Best AI Fine-Tuning Platforms in Canada

LM-Kit.NET, a cutting edge high-level inference toolkit, is designed to bring the advanced capabilities Large Language Models into the C# ecosystem. LM-Kit.NET is a powerful Generative AI toolkit that's tailored for developers who work within.NET. It makes it easier than ever before to integrate AI functionality into your applications. The SDK offers a wide range of AI features to cater to different industries. Text completion, Natural Language Processing, content retrieval and summarization, text enrichment, language translation are just a few of the many features. Whether you want to automate content creation or build intelligent data retrieval system, LM Kit.NET provides the flexibility and performance to accelerate your project.

Our open-source research efforts are focused on both the new "ware" (middleware and "software" which will wrap and integrate LLMs) as well as building high quality, automation-focused enterprise model available in Hugging Face. LLMWare is also a coherent, high quality, integrated and organized framework for developing LLM-applications in an open system. This provides the foundation for creating LLM-applications that are designed for AI Agent workflows and Retrieval Augmented Generation. Our LLM framework was built from the ground-up to handle complex enterprise use cases. We can provide pre-built LLMs tailored to your industry, or we can fine-tune and customize an LLM for specific domains and use cases. We provide an end-toend solution, from a robust AI framework to specialized models.

Tune Studio is a versatile and intuitive platform that allows users to fine-tune AI models with minimum effort. It allows users to customize machine learning models that have been pre-trained to meet their specific needs, without needing to be a technical expert. Tune Studio's user-friendly interface simplifies the process for uploading datasets and configuring parameters. It also makes it easier to deploy fine-tuned machine learning models. Tune Studio is ideal for beginners and advanced AI users alike, whether you're working with NLP, computer vision or other AI applications. It offers robust tools that optimize performance, reduce the training time and accelerate AI development.

With just one click, you can easily fine-tune and monetize your AI model. For AI startups, ML researchers, and engineers. Bakery is an AI platform that allows researchers, machine learning engineers and startups to easily fine-tune AI models and monetize them. Users can upload datasets, edit model settings, and publish models on the market. The platform supports a variety of model types and gives users access to datasets generated by the community for project development. The fine-tuning of models is made easier with Bakery. Users can build, test and deploy models more efficiently. The platform integrates with Hugging Face, and supports decentralized storage options. This ensures flexibility and scalability in diverse AI projects. The bakery allows contributors to build AI models collaboratively without exposing parameters or data. It ensures fair revenue distribution and proper attribution to all contributors.

Our platform allows you to create AI products, train, fine-tune, and deploy models on the cloud. You don't have to worry about scaling, infrastructure, cost management, or other technical issues. Prebuilt, fully customizable modular components make it easy to train, fine tune, and deploy models. The science, not the engineering, should be your focus. Lightning components organize code to run on the cloud and manage its own infrastructure, cloud cost, and other details. 50+ optimizations to lower cloud cost and deliver AI in weeks, not months. Enterprise-grade control combined with consumer-level simplicity allows you to optimize performance, reduce costs, and take on less risk. Get more than a demo. In days, not months, you can launch your next GPT startup, diffusion startup or cloud SaaSML service.

Designing and implementing solutions that use collective intelligence and augmented tech. Stability AI is a company that creates open AI tools that will allow us to reach our full potential. We are a team of builders who care deeply for real-world applications and implications. Collaboration across multiple teams is a key factor in many of our greatest achievements. We don't mind challenging established norms and exploring creativity. Our primary goal is to create breakthrough ideas and turn them into solutions. We value innovation more than tradition. We believe that our differences make us stronger, so we seek reason in every perspective.

