#Model training
10 tools curated for you
Build custom AI models without writing code using a no-code interface for image, text, audio, and video classification Deploy trained models instantly by exporting to multiple platforms for straightforward integration into your applications Keep your data completely private with local training that happens on your computer, not in the cloud Train models on any standard computer with CPU-optimized models that require no specialized GPU hardware Start projects faster using pre-built templates for object detection, image segmentation, and pose classification
Focus entirely on your data and algorithms while Tinker manages scheduling, tuning, and reliable infrastructure for you. Fine-tune models efficiently with LoRA, matching full fine-tuning performance while using less computational resources. Train on powerful GPU clusters with distributed training managed behind the scenes, eliminating hardware management. Maintain complete control over training with granular functions for forward/backward passes, weight updates, token sampling, and saving progress. Use your data privately with a strict policy ensuring it's only for fine-tuning your own models, not for training ours. Accelerate experimentation by supporting a wide range of open-source models, from compact to large-scale architectures.
Deploy custom AI models for content generation and data classification without hiring a machine learning team, using a platform that requires zero coding or technical expertise. Achieve precise brand voice and style adherence in emails, blogs, and marketing content by fine-tuning models directly on your own documents and writing samples. Transform raw business documents (PDF, CSV) into actionable classification and labeling models in minutes, bypassing complex data formatting and preprocessing steps. Maintain full control and reduce cloud costs by downloading fine-tuned LoRA adapters for open-source models to run on your own local hardware. Accelerate AI projects from days to under 5 minutes for typical jobs, using automated optimization that handles infrastructure and configuration behind the scenes.
Solve complex business problems and academic research challenges by simply describing your goal in natural language, as AIDE designs and optimizes complete Machine Learning pipelines. Gain a deeper understanding of your data and problem through an extensive research report delivered alongside final professionally crafted code. Deploy a ready-to-use model in relevant tasks immediately, moving beyond traditional AutoML that only automates parts of the process. Achieve optimal pipeline designs through iterative solution refinement, where AIDE systematically writes code to evaluate and improve each candidate solution. Empower novice users to produce expert-level ML solutions without deep technical expertise, while experts retain full control through detailed natural language instructions.
Train AI models of any size, from 1 billion to 24 trillion parameters, using a platform that eliminates model sharding and parallelism complexities. Start training immediately with preconfigured environments, secure storage, and built-in networking that require zero setup. Maintain full ownership of your data, models, and outputs with a platform that never stores, logs, or reuses your intellectual property without explicit permission. Design custom dense, MoE, multimodal, multilingual, reasoning, and agentic models, or start quickly with built-in support for leading open-source models. Choose flexible pricing that fits your needs by paying per hour or per model. Get a generative AI solution tailored to your dataset without spending time on training, as Cerebras AI experts construct, train, and fine-tune models on your behalf.
Spin up GPU inference in seconds instead of waiting on cluster provisioning with Kubernetes-native GPU compute and automated lifecycle management. Keep clusters running at peak utilization through rigorous health checks and near real-time reliability that eliminate idle GPU spend. Train and fine-tune large models faster using early access to the latest NVIDIA GPUs and high-performance networking built for distributed AI workloads. Cut infrastructure management overhead with managed software services, API, MCP, agents, and automation that let your team focus on model quality. Scale training and inference for Fintech risk analysis, Medical Research disease prediction, and Biotech genetic data workloads without re-architecting. Run predictive analytics, pattern recognition, and real-time data processing on purpose-built storage and networking tuned for demanding AI pipelines. Get 24/7 support from dedicated engineering teams that keep mission-critical AI training and inference workloads online around the clock.
Achieve your exact model performance targets by defining preferred outcomes through the interface, letting the system align results with your objectives without manual research. Save time and computational resources through parallel co-optimization of data and training recipes, which iteratively adjusts both elements simultaneously for efficient calibration. Deploy models immediately after optimization ends, as the system makes them readily production-ready once performance matches your defined goals. Apply the same system across medical, technology, finance, and enterprise domains without building specific retraining workflows for each vertical, eliminating sector-specific adaptation. Operate complex AI modeling without specialized expertise or pre-existing knowledge in building artificial intelligence systems, using a user-friendly design that makes advanced training accessible. Secure consistent gains in model performance across all industries, leveraging a core design focus that delivers reliable improvements for varied contexts.
Launch AI models without infrastructure delays using Prime Intellect's fully managed service that requires zero setup Build autonomous, decision-making AI systems with large-scale training services optimized for agentic workflows Access 2,500+ ready-to-use RL environments through the environment hub to accelerate model development Benchmark model performance against competitors using hosted evaluations and a public leaderboard Deploy custom models instantly with serverless inference capabilities and native LoRA support Turn production traces into continuously improving models through diverse API functions that complete the training loop Develop RL-trained subagents for specific business workflows to achieve maximum accuracy at reduced costs Execute code safely at scale with a secure sandbox optimized for reinforcement learning workflows Create, initialize, and update RL environments efficiently using the Prime Command Line Interface Collaborate with researchers and developers by contributing to open-source RL environments
Deploy a production-ready, business-specific AI model without writing a single line of code, using Vibe Training where you simply describe the desired behavior and Monostate handles data generation and training. Achieve superior accuracy and predictable results on your specific tasks by leveraging custom-trained, specialized smaller models that outperform generic alternatives. Cut operational costs significantly through task-focused models that require less computational power and memory than large, general-purpose AI. Guarantee your AI understands your unique business context and terminology by training models on your proprietary data, ensuring they are perfectly tailored to your needs. Eliminate in-production surprises with rigorously tested and fine-tuned AI models, ensuring reliable and consistent behavior in real-world business cases. Integrate AI seamlessly into your existing infrastructure without disruption, using ready-to-use APIs that ensure smooth incorporation into your current systems. Overcome data scarcity and bias by utilizing expert-curated data collection, human-validated annotation, and synthetic data generation to build a robust training foundation. Accelerate your AI development lifecycle with end-to-end enterprise consulting that manages everything from data organization and validation to final deployment and ongoing support.
Achieve continuous alignment between your LLMs and business metrics through an in-built reinforcement learning framework that dynamically tunes models for the outcomes you prioritize most. Cut model training costs and reduce reliance on real-world datasets by leveraging synthetic data generation that fine-tunes LLMs against written guidelines and expert feedback. Eliminate model selection guesswork by comparing proprietary and open LLMs side-by-side with personalized evaluations, AI judges, built-in RAG evaluators, and custom guideline assessments. Deploy new AI models without risking your entire user base using A/B testing with user subsets that deliver risk-free performance assurances before full rollout. Maintain real-time visibility into production AI performance with granular observability that tracks key metrics and traces at runtime for immediate course correction. Slash GPU expenses and accelerate response times with a proprietary inference engine optimized for low-latency, cost-effective model serving. Retain complete authority over where and how your enterprise data is stored by deploying on-premise or via private cloud infrastructure. Drive measurable business efficiency across enterprise search, business intelligence, and customer support by aligning AI model behavior with your unique success criteria.