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watsonx.ai
Produce powerful AI solutions with user-friendly interfaces, workflows and access to industry-standard APIs and SDKs.
Get one-stop access to capabilities that span the AI development lifecycle with built-in performance and scalability.
Unlock innovations for the AI builder through a collaborative development experience—with or without code.
Quickly build, run and manage gen AI applications in the hybrid cloud platform of your choice.
IBM® watsonx.ai™ is an enterprise-grade studio for developing AI services and deploying them into your applications of choice―with a collection of the APIs, tools, models and runtimes you need to turn your ideas and requirements into reality.
Streamline the application development process
This comprehensive suite of developer-focused capabilities―preconfigured SDKs, APIs, agentic workflows, retrieval augmented generation (RAG) frameworks and templates, advanced tuning methods and more―helps bring your AI application development process together in natural language or code. Explore AI agent development on watsonx.ai.
Manage the full model and application process
No need to scramble to pull together what you need for model training and generative AI development. Access a variety of powerful, low-cost, fit-for-purpose models, MLOps pipelines and AI runtimes that reside in one place for more efficient building of powerful applications with trusted data and built-in governance―that you can effectively manage, monitor and govern across the AI lifecycle.
Access a robust data science toolset
Bring generative AI and machine learning into production faster. Build AI/ML models automatically with model training, development and visual modeling, and synthetic data generation. You can also develop predictive and prescriptive models, code in Python Notebooks or Rstudio, or work directly in your IDE of choice.
Access enterprise-grade models that are performant, trusted and cost-effective, including IBM Granite models, select open-source models from Hugging Face, third-party models from strategic partners and custom foundation models.
Whether you need code explanation, campaigns or lesson planning, use fit-for-purpose foundation models to get a head start on quality content—both general and personalized.
AI developers can build and deploy ready-to-use knowledge management applications quickly with pre-built RAG templates, frameworks and APIs.
Streamline discovery and analysis of large amounts of data for faster, more valuable insights and forecasts specific to your needs and business requirements.
From more customer satisfaction to deeper analysis, the numbers tell the story
AddAI saw 50% fewer unanswered customer service queries
Silver Egg Technology saw a 75% faster hiring process anticipated
UHCW NHS Trust had 700 more patients seen weekly without adding staff
Blendow Group saw 90% less time needed to summarize and analyze documents
Resources for deeper dives into the many ways you can put watsonx.ai to work
Our developer hub provides a library of templates and guides to help developers get started.
Explore a curated set of demos, sample applications, trials and education for IBM’s AI solutions.
Chat directly with an LLM such as IBM Granite in a 30-day demo and see how to put AI models to work.
Build, deploy, and manage machine learning and generative AI solutions with watsonx.ai.
Learn about the historical rise of AI and what it means for business.
Read what companies are saying about watsonx.
Explore the product guide and learn about the latest product updates.
Seamless integrations and expanded capabilities with technology leaders.
In Mixture of Experts - episode 40, the panel tackles DeepSeek R1 misconceptions, explains model distillation, and dissects the open-source competition landscape.
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What does the future hold for DeepSeek? In episode 39 of Mixture of Experts, our panel debriefs DeepSeek-R1, Mistrals IPO indication, the FrontierMath controversy and the IDC gen AI code assistants report.
DeepSeek-R1 is a digital assistant that performs as well as OpenAI’s o1 on certain AI benchmarks for math and coding tasks, was trained with far fewer chips and is approximately 96% cheaper to use, according to the company.