Alternatives to Core ML
Compare Core ML alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Core ML in 2026. Compare features, ratings, user reviews, pricing, and more from Core ML competitors and alternatives in order to make an informed decision for your business.
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1
Google Cloud Vision AI
Google
Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more. Google Cloud offers two computer vision products that use machine learning to help you understand your images with industry-leading prediction accuracy. Automate the training of your own custom machine learning models. Simply upload images and train custom image models with AutoML Vision’s easy-to-use graphical interface; optimize your models for accuracy, latency, and size; and export them to your application in the cloud, or to an array of devices at the edge. Google Cloud’s Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. Assign labels to images and quickly classify them into millions of predefined categories. Detect objects and faces, read printed and handwritten text, and build valuable metadata into your image catalog. -
2
TensorFlow
TensorFlow
An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.Starting Price: Free -
3
Create ML
Apple
Experience an entirely new way of training machine learning models on your Mac. Create ML takes the complexity out of model training while producing powerful Core ML models. Train multiple models using different datasets, all in a single project. Preview your model performance using Continuity with your iPhone camera and microphone on your Mac, or drop in sample data. Pause, save, resume, and extend your training process. Interactively learn how your model performs on test data from your evaluation set. Explore key metrics and their connections to specific examples to help identify challenging use cases, further investments in data collection, and opportunities to help improve model quality. Use an external graphics processing unit with your Mac for even better model training performance. Train models blazingly fast right on your Mac while taking advantage of CPU and GPU. Create ML has a variety of model types to choose from. -
4
Ultralytics
Ultralytics
Ultralytics offers a full-stack vision-AI platform built around its flagship YOLO model suite that enables teams to train, validate, and deploy computer-vision models with minimal friction. The platform allows you to drag and drop datasets, select from pre-built templates or fine-tune custom models, then export to a wide variety of formats for cloud, edge or mobile deployment. With support for tasks including object detection, instance segmentation, image classification, pose estimation and oriented bounding-box detection, Ultralytics’ models deliver high accuracy and efficiency and are optimized for both embedded devices and large-scale inference. The product also includes Ultralytics HUB, a web-based tool where users can upload their images/videos, train models online, preview results (even on a phone), collaborate with team members, and deploy via an inference API. -
5
Xilinx
Xilinx
The Xilinx’s AI development platform for AI inference on Xilinx hardware platforms consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP. Supports mainstream frameworks and the latest models capable of diverse deep learning tasks. Provides a comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices. You can find the closest model and start re-training for your applications! Provides a powerful open source quantizer that supports pruned and unpruned model quantization, calibration, and fine tuning. The AI profiler provides layer by layer analysis to help with bottlenecks. The AI library offers open source high-level C++ and Python APIs for maximum portability from edge to cloud. Efficient and scalable IP cores can be customized to meet your needs of many different applications. -
6
Virtual Face
Virtual Face
With just 15 photos of you, our advanced algorithm creates over 56 stunning variations that capture your true essence. Your photos are only used to train your own fine-tuned model. The fine-tuning takes a base model (in our case Stable Diffusion 1.5+) which is already trained on a large variety of images, then we leverage the Dreambooth paper written by Google Researchers to align the diffusion model on your face. If you liked a style in particular feel free to order a new set of virtual faces with only your preferred styles.Starting Price: $9.49 one-time payment -
7
Replicate
Replicate
Replicate is a platform that enables developers and businesses to run, fine-tune, and deploy machine learning models at scale with minimal effort. It offers an easy-to-use API that allows users to generate images, videos, speech, music, and text using thousands of community-contributed models. Users can fine-tune existing models with their own data to create custom versions tailored to specific tasks. Replicate supports deploying custom models using its open-source tool Cog, which handles packaging, API generation, and scalable cloud deployment. The platform automatically scales compute resources based on demand, charging users only for the compute time they consume. With robust logging, monitoring, and a large model library, Replicate aims to simplify the complexities of production ML infrastructure.Starting Price: Free -
8
Entry Point AI
Entry Point AI
