Best AI Fine-Tuning Platforms for Mid Size Business

LM-Kit.NET provides.NET developers with advanced AI capabilities to optimize large language model for specific needs. Use robust training parameters such as LoraAlpha and LoraRank alongside efficient optimization algorithms and a dynamic sample processing to tailor models with ease. LM-Kit.NET goes beyond fine-tuning to streamline model quantization, reducing model size while maintaining accuracy. This process converts the models into lower-precision formats. This allows for faster inference, and lower resource consumption. LoRA integration also allows for the modular merging of adapters, which facilitates quick adaptation to new tasks, without requiring a full retraining. LM-Kit.NET provides AI optimization for.NET applications using comprehensive guides, APIs and on-device processing.

Mistral AI is an advanced artificial intelligence company focused on open-source generative AI solutions. Offering adaptable, enterprise-level AI tools, the company enables deployment across cloud, on-premises, edge, and device-based environments. Key offerings include "Le Chat," a multilingual AI assistant designed for enhanced efficiency in both professional and personal settings, and "La Plateforme," a development platform for building and integrating AI-powered applications. With a strong emphasis on transparency and innovation, Mistral AI continues to drive progress in open-source AI and contribute to shaping AI policy.

Ango Hub is an all-in-one, quality-oriented data annotation platform that AI teams can use. Ango Hub is available on-premise and in the cloud. It allows AI teams and their data annotation workforces to quickly and efficiently annotate their data without compromising quality. Ango Hub is the only data annotation platform that focuses on quality. It features features that enhance the quality of your annotations. These include a centralized labeling system, a real time issue system, review workflows and sample label libraries. There is also consensus up to 30 on the same asset. Ango Hub is versatile as well. It supports all data types that your team might require, including image, audio, text and native PDF. There are nearly twenty different labeling tools that you can use to annotate data. Some of these tools are unique to Ango hub, such as rotated bounding box, unlimited conditional questions, label relations and table-based labels for more complicated labeling tasks.

SuperAnnotate is the best platform to build high-quality training datasets for NLP and computer vision. We enable machine learning teams to create highly accurate datasets and successful pipelines of ML faster with advanced tooling, QA, ML, and automation features, data curation and robust SDK, offline accessibility, and integrated annotation services. We have created a unified annotation environment by bringing together professional annotators and our annotation tool. This allows us to provide integrated software and services that will lead to better quality data and more efficient data processing.

At Kili Technology, we believe the foundation of better AI is excellent data. Kili Technology's complete training data platform empowers all businesses to transform unstructured data into high quality data to train their AI and deliver successful AI projects. By using Kili Technology to build training datasets, teams will improve their productivity, accelerate go-to-production cycles of their AI projects and deliver quality AI.

Cohere is an AI company that provides advanced language models designed to help businesses and developers create intelligent text-based applications. Their models support tasks like text generation, summarization, and semantic search, with options such as the Command family for high-performance applications and Aya Expanse for multilingual capabilities across 23 languages. Cohere emphasizes flexibility and security, offering deployment on cloud platforms, private environments, and on-premises systems. The company partners with major enterprises like Oracle and Salesforce to enhance automation and customer interactions through generative AI. Additionally, its research division, Cohere For AI, contributes to machine learning innovation by fostering global collaboration and open-source advancements.

Google Colab is a cloud-based Jupyter Notebook environment that allows users to write and execute Python code without any setup required. It provides access to powerful computing resources, including GPUs and TPUs, making it an excellent tool for machine learning, data analysis, and academic research. Colab is designed for collaborative work, enabling users to share notebooks with others, track changes, and easily integrate with Google Drive for file storage. It is particularly beneficial for both beginners and professionals seeking a free, interactive platform for experimenting with code and data. With Colab notebooks, you can integrate executable code with rich text, images, LaTeX, HTML, and other elements in a single document. All notebooks you create are saved to your Google Drive. Sharing with colleagues or friends is easy, allowing them to comment or edit your work. Additionally, Colab now provides a Data Science Agent that automates the entire analysis process, from data understanding to delivering insights in a functional notebook. (Note: Sequences are shortened, and results are for illustration. The Data Science Agent may occasionally make errors.)

Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility.