You can choose from our library and fine-tune or create your own capabilities to analyze, process, and present text, audio, and video at large scale. Incorporate advanced NLP in your app or workflow. You can choose from the existing library or create your own. With just one API call, you can summarize, tag, and analyze language using stackable, composable NLP blocks. These blocks are built on state of the art models. Our powerful Custom-Skill engine allows you to create and fine-tune custom Language skills from your data. Only 5% of the world’s population can speak English as their first language. One AI's capabilities can be used in multiple languages. You can build a podcast platform, CRM or content publishing tool using One AI's multilingual capabilities.

We'll host your website so you can focus on building. Our rest API is easy to use. No limits, no headaches. We have all the information you need. Don't pay for nothing. We only charge for use. Our support team is also our tech team. You'll get support directly, not through a series of hoops. Our flexible infrastructure allows us scale with you as your business grows and can handle spikes in activity. Our stable diffusion API allows you to easily create images and art from text to image, or image to image. Additional models allow you to change the output's style. MJ v4, Any v3, Analog and Redshift, and many more. Other stable diffusion versions such as 2.0+ will also include. You can train your own stable diffusion model (fine tuning) and then deploy on Evoke via an API. In the future, we will have models such as Whisper, Yolo and GPT-J. We also plan to offer training and deployment on many other models.

Fine-tuning is when you take a dataset, and create a model to use AI. This is not always possible without programming so we created a solution in RIku that handles everything in a very easy format. Fine-tuning unlocks an entirely new level of power for artificial intelligence and we are excited to help you explore this. Public Share Links are landing pages you can create for any of the prompts. These can be designed with your brand in mind, including colors and adding your logo. These links can be shared with anyone, and if they have access to the password to unlock it they will be able make generations. No-code assistant builder for your audience. We found that projects using multiple large languages models have a lot of problems. They all return their outputs in a slightly different way.

With just one line of code, you can deploy all major ML frameworks like Pytorch and Onnx. Do you not have your own models? Prebuilt models can be deployed to reduce latency and cost. You can fine-tune models for specific tasks to reduce latency and costs while increasing performance. It's easy to do and you don't have to worry about infrastructure. Integrate with the top ML observability platform to be alerted on feature or prediction drift, compare models versions, and resolve issues quickly. To resolve model performance problems, discover the root causes of prediction and feature drift. Find out which features contribute the most to your model's performance.

Amazon Bedrock is a managed AWS service designed to make building and scaling generative AI applications easier by providing access to a diverse range of foundation models (FMs) from leading providers such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself. Through a single API, developers can test, fine-tune, and customize these models to meet specific use cases using advanced techniques like Retrieval Augmented Generation (RAG). The platform allows for the creation of intelligent agents that seamlessly integrate with enterprise systems and data sources, enabling enhanced automation and decision-making. Bedrock’s serverless architecture removes the need for infrastructure management, ensuring high scalability and minimal operational complexity. With a focus on security, data privacy, and responsible AI, Amazon Bedrock empowers organizations to accelerate innovation while maintaining trust and compliance. It represents a powerful tool for businesses aiming to integrate cutting-edge AI solutions into their operations effortlessly.

Data labeling project management is made easier with the fastest data annotation tool. High level of collaboration and automation.

The most flexible data annotation software. Quickly installable. Create custom UIs, or use pre-built labeling template. Layouts and templates that can be customized to fit your dataset and workflow. Detect objects in images. Supported are boxes, polygons and key points. Partition an image into multiple segments. Use ML models to optimize and pre-label the process. Webhooks, Python SDK and API allow you authenticate, create tasks, import projects, manage model predictions and more. ML backend integration allows you to save time by using predictions as a tool for your labeling process. Connect to cloud object storage directly and label data there with S3 and GCP. Data Manager allows you to manage and prepare your datasets using advanced filters. Support multiple projects, use-cases, and data types on one platform. You can preview the labeling interface as you type in the configuration. You can see live serialization updates at the bottom of the page.