Entry Point AI is the modern AI optimization platform for proprietary and open source language models. Manage prompts, fine-tunes, and evals all in one place. When you reach the limits of prompt engineering, it’s time to fine-tune a model, and we make it easy. Fine-tuning is showing a model how to behave, not telling. It works together with prompt engineering and retrieval-augmented generation (RAG) to leverage the full potential of AI models. Fine-tuning can help you to get better quality from your prompts. Think of it like an upgrade to few-shot learning that bakes the examples into the model itself. For simpler tasks, you can train a lighter model to perform at or above the level of a higher-quality model, greatly reducing latency and cost. Train your model not to respond in certain ways to users, for safety, to protect your brand, and to get the formatting right. Cover edge cases and steer model behavior by adding examples to your dataset.Starting Price: $49 per month -
9
Tune Studio
NimbleBox
Tune Studio is an intuitive and versatile platform designed to streamline the fine-tuning of AI models with minimal effort. It empowers users to customize pre-trained machine learning models to suit their specific needs without requiring extensive technical expertise. With its user-friendly interface, Tune Studio simplifies the process of uploading datasets, configuring parameters, and deploying fine-tuned models efficiently. Whether you're working on NLP, computer vision, or other AI applications, Tune Studio offers robust tools to optimize performance, reduce training time, and accelerate AI development, making it ideal for both beginners and advanced users in the AI space.Starting Price: $10/user/month -
10
Simplismart
Simplismart
Fine-tune and deploy AI models with Simplismart's fastest inference engine. Integrate with AWS/Azure/GCP and many more cloud providers for simple, scalable, cost-effective deployment. Import open source models from popular online repositories or deploy your own custom model. Leverage your own cloud resources or let Simplismart host your model. With Simplismart, you can go far beyond AI model deployment. You can train, deploy, and observe any ML model and realize increased inference speeds at lower costs. Import any dataset and fine-tune open-source or custom models rapidly. Run multiple training experiments in parallel efficiently to speed up your workflow. Deploy any model on our endpoints or your own VPC/premise and see greater performance at lower costs. Streamlined and intuitive deployment is now a reality. Monitor GPU utilization and all your node clusters in one dashboard. Detect any resource constraints and model inefficiencies on the go. -
11
Ilus AI
Ilus AI
The quickest way to get started with our illustration generator is to use pre-made models. If you want to depict a style or an object that is not available in the premade models you can train your own fine tune by uploading 5-15 illustrations. there are no limits to fine-tuning you can use it for illustrations icons or any assets you need. Read more about fine-tuning. Illustrations are exportable in PNG and SVG formats. Fine-tuning allows you to train the stable-diffusion AI model, on a particular object or style, and create a new model that generates images of those objects or styles. The fine-tuning will be only as good as the data you provide. Around 5-15 images are recommended for fine-tuning. Images can be of any unique object or style. Images should contain only the subject itself, without background noise or other objects. Images must not include any gradients or shadows if you want to export it as SVG later. PNG export still works fine with gradients and shadows.Starting Price: $0.06 per credit -
12
LLaMA-Factory
hoshi-hiyouga
LLaMA-Factory is an open source platform designed to streamline and enhance the fine-tuning process of over 100 Large Language Models (LLMs) and Vision-Language Models (VLMs). It supports various fine-tuning techniques, including Low-Rank Adaptation (LoRA), Quantized LoRA (QLoRA), and Prefix-Tuning, allowing users to customize models efficiently. It has demonstrated significant performance improvements; for instance, its LoRA tuning offers up to 3.7 times faster training speeds with better Rouge scores on advertising text generation tasks compared to traditional methods. LLaMA-Factory's architecture is designed for flexibility, supporting a wide range of model architectures and configurations. Users can easily integrate their datasets and utilize the platform's tools to achieve optimized fine-tuning results. Detailed documentation and diverse examples are provided to assist users in navigating the fine-tuning process effectively.Starting Price: Free -
13
Tinker
Thinking Machines Lab
Tinker is a training API designed for researchers and developers that allows full control over model fine-tuning while abstracting away the infrastructure complexity. It supports primitives and enables users to build custom training loops, supervision logic, and reinforcement learning flows. It currently supports LoRA fine-tuning on open-weight models across both LLama and Qwen families, ranging from small models to large mixture-of-experts architectures. Users write Python code to handle data, loss functions, and algorithmic logic; Tinker handles scheduling, resource allocation, distributed training, and failure recovery behind the scenes. The service lets users download model weights at different checkpoints and doesn’t force them to manage the compute environment. Tinker is delivered as a managed offering; training jobs run on Thinking Machines’ internal GPU infrastructure, freeing users from cluster orchestration. -
14
UnionML
Union
