Microsoft Azure Cognitive Services
generalPre-built AI APIs from Microsoft for vision, speech, language, and decision tasks
The ML category is a broad collection of 674 tools that apply machine learning across industries and functions, from healthcare documentation and legal research to user research, code generation, and content creation. It captures AI applications that do not fit cleanly into a single vertical.
Pre-built AI APIs from Microsoft for vision, speech, language, and decision tasks
Open-source hub for ML models, datasets, and inference.
Real-time voice changer for gaming and streaming
AI assistant supporting multiple major models
AI contact center for omnichannel communication
Docs, spreadsheets, and apps in one platform
Payment and tax handling for digital products
Remove watermarks and unwanted objects from photos
AI voice changer for gaming, streaming, and chat
AI code assistant for faster development
Writing clarity tool that flags clunky sentences
Knowledge base software for teams
AI chatbot for customer support
Text-to-video with custom avatars and AI dubbing
Build and deploy custom apps with no code
AI-powered knowledge base for company information
All-in-one AI development platform from browser with zero setup
AI presentation maker for PowerPoint and Google Slides
AI content generation for businesses
Customer intelligence platform for feedback analysis
Real-time voice changer for Discord, Zoom, and OBS
Chrome extension combining multiple AI models
Enterprise platform for building voice AI agents
Practice management for healthcare
Because this category covers so many domains, browsing by sub-use case is more efficient than scrolling the full list. Tools like Hippo Scribe and SopCreator serve very specific professional workflows, while others like User Evaluation or Userpersona target product and UX teams. The quality bar across the category is uneven: some tools are mature products with enterprise customers, while others are early-stage experiments. When evaluating any tool in this space, look for evidence of actual accuracy and reliability in your specific domain, since ML performance varies dramatically across tasks. Integration depth and data handling are often the deciding factors for business use. Pricing models are diverse, from usage-based API billing to flat-rate SaaS subscriptions. Open-source alternatives exist for many of the underlying tasks, so for teams with technical resources, comparing commercial tools against self-hosted options is worth the effort.