Haystack’s pipeline architecture allows you to apply the latest NLP technologies to your data. Implement production-ready semantic searching, question answering and document ranking. Evaluate components and fine tune models. Haystack's pipelines allow you to ask questions in natural language, and find answers in your documents with the latest QA models. Perform semantic search to retrieve documents ranked according to meaning and not just keywords. Use and compare the most recent transformer-based language models, such as OpenAI's GPT-3 and BERT, RoBERTa and DPR. Build applications for semantic search and question answering that can scale up to millions of documents. Building blocks for the complete product development cycle, including file converters, indexing, models, labeling, domain adaptation modules and REST API.

Construct your model as a live interactive graph. View data flowing through the architecture of your visualized model. View and edit the model architecture at the atomic level. Graphbook offers X-ray transparency without black boxes. Graphbook checks data type and form in real-time, with clear error messages. This makes model debugging easy. Graphbook abstracts out software dependencies and configuration of the environment, allowing you to focus on your model architecture and data flows with the computing resources required. Cerbrec Graphbook transforms cumbersome AI modeling into a user friendly experience. Graphbook, which is backed by a growing community that includes machine learning engineers and data science experts, helps developers fine-tune their language models like BERT and GPT using text and tabular data. Everything is managed out of box, so you can preview how your model will behave.

Unlock prices that are 3-5x higher than those of traditional clouds. FluidStack aggregates GPUs from data centres around the world that are underutilized to deliver the best economics in the industry. Deploy up to 50,000 high-performance servers within seconds using a single platform. In just a few days, you can access large-scale A100 or H100 clusters using InfiniBand. FluidStack allows you to train, fine-tune and deploy LLMs for thousands of GPUs at affordable prices in minutes. FluidStack unifies individual data centers in order to overcome monopolistic GPU pricing. Cloud computing can be made more efficient while allowing for 5x faster computation. Instantly access over 47,000 servers with tier four uptime and security through a simple interface. Train larger models, deploy Kubernetes Clusters, render faster, and stream without latency. Setup with custom images and APIs in seconds. Our engineers provide 24/7 direct support through Slack, email, or phone calls.

Train, fine-tune and generate text and image AI based on your data. We use the best open-source models for image and text generation, and can train them within minutes using LoRA fine tuning. Click the share button to generate a link or bot to your session. You can deploy your own private infrastructure. Create a free Stable Diffusion XL account to start chatting and generating images using open source language models. Drag'n'drop is the easiest way to fine-tune your model using your own text or images. It takes between 3-10 minutes. You can chat with the models and create images using a familiar chat interface.

Pre-made models are the fastest way to get started using our illustration generator. Uploading 5-15 illustrations will allow you to fine-tune your own model if you need to depict a particular style or object that isn't available in our premade models. Fine-tuning is unlimited. You can use it to create icons, illustrations or any other assets. Learn more about fine tuning. Illustrations can be exported in PNG or SVG formats. Fine-tuning lets you train the AI model on a specific object or style and create a model that generates images based on those objects or styles. The quality of the fine-tuning depends on the data that you provide. For fine-tuning, it is recommended to use 5-15 images. Images can be any unique style or object. Images should only contain the subject, without background noises or other objects. Images cannot contain gradients or shadows, if you plan to export them as SVG. The PNG export works with gradients and Shadows.

Instill Core is a powerful AI infrastructure tool that orchestrates data, models, and pipelines, allowing for the rapid creation of AI-first apps. Instill Cloud is available or you can self-host from the instill core GitHub repository. Instill Core includes Instill VDP: Versatile Data Pipeline, designed to address unstructured data ETL problems and provide robust pipeline orchestration. Instill Model: A MLOps/LLMOps Platform that provides seamless model serving, fine tuning, and monitoring to ensure optimal performance with unstructured ETL. Instill Artifact: Facilitates orchestration of data for unified unstructured representation. Instill Core simplifies AI workflows and makes them easier to manage. It is a must-have for data scientists and developers who use AI technologies.