Creating ML apps should be simple and frictionless. UnionML is an open-source Python framework built on top of Flyte™, unifying the complex ecosystem of ML tools into a single interface. Combine the tools that you love using a simple, standardized API so you can stop writing so much boilerplate and focus on what matters: the data and the models that learn from them. Fit the rich ecosystem of tools and frameworks into a common protocol for machine learning. Using industry-standard machine learning methods, implement endpoints for fetching data, training models, serving predictions (and much more) to write a complete ML stack in one place. Data science, ML engineering, and MLOps practitioners can all gather around UnionML apps as a way of defining a single source of truth about your ML system’s behavior. -
15
Helix AI
Helix AI
Build and optimize text and image AI for your needs, train, fine-tune, and generate from your data. We use best-in-class open source models for image and language generation and can train them in minutes thanks to LoRA fine-tuning. Click the share button to create a link to your session, or create a bot. Optionally deploy to your own fully private infrastructure. You can start chatting with open source language models and generating images with Stable Diffusion XL by creating a free account right now. Fine-tuning your model on your own text or image data is as simple as drag’n’drop, and takes 3-10 minutes. You can then chat with and generate images from those fine-tuned models straight away, all using a familiar chat interface.Starting Price: $20 per month -
16
Peltarion
Peltarion
The Peltarion Platform is a low-code deep learning platform that allows you to build commercially viable AI-powered solutions, at speed and at scale. The platform allows you to build, tweak, fine-tune and deploy deep learning models. It is end-to-end, and lets you do everything from uploading data to building models and putting them into production. The Peltarion Platform and its precursor have been used to solve problems for organizations like NASA, Tesla, Dell, and Harvard. Build your own AI models or use our pre-trained ones. Just drag & drop, even the cutting-edge ones! Own the whole development process from building, training, tweaking to deploying AI. All under one hood. Operationalize AI and drive business value, with the help of our platform. Our Faster AI course is created for users who have no prior knowledge of AI. After completing seven short modules, users will be able to design and tweak their own AI models on the Peltarion platform. -
17
LEAP
Liquid AI
The LEAP Edge AI Platform offers a full-stack on-device AI toolchain that enables developers to build edge AI applications, from model selection through inference, entirely on device. It includes a best-model search engine to find the most appropriate model for a given task and device constraint, a curated library of pre-trained model bundles ready for download, and fine-tuning tools (such as GPU-optimized scripts) for customizing models like LFM2 to specific use cases. It supports vision-enabled capabilities across iOS, Android, and laptop devices, and includes function-calling so AI models can interact with external systems via structured outputs. For deployment, LEAP provides an Edge SDK that lets developers load and query models locally, just like a cloud API, but entirely offline, and a model bundling service to package any supported model or checkpoint into a bundle optimized for edge deployment.Starting Price: Free -
18
TheFluxTrain
TheFluxTrain
TheFluxTrain is an AI tool designed for creators, filmmakers, photographers, and fashion professionals. It empowers users to train and fine-tune Flux models using their own datasets, making it highly adaptable for unique creative projects such as film production, photography, and fashion campaigns. The platform offers tailored solutions for generating headshots, fashion model imagery, and other personalized visuals, ensuring professional-grade results. At its core is an AI-powered editor that combines state-of-the-art image editing features with the ability to use your custom-trained models. This seamless integration allows users to create, refine, and inpaint visuals with precision, blending creativity with advanced AI functionality. The intuitive interface makes it easy for professionals to explore endless possibilities, whether crafting striking visuals, enhancing portraits, or designing unique scenes for storytelling and branding.Starting Price: $15 -
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Baidu AI Cloud Machine Learning (BML), an end-to-end machine learning platform designed for enterprises and AI developers, can accomplish one-stop data pre-processing, model training, and evaluation, and service deployments, among others. The Baidu AI Cloud AI development platform BML is an end-to-end AI development and deployment platform. Based on the BML, users can accomplish the one-stop data pre-processing, model training and evaluation, service deployment, and other works. The platform provides a high-performance cluster training environment, massive algorithm frameworks and model cases, as well as easy-to-operate prediction service tools. Thus, it allows users to focus on the model and algorithm and obtain excellent model and prediction results. The fully hosted interactive programming environment realizes the data processing and code debugging. The CPU instance supports users to install a third-party software library and customize the environment, ensuring flexibility.