Explore a new library and dataset in a notebook. A 2orkflow automates preprocessing, training, and testing. A deployment brings your application to life. You can use notebooks, workflows, or deployments separately. Compatible with all. Gradient is compatible with all major frameworks. Gradient is powered with Paperspace's top-of-the-line GPU instances. Source control integration makes it easier to move faster. Connect to GitHub to manage your work and compute resources using git. In seconds, you can launch a GPU-enabled Jupyter Notebook directly from your browser. Any library or framework is possible. Invite collaborators and share a link. This cloud workspace runs on free GPUs. A notebook environment that is easy to use and share can be set up in seconds. Perfect for ML developers. This environment is simple and powerful with lots of features that just work. You can either use a pre-built template, or create your own. Get a free GPU

Machine learning can do amazing things, including understanding the world, driving cars, writing code, and making art. It's still very difficult to use. Research is usually published in a PDF format. There are also bits of code on GitHub and weights (if you're fortunate!) on Google Drive. It's difficult to apply that work to a real-world problem unless you're an expert. Machine learning is now accessible to everyone. Machine learning models should be shared by people who create them. People who want to use machine-learning should not need a PhD to share their machine learning models. Great power comes with great responsibility. We believe that this technology can be made safer and more understandable by using better tools and safeguards.

Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.

Google AI Studio is an online tool that's free and allows individuals and small groups to create apps and chatbots by using natural language prompting. It allows users to create API keys and prompts for app development. Google AI Studio allows users to discover Gemini Pro's APIs, create prompts and fine-tune Gemini. It also offers generous free quotas, allowing 60 requests a minute. Google has also developed a Generative AI Studio based on Vertex AI. It has models of various types that allow users to generate text, images, or audio content.

Build complex backend agents using multiple steps with an action-based orchestration. All LLMs are supported. Build a fully customized UI without writing a line of code for your agent, and serve it on your own domain. Track your agent's every move, literally, to cope with the nondeterministic nature LLMs. Access control can be built at a finer grain for your application, data and agent. A fine-tuned, specialized model to accelerate software development. Automatically handle concurrency and rate limiting.

Vishwa.ai, an AutoOps Platform for AI and ML Use Cases. It offers expert delivery, fine-tuning and monitoring of Large Language Models. Features: Expert Prompt Delivery : Tailored prompts tailored to various applications. Create LLM Apps without Coding: Create LLM workflows with our drag-and-drop UI. Advanced Fine-Tuning : Customization AI models. LLM Monitoring: Comprehensive monitoring of model performance. Integration and Security Cloud Integration: Supports Google Cloud (AWS, Azure), Azure, and Google Cloud. Secure LLM Integration - Safe connection with LLM providers Automated Observability for efficient LLM Management Managed Self Hosting: Dedicated hosting solutions. Access Control and Audits - Ensure secure and compliant operations.

Langtail is a cloud-based development tool designed to streamline the debugging, testing, deployment, and monitoring of LLM-powered applications. The platform provides a no-code interface for debugging prompts, adjusting model parameters, and conducting thorough LLM tests to prevent unexpected behavior when prompts or models are updated. Langtail is tailored for LLM testing, including chatbot evaluations and ensuring reliable AI test prompts. Key features of Langtail allow teams to: • Perform in-depth testing of LLM models to identify and resolve issues before production deployment. • Easily deploy prompts as API endpoints for smooth integration into workflows. • Track model performance in real-time to maintain consistent results in production environments. • Implement advanced AI firewall functionality to control and protect AI interactions. Langtail is the go-to solution for teams aiming to maintain the quality, reliability, and security of their AI and LLM-based applications.

Test, launch and refine Gen AI prompting. RAG Agents. Datasets. Workflows. A single workspace for Engineers and Product Managers to explore LLM technology.

Sync Computing's Gradient is an advanced AI-driven optimization engine designed to streamline and enhance cloud-based data infrastructure. Utilizing cutting-edge machine learning technology developed at MIT, Gradient enables organizations to optimize the performance of their cloud workloads on CPUs and GPUs while significantly reducing costs. The platform offers up to 50% savings on Databricks compute expenses, ensuring workloads consistently meet runtime service level agreements (SLAs). With continuous monitoring and dynamic adjustments, Gradient adapts to changing data sizes and workload patterns, delivering peak efficiency across complex pipelines. Seamlessly integrating with existing tools and supporting various cloud providers, Sync Computing provides a robust solution for optimizing modern data infrastructure.

You can use advanced language models and coding to solve a variety of problems. To build cutting-edge applications, leverage large-scale, generative AI models that have deep understandings of code and language to allow for new reasoning and comprehension. These coding and language models can be applied to a variety use cases, including writing assistance, code generation, reasoning over data, and code generation. Access enterprise-grade Azure security and detect and mitigate harmful use. Access generative models that have been pretrained with trillions upon trillions of words. You can use them to create new scenarios, including code, reasoning, inferencing and comprehension. A simple REST API allows you to customize generative models with labeled information for your particular scenario. To improve the accuracy of your outputs, fine-tune the hyperparameters of your model. You can use the API's few-shot learning capability for more relevant results and to provide examples.