Entry Point AI is a modern AI optimization platform that optimizes proprietary and open-source language models. Manage prompts and fine-tunes in one place. We make it easy to fine-tune models when you reach the limits. Fine-tuning involves showing a model what to do, not telling it. It works in conjunction with prompt engineering and retrieval augmented generation (RAG) in order to maximize the potential of AI models. Fine-tuning your prompts can help you improve their quality. Imagine it as an upgrade to a few-shot model that incorporates the examples. You can train a model to perform at the same level as a high-quality model for simpler tasks. This will reduce latency and costs. For safety, to protect the brand, or to get the formatting correct, train your model to not respond in a certain way to users. Add examples to your dataset to cover edge cases and guide model behavior.

Amazon Elastic Compute Cloud Trn1 instances powered by AWS Trainium are designed for high-performance deep-learning training of generative AI model, including large language models, latent diffusion models, and large language models. Trn1 instances can save you up to 50% on the cost of training compared to other Amazon EC2 instances. Trn1 instances can be used to train 100B+ parameters DL and generative AI model across a wide range of applications such as text summarizations, code generation and question answering, image generation and video generation, fraud detection, and recommendation. The AWS neuron SDK allows developers to train models on AWS trainsium (and deploy them on the AWS Inferentia chip). It integrates natively into frameworks like PyTorch and TensorFlow, so you can continue to use your existing code and workflows for training models on Trn1 instances.

Virtualization Software for AI Infrastructure. Increase GPU utilization by having visibility and control over AI workloads. Run:AI has created the first virtualization layer in the world for deep learning training models. Run:AI abstracts workloads from the underlying infrastructure and creates a pool of resources that can dynamically provisioned. This allows for full utilization of costly GPU resources. You can control the allocation of costly GPU resources. The scheduling mechanism in Run:AI allows IT to manage, prioritize and align data science computing requirements with business goals. IT has full control over GPU utilization thanks to Run:AI's advanced monitoring tools and queueing mechanisms. IT leaders can visualize their entire infrastructure capacity and utilization across sites by creating a flexible virtual pool of compute resources.

The Xilinx AI development platform for AI Inference on Xilinx hardware platforms consists optimized IP, tools and libraries, models, examples, and models. It was designed to be efficient and easy-to-use, allowing AI acceleration on Xilinx FPGA or ACAP. Supports mainstream frameworks as well as the most recent models that can perform diverse deep learning tasks. A comprehensive collection of pre-optimized models is available for deployment on Xilinx devices. Find the closest model to your application and begin retraining! This powerful open-source quantizer supports model calibration, quantization, and fine tuning. The AI profiler allows you to analyze layers in order to identify bottlenecks. The AI library provides open-source high-level Python and C++ APIs that allow maximum portability from the edge to the cloud. You can customize the IP cores to meet your specific needs for many different applications.

It's not enough to just look at a few examples. To get actionable insights about how to improve your models, gather feedback from end-users at large. With the GPT improvement engine, you can easily A/B test models. You can only go so far with prompts. Fine-tuning your best data will produce better results. No coding or data science required. Integration in one line of code You can experiment with ChatGPT, Claude and other language model providers without having to touch it again. If you have the right tools to customize models for your customers, you can build innovative and defensible products on top APIs. Copy AI allows you to fine tune models based on the best data. This will allow you to save money and give you a competitive edge. This technology allows for magical product experiences that delight more than 2 million users.

Choose a model from our open-source collection, run it and access the model API within your product. No matter what model you are using for Image Recognition or Language Processing, all models come pre-trained and packaged with an easy-to use API. There are many models to choose from, and the list is growing. We curate and fine-tune only the best models from HuggingFace or Github. You can either host the model yourself or get your API-Key and endpoint with just one click. Cargoship keeps up with the advancement of AI so you don’t have to. The Cargoship Model Store has a collection that can be used for any ML use case. You can test them in demos and receive detailed guidance on how to implement the model. No matter your level of expertise, our team will pick you up and provide you with detailed instructions.