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20
Cleanlab
Cleanlab
Cleanlab Studio handles the entire data quality and data-centric AI pipeline in a single framework for analytics and machine learning tasks. Automated pipeline does all ML for you: data preprocessing, foundation model fine-tuning, hyperparameter tuning, and model selection. ML models are used to diagnose data issues, and then can be re-trained on your corrected dataset with one click. Explore the entire heatmap of suggested corrections for all classes in your dataset. Cleanlab Studio provides all of this information and more for free as soon as you upload your dataset. Cleanlab Studio comes pre-loaded with several demo datasets and projects, so you can check those out in your account after signing in. -
21
Bakery
Bakery
Easily fine-tune & monetize your AI models with one click. For AI startups, ML engineers, and researchers. Bakery is a platform that enables AI startups, machine learning engineers, and researchers to fine-tune and monetize AI models with ease. Users can create or upload datasets, adjust model settings, and publish their models on the marketplace. The platform supports various model types and provides access to community-driven datasets for project development. Bakery's fine-tuning process is streamlined, allowing users to build, test, and deploy models efficiently. The platform integrates with tools like Hugging Face and supports decentralized storage solutions, ensuring flexibility and scalability for diverse AI projects. The bakery empowers contributors to collaboratively build AI models without exposing model parameters or data to one another. It ensures proper attribution and fair revenue distribution to all contributors.Starting Price: Free -
22
Sagify
Sagify
Sagify complements AWS Sagemaker by hiding all its low-level details so that you can focus 100% on Machine Learning. Sagemaker is the ML engine and Sagify is the data science-friendly interface. You just need to implement 2 functions, a train and a predict in order to train, tune and deploy hundreds of ML models. Manage your ML models from one place without dealing with low level engineering tasks. No more flaky ML pipelines. Sagify offers 100% reliable training and deployment on AWS. Train, tune and deploy hundreds of ML models by implementing just 2 functions. -
23
Ludwig
Uber AI
Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Build custom models with ease: a declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Optimized for scale and efficiency: automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. Expert level control: retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations. Modular and extensible: experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning. -
24
Edge Impulse
Edge Impulse
Build advanced embedded machine learning applications without a PhD. Collect sensor, audio, or camera data directly from devices, files, or cloud integrations to build custom datasets. Leverage automatic labeling tools from object detection to audio segmentation. Set up and run reusable scripted operations that transform your input data on large sets of data in parallel by using our cloud infrastructure. Integrate custom data sources, CI/CD tools, and deployment pipelines with open APIs. Accelerate custom ML pipeline development with ready-to-use DSP and ML algorithms. Make hardware decisions based on device performance and flash/RAM every step of the way. Customize DSP feature extraction algorithms and create custom machine learning models with Keras APIs. Fine-tune your production model with visualized insights on datasets, model performance, and memory. Find the perfect balance between DSP configuration and model architecture, all budgeted against memory and latency constraints. -
25
OpenPipe
OpenPipe
OpenPipe provides fine-tuning for developers. Keep your datasets, models, and evaluations all in one place. Train new models with the click of a button. Automatically record LLM requests and responses. Create datasets from your captured data. Train multiple base models on the same dataset. We serve your model on our managed endpoints that scale to millions of requests. Write evaluations and compare model outputs side by side. Change a couple of lines of code, and you're good to go. Simply replace your Python or Javascript OpenAI SDK and add an OpenPipe API key. Make your data searchable with custom tags. Small specialized models cost much less to run than large multipurpose LLMs. Replace prompts with models in minutes, not weeks. Fine-tuned Mistral and Llama 2 models consistently outperform GPT-4-1106-Turbo, at a fraction of the cost. We're open-source, and so are many of the base models we use. Own your own weights when you fine-tune Mistral and Llama 2, and download them at any time.Starting Price: $1.20 per 1M tokens -
26
Nixtla
Nixtla
Nixtla is a platform for time-series forecasting and anomaly detection built around its flagship model TimeGPT, described as the first generative AI foundation model for time-series data. It was trained on over 100 billion data points spanning domains such as retail, energy, finance, IoT, healthcare, weather, web traffic, and more, allowing it to make accurate zero-shot predictions across a wide variety of use cases. With just a few lines of code (e.g., via their Python SDK), users can supply historical data and immediately generate forecasts or detect anomalies, even for irregular or sparse time series, and without needing to build or train models from scratch. TimeGPT supports advanced features like handling exogenous variables (e.g., events, prices), forecasting multiple time-series at once, custom loss functions, cross-validation, prediction intervals, and model fine-tuning on bespoke datasets.Starting Price: Free -