Production-ready AI models that are fast and accurate. High-availability inference API that leverages the most advanced NVIDIA GPUs. We have selected the most popular open-source natural language processing models (NLP) and deployed them for the community. You can fine-tune your models (including GPT-J) or upload your custom models. Then, deploy them to production. Upload your AI models, including GPT-J, to your dashboard and immediately use them in production.

Create, evaluate, deploy, refine, and improve custom natural language processing models. Your team can automate workflows without the need for an AI expert team or expensive infrastructure. Metatext makes it easy to create customized AI/NLP models without any prior knowledge of ML, data science or MLOps. Automate complex workflows in just a few steps and rely on intuitive APIs and UIs to handle the heavy lifting. Our APIs will handle all the heavy lifting. Your custom AI will be trained and deployed automatically. A set of deep learning algorithms will help you get the most out of your custom AI. You can test it in a Playground. Integrate our APIs into your existing systems, Google Spreadsheets, or other tools. Choose the AI engine that suits your needs. Each AI engine offers a variety of tools that can be used to create datasets and fine tune models. Upload text data in different file formats and use our AI-assisted data labeling tool to annotate labels.

RunPod provides a cloud infrastructure that enables seamless deployment and scaling of AI workloads with GPU-powered pods. By offering access to a wide array of NVIDIA GPUs, such as the A100 and H100, RunPod supports training and deploying machine learning models with minimal latency and high performance. The platform emphasizes ease of use, allowing users to spin up pods in seconds and scale them dynamically to meet demand. With features like autoscaling, real-time analytics, and serverless scaling, RunPod is an ideal solution for startups, academic institutions, and enterprises seeking a flexible, powerful, and affordable platform for AI development and inference.

Labellerr is an advanced data annotation platform that streamlines the creation of high-quality labeled datasets for AI and machine learning applications. It supports a variety of data formats, including images, videos, text, PDFs, and audio, making it adaptable for different industries. The platform leverages AI-powered automation, such as model-assisted labeling and active learning, to significantly speed up the annotation process while maintaining accuracy. Labellerr also integrates smart quality assurance tools and detailed analytics to enhance data reliability and reduce human error. For specialized projects, it offers expert-in-the-loop services, providing access to professionals in domains like healthcare, automotive, and finance. The platform is designed for scalability, allowing teams to efficiently manage large datasets while ensuring high annotation quality. With its combination of automation, expert oversight, and user-friendly interface, Labellerr helps businesses accelerate AI model development with precise, well-labeled data.

Metal is a fully-managed, production-ready ML retrieval platform. Metal embeddings can help you find meaning in unstructured data. Metal is a managed services that allows you build AI products without having to worry about managing infrastructure. Integrations with OpenAI and CLIP. Easy processing & chunking of your documents. Profit from our system in production. MetalRetriever is easily pluggable. Simple /search endpoint to run ANN queries. Get started for free. Metal API Keys are required to use our API and SDKs. Authenticate by populating headers with your API Key. Learn how to integrate Metal into your application using our Typescript SDK. You can use this library in JavaScript as well, even though we love TypeScript. Fine-tune spp programmatically. Indexed vector data of your embeddings. Resources that are specific to your ML use case.

AI agents that interact and answer questions with users and complete tasks using your data and APIs. AI that can answer questions, summarize and extract insights from any long document. Transfer styles and formats, as well as tags and summaries between documents and data sources. Stack AI is used by developer teams to automate customer service, process documents, qualify leads, and search libraries of data. With a single button, you can try multiple LLM architectures and prompts. Collect data, run fine-tuning tasks and build the optimal LLM to fit your product. We host your workflows in APIs, so that your users have access to AI instantly. Compare the fine-tuning services of different LLM providers.

Describe examples of API requests and responses. Define API logic in natural language. Test your API endpoints, and fine-tune prompt, response structure, or request structure. Integrate API endpoints into your applications with just a click. In less than one minute, you can build and deploy sophisticated application logic with no code. No need for individual LLM accounts. Sign up for Backengine and get started building. Our super-fast backend architecture is available immediately. All endpoints have been secured and protected, so that only you and your application can use them. Manage your team members easily so that everyone can work on Backengine endpoints. Add persistent data to your Backengine endpoints. A complete replacement for the backend. Use external APIs to integrate your endpoints.