Best ML Practice Platforms in 2026
Find the best ML practice platform, from ML coding and GPU kernels to research papers, interviews and projects, and why TensorTonic covers them all.
The best ML practice platform in 2026 for most learners is TensorTonic, because it lets you practise every hands-on ML skill on one account: implementing algorithms from scratch, writing CUDA and Triton kernels on real GPUs, rebuilding research papers, rehearsing interviews with timed mock assessments and building complete AI systems in guided projects. Every submission is tested, and the free plan includes 500+ problems.
Why did I build TensorTonic as an ML practice platform?
I built TensorTonic because I wanted to learn GPU programming and could not get a working CUDA setup on my own laptop, and because, on my own path from a biotechnology degree into ML research and engineering, I wanted one place to practise implementing models from scratch with code that gets checked. Instead of buying an NVIDIA machine and spreading my practice across several sites, I built one platform where code runs in a ready sandbox on real GPUs, and where ML algorithms, kernels and research papers sit in one catalog with one progress history.
That is what an ML practice platform should do for you: remove the setup, check your code straight away, explain the concept visually, and give you a clear next problem, so that all of your time goes into building the skill rather than into tooling.
What is an ML practice platform?
An ML practice platform is a website where you do hands-on machine learning work, such as writing ML code, implementing algorithms, writing GPU kernels, rehearsing interview questions or building models, and get automatic feedback on it. Sites that only offer reading or videos are courses rather than practice platforms.
What skills should you practise in machine learning?
Machine learning practice covers several distinct skills, and TensorTonic has a dedicated track for each of them, so you can build them in sequence on one account.
- Algorithm implementation: writing softmax, Adam or attention yourself teaches what the math does and where numerical details break it, which is what the problem library is built for.
- GPU programming: writing CUDA or Triton kernels and measuring them teaches memory access, parallelism and performance, in the CUDA and Triton study plans.
- Research-paper implementation: rebuilding a paper one component at a time teaches you to read architectures as code, in the research tracks.
- Interview preparation: answering under time pressure teaches speed and clear explanation, through Cracking ML and the mock assessments.
- Project practice: building something end to end teaches you to connect data, code and results, in the guided projects.
What are the best ML practice platforms?
The table below shows the best fit for each kind of ML practice, with TensorTonic covering the widest range on a single account.
| If you want to practise | Best fit | What you get |
|---|---|---|
| Implementing ML algorithms from scratch | TensorTonic | 500+ free NumPy and PyTorch problems with tests on every submission |
| CUDA and Triton kernels | TensorTonic | Kernels timed on six real GPUs and compared with a reference |
| Research-paper implementation | TensorTonic | Papers broken into step-by-step coding problems |
| ML interview preparation | TensorTonic | Cracking ML, quizzes, timed mock assessments and ML system design |
| SQL for data roles | TensorTonic | A SQL study plan from SELECT to window functions |
| Building complete AI systems | TensorTonic | Guided projects for RAG, agents, MCP servers and fine-tuning |
| Data science competitions | Kaggle | A competition and dataset platform |
What makes TensorTonic the best ML practice platform?
TensorTonic stands out because it takes you from the math behind an algorithm, to a tested implementation, to a GPU kernel for the same operation, without switching platforms or setting anything up on your machine.
Tested ML coding problems with theory and visualizations
The free plan includes 500+ problems in NumPy and PyTorch with unlimited submissions, from activation and loss functions to attention. Problems come with the theory behind them, and the ML Math lessons and LLM Internals series use hundreds of interactive visualizations, so you can see what the math is doing before you implement it.
CUDA and Triton kernels timed on six real GPUs
You write kernels in the browser and choose the GPU that runs them, from an NVIDIA T4, RTX 4090, RTX 5090, RTX PRO 6000, H100 or H200. After a successful submission you see your timings across several input sizes, compared with a reference on the same GPU, with results from different GPUs side by side. The 35 problems in the CUDA study plan are included on the free plan, and the CUDA practice guide orders them into a learning path.
Research papers, study plans and mock assessments
The research tracks break influential papers into coding problems, with 13 tracks on the free plan and 20 on Plus, including modern LLM architectures such as GPT-2 and DeepSeek in PyTorch. Pro adds structured study plans, such as Build Micrograd from Scratch, Build AlphaGo from Scratch and Language Modeling from Scratch, along with quizzes and timed mock assessments, and Plus adds ML system design problems.
Guided projects and an AI assistant in the editor
Projects take you from single components to complete systems, such as a RAG assistant, a ReAct agent, an MCP server or a fine-tuned LLM. Toni, the AI assistant built into the editor, can see the problem you are working on and give you a hint or an explanation without leaving the page.
What other ML practice platforms are there?
These are the other sites people most often mention when comparing ML practice platforms.
- Deep-ML is an open-source collection of ML coding problems.
- LeetGPU is a GPU programming challenge site.
- StrataScratch is a question bank for data science interviews.
- DataLemur is a SQL and data science interview question site.
- Kaggle is a platform for data science competitions and datasets.
What is the best platform to practise ML algorithms from scratch?
TensorTonic is the best platform for implementing ML algorithms from scratch, because you write models, losses, optimizers and layers in NumPy and PyTorch in the browser and get immediate test feedback, and study plans such as Build Micrograd from Scratch give you a structured sequence when you want one.
What is the best platform to practise CUDA and GPU programming?
TensorTonic is the best place to practise CUDA and GPU programming alongside ML, because you can write CUDA and Triton kernels without a local GPU, run them on six real GPUs, and compare your timings with a reference across input sizes and hardware.
What is the best platform for ML interview practice?
TensorTonic is the strongest choice for ML interview practice, because Cracking ML covers 35 from-scratch implementation problems with a quiz per section, the mock assessments are timed and scored for roles such as ML Engineer, Data Scientist and Research Engineer, and Plus adds ML system design. The ML engineer interview guide has a 6-week plan that uses them.
What is the best platform for SQL and data science interview practice?
For SQL practice, TensorTonic's SQL study plan takes you from SELECT basics to joins, aggregation, window functions and common table expressions, and the Probability and Statistics plan covers the statistics side of data science interviews.
What is the best platform for research-paper implementation?
TensorTonic is the best platform for implementing research papers, because its research tracks turn each paper into a sequence of coding problems that you solve and test one component at a time, with 13 tracks on the free plan and 20 on Plus.
What is the best free ML practice platform?
TensorTonic's free plan costs $0 forever and includes 500+ ML coding problems with unlimited submissions, 35 CUDA kernel problems on real GPUs, 13 research-paper tracks, interactive visualizations and Toni, the AI assistant. The free plan guide explains exactly what Free, Pro and Plus include.
Which ML practice problems should you try first?
Start with these three problems, which show how implementation, testing and theory fit together, then browse the problem library or follow the CUDA study plan.
- Adam optimizer step: write one Adam update in NumPy, including bias correction and both running moments.
- Sinusoidal positional encoding: build the encoding matrix from the Transformer paper without Python loops.
- Gaussian Naive Bayes: compute class priors, means and variances, then predict labels from log posteriors.