a "You need to enable JavaScript to run this app" message, enable JavaScript in your browser and reload before you can browse anything.

What Each Resource Type Contains

OpenML describes itself as an open platform for sharing datasets, algorithms, and experiments "to learn how to learn better, together." The three categories map to distinct things you can look up:

Because the platform is built around sharing, each resource is tied to the others — an experiment typically references the dataset and algorithm involved, so starting from any one list can lead you to the others.

Steps to Reach the Resource Lists

1. Open a browser and go to openml.org.
2. Confirm JavaScript is enabled. If you see only the "enable JavaScript" notice, turn it on in your browser settings and reload the page.
3. Use the site's top-level navigation to select the resource type you want — datasets, algorithms, or experiments.
4. On the resource page, use the search or filter controls to narrow results (for example, by name or by related task).
5. Open an individual entry to see its details and its links to related datasets, algorithms, or experiments.

Expected result: you land on a browsable, searchable list for the resource type you chose, and each entry opens to a detail page.

Common Snags

• Blank page or the JavaScript notice: the app is client-rendered, so nothing loads without JavaScript. This is the most likely reason a first visit appears to fail.
• Wrong starting point: if you search from a dataset page but want experiment records, switch resource type first — the lists are separate.
• Narrow searches returning nothing: broaden the query or clear filters before concluding a resource isn't there.

Once JavaScript is on and you're on the right resource list, finding datasets, algorithms, and experiments on OpenML is a matter of navigating to the category and searching within it.

OpenML is an open platform for sharing datasets, algorithms, and experiments, so the resources you can browse and reuse fall into those three categories. It suits people who want public datasets for benchmarking, records of algorithms and experiment runs for comparison, and material they can reproduce or extend in their own work. The site itself requires JavaScript to run, so plan to use it through a browser rather than expecting a plain static page.

Datasets you can browse and download

OpenML's dataset collection is the most direct resource for research and teaching. You can search for public datasets, inspect their metadata, and pull them into your own workflow instead of rebuilding data from scratch. This is useful when you need a standard benchmark to compare methods, or a real-world dataset to test an idea quickly.

For example, if you are testing a new classifier and want a familiar reference point, you can look for a public dataset on OpenML, check its description and properties, then download or load it into your experiment. The practical condition is that the dataset must be publicly shared; private or restricted data is not part of what you can freely browse.

Algorithms and experiment records

Beyond raw data, OpenML hosts shared algorithms and experiment records. These let you see how others have approached a task and compare runs rather than starting from an empty page. The value here is traceability: an experiment record ties a task, a method, and a result together, which makes it easier to understand what was tried and to check whether your own results are in a comparable range.

A useful pattern is to find an experiment similar to yours, note the algorithm and setup recorded there, and then run your own version to see how it differs. Treat shared records as reference points, not as guaranteed best practices — the conditions behind each run still matter.

Using these resources to reproduce, compare, or extend work

The three resource types work together. A typical flow looks like this:

1. Find a dataset that matches your problem or a benchmark you want to use.
2. Look for related experiments on that dataset to see which algorithms have been tried.
3. Run your own experiment and compare your results against the shared records.
4. Share your own dataset, algorithm, or experiment if you want others to build on it.

The expected result is a reproducible comparison rather than an isolated number. A common sticking point is that a shared experiment may not match your exact preprocessing or evaluation setup, so check the recorded details before treating results as directly comparable.

What to check before relying on a resource

If your goal is a quick benchmark, start with datasets and related experiments. If your goal is to publish or collaborate, contributing your own records is the natural next step. The platform's stated purpose — learning how to learn better, together — reflects that these resources are meant to be reused and extended, not just viewed.

Sharing experiments on OpenML matters because it preserves the full runnable record behind a result—data, algorithm, and run configuration—so others can reproduce, compare, and build on it. A paper's reported number tells you what happened once; a shared experiment lets the community re-run it, test it on new datasets, and see where it breaks. This is most valuable when you want your method to be reusable and comparable, not just citable.

What OpenML Is Built to Share

OpenML describes itself as an open platform for sharing datasets, algorithms, and experiments "to learn how to learn better, together." That framing points to the core difference from a publication: the platform links the three artifacts together rather than reporting a result in isolation.

When these are linked, a result becomes a queryable object: someone can find every experiment using a given dataset or algorithm and compare outcomes directly.

Why This Beats Results-Only Publishing

Reproducibility becomes checkable, not aspirational

A published result usually gives you a number and a prose description of the setup. A shared experiment gives you the actual configuration. If a replication fails, the community can inspect the shared run to see whether the difference is in the data, the algorithm, or the setup—rather than guessing.

Comparison happens on common ground

Because datasets and algorithms have stable identities on the platform, comparisons across papers stop depending on whether two authors preprocessed data the same way. The shared experiment is the common reference point.

Collaboration is built into the record

Others can take your algorithm and run it on datasets you never tried, or take your dataset and apply methods you did not consider. Each new run adds to a connected body of evidence instead of sitting in a separate paper.

The work stays useful after publication

A result in a paper is fixed at publication. A shared experiment can be re-run, extended, and referenced as the platform and community grow.

A Concrete Example

Suppose you develop a classification method and report 87% accuracy on one dataset. If you publish only the result, a reader who gets 84% has no way to tell whether the gap comes from a different data split, a different preprocessing step, or an implementation difference.

If you share the experiment on OpenML instead, the reader can pull the same dataset and algorithm record, re-run it, and compare configurations directly. If they then apply your algorithm to three other datasets, those runs attach to the same algorithm entry—so your method's profile grows beyond the single number in the paper.

When Sharing Experiments Is the Right Choice

Sharing experiments on OpenML fits best when:

• You want your method to be reused and benchmarked by others, not just read.
• Reproducibility is a stated claim of your work and you want it verifiable.
• You are working with datasets others can access and want a shared reference for comparisons.
• You expect follow-on work that applies your method to new data.

It is less central when your contribution is purely theoretical, when the data cannot be shared, or when the result depends on infrastructure others cannot replicate.

Practical Starting Point

To get value from the platform, share the three linked pieces rather than a result alone: upload or reference the dataset, register the algorithm or workflow, and record the experiment that ties them together. The platform's own description emphasizes exactly this combination—datasets, algorithms, and experiments—as the unit of sharing. Start with one well-documented experiment; a single reproducible, linked run is more useful to the community than a dozen reported numbers with no traceable setup.