Machine Learning at Scale
Predictive AnalyticsGet weekly insights to upskill as a Machine Learning Engineer. It's limited to case studies and design patterns.
Browse by the job,
not the category
Most people arrive knowing what they need done, not which category it lives in. 65 jobs grouped into 12 kinds of work. Pick the kind on the left, or search.
Tick Compare on any two to four cards to put them side by side.
Get weekly insights to upskill as a Machine Learning Engineer. It's limited to case studies and design patterns.
Suits data scientists, analysts; bad for casual users.
Data scientists and analysts; not for small businesses with limited budgets.
Runs AutoML training, monitoring, and deployment in one platform that plugs directly into your data warehouse.
Scrapes and converts messy web data into structured JSON or Markdown ready for an LLM's RAG pipeline.
A Python tool for searching, cleaning, and quantifying datasets before training an LLM.
For ML engineers building large-scale systems, but not for beginners or small projects.
Chats with locally running LLMs through Ollama and lets you RAG the page you're viewing.
An open-source Python framework for building and deploying real-world ML workflows at scale.
Steps through a dataset record by record and links AI prompts to specific columns as it goes.
Focus on ML, not infrastructure.
Uploads your chatbot conversations automatically to build a shared open-source training dataset.
Data Science Jobs & AI Careers, search over a thousand jobs worldwide.
Learn prompt engineering for LLMs and AI systems effectively. Practical articles, free lessons.
LLM Pricing Comparison tool lacks comprehensive coverage.
Lobe makes machine learning accessible with a simple tool, but lacks extensive features.
A dataset inspection tool that surfaces PII, duplicates, and bias before training an LLM.
KNIME simplifies data workflows but lacks built-in machine learning.
Weights & Biases helps track ML experiments, but requires manual setup for large teams.
Rent GPUs for AI and ML at real-time, transparent prices.
Lightning AI is a PyTorch framework for building and training models.
Filters large datasets with plain-language queries and surfaces the top options automatically.
Scores and prioritizes sales leads with machine learning, then syncs the results into major CRMs.
Lacks integration with native ad networks, only supports Google Ads and Facebook Ads.
Adds LLM-powered autocomplete to Overleaf, using a server you host yourself.
Turn data into insights with AI.
Professional and cost-effective data annotation services.
Tabirim is an AI-powered dream interpreter, offering personalized insights. It’s great for those curious about their dreams.
Collaborate on prompts, evaluate results with shared tooling. Optimizes iteration cycles.
Expert data labeling services for AI models, but costly and limited for small projects.
Turn enterprise documents into AI-ready knowledge. Build repeatable development workflows.
Build AI systems easily.
Suits research-driven businesses, not simple data analysis.
Bethge Lab specializes in autonomous lifelong learning, but lacks commercial applications.
Athenic is an AI that analyzes your business data, answering questions in plain English and building dashboards—no SQL needed.
On-demand context is available, but the tool requires manual selection of data points for each request.
An Azure-focused consultancy delivering data, AI and Copilot integration projects for enterprises.
The only vector database with a feedback loop, powering personalized search and recommendations.
NanthAI Edge is an AI workspace for multiple models and applications.
Pixta AI is a marketplace for AI training data.
Open-source, self-hosted AI gateway.
B2Metric transforms data into actionable insights.
AI-driven demand planning that forecasts market shifts and lines up inventory with changing demand.
Counts tokens to stay within OpenAI model limits.
Not ideal for large datasets, lacks advanced analytics.
Speeds up financial crime investigations by up to 70% with a centralized, AI-assisted platform.
The Grok App helps developers find information in documents but may struggle with very large datasets.
Saves time by reducing documentation and forum searches.
Models, training data and machine learning.
Data scientists and ML engineers prepare data, train and evaluate models, run experiments and move working models into production. The tools here cover that pipeline: model hubs and libraries (HuggingFace), no-code and AutoML platforms (BigML, Lobe, Abacus.AI), GPU compute (Vast.ai, Lightning AI, Modal) and local LLM runners like LM Studio. Compare reproducibility and how easily you can export a trained model.
AutoML platforms such as BigML and Abacus.AI choose algorithms and tune settings for you, which is fast for standard tabular problems. Writing models yourself gives more control over features, architecture and evaluation. Many teams use AutoML for a baseline, then build custom models when it falls short.
Apps such as LM Studio download open models and run them on your own machine, so data stays local. You need enough memory for the model size you choose, and smaller quantised models trade some quality for speed. This suits private data and offline experiments.
Marketplaces and platforms such as Vast.ai, Lightning AI and Modal rent GPU time by usage instead of hardware ownership. Compare availability, startup time, storage and how they handle interrupted jobs. Keep checkpoints saved, since cheaper capacity can be taken away mid-run.