27
Axolotl
Axolotl
Axolotl is an open source tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures. It enables users to train models, supporting methods like full fine-tuning, LoRA, QLoRA, ReLoRA, and GPTQ. Users can customize configurations using simple YAML files or command-line interface overrides, and load different dataset formats, including custom or pre-tokenized datasets. Axolotl integrates with technologies like xFormers, Flash Attention, Liger kernel, RoPE scaling, and multipacking, and works with single or multiple GPUs via Fully Sharded Data Parallel (FSDP) or DeepSpeed. It can be run locally or on the cloud using Docker and supports logging results and checkpoints to several platforms. It is designed to make fine-tuning AI models friendly, fast, and fun, without sacrificing functionality or scale.Starting Price: Free -
28
vishwa.ai
vishwa.ai
vishwa.ai is an AutoOps platform for AI and ML use cases. It provides expert prompt delivery, fine-tuning, and monitoring of Large Language Models (LLMs). Features: Expert Prompt Delivery: Tailored prompts for various applications. Create no-code LLM Apps: Build LLM workflows in no time with our drag-n-drop UI Advanced Fine-Tuning: Customization of AI models. LLM Monitoring: Comprehensive oversight of model performance. Integration and Security Cloud Integration: Supports Google Cloud, AWS, Azure. Secure LLM Integration: Safe connection with LLM providers. Automated Observability: For efficient LLM management. Managed Self-Hosting: Dedicated hosting solutions. Access Control and Audits: Ensuring secure and compliant operations.Starting Price: $39 per month -
29
NVIDIA Cosmos
NVIDIA
NVIDIA Cosmos is a developer-first platform of state-of-the-art generative World Foundation Models (WFMs), advanced video tokenizers, guardrails, and an accelerated data processing and curation pipeline designed to supercharge physical AI development. It enables developers working on autonomous vehicles, robotics, and video analytics AI agents to generate photorealistic, physics-aware synthetic video data, trained on an immense dataset including 20 million hours of real-world and simulated video, to rapidly simulate future scenarios, train world models, and fine‑tune custom behaviors. It includes three core WFM types; Cosmos Predict, capable of generating up to 30 seconds of continuous video from multimodal inputs; Cosmos Transfer, which adapts simulations across environments and lighting for versatile domain augmentation; and Cosmos Reason, a vision-language model that applies structured reasoning to interpret spatial-temporal data for planning and decision-making.Starting Price: Free -
30
Forefront
Forefront.ai
Powerful language models a click away. Join over 8,000 developers building the next wave of world-changing applications. Fine-tune and deploy GPT-J, GPT-NeoX, Codegen, and FLAN-T5. Multiple models, each with different capabilities and price points. GPT-J is the fastest model, while GPT-NeoX is the most powerful—and more are on the way. Use these models for classification, entity extraction, code generation, chatbots, content generation, summarization, paraphrasing, sentiment analysis, and much more. These models have been pre-trained on a vast amount of text from the open internet. Fine-tuning improves upon this for specific tasks by training on many more examples than can fit in a prompt, letting you achieve better results on a wide number of tasks. -
31
Llama 2
Meta
The next generation of our open source large language model. This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters. Llama 2 pretrained models are trained on 2 trillion tokens, and have double the context length than Llama 1. Its fine-tuned models have been trained on over 1 million human annotations. Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests. Llama 2 was pretrained on publicly available online data sources. The fine-tuned model, Llama-2-chat, leverages publicly available instruction datasets and over 1 million human annotations. We have a broad range of supporters around the world who believe in our open approach to today’s AI — companies that have given early feedback and are excited to build with Llama 2.Starting Price: Free -
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Cerebrium
Cerebrium
Deploy all major ML frameworks such as Pytorch, Onnx, XGBoost etc with 1 line of code. Don't have your own models? Deploy our prebuilt models that have been optimised to run with sub-second latency. Fine-tune smaller models on particular tasks in order to decrease costs and latency while increasing performance. It takes just a few lines of code and don't worry about infrastructure, we got it. Integrate with top ML observability platforms in order to be alerted about feature or prediction drift, compare model versions and resolve issues quickly. Discover the root causes for prediction and feature drift to resolve degraded model performance. Understand which features are contributing most to the performance of your model.Starting Price: $ 0.00055 per second -
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Code Snippets AI
Code Snippets AI
Turn your questions into code. Easily store and fetch your snippets. Collaborate with your team. Powered by ChatGPT & our fine-tuned GPT3 model. Gain a deeper understanding of your code to further your knowledge. Increase the quality of your code with our refactor and debug features. Securely share code snippets with your team, without losing formatting. We use ChatGPT & our fine-tuned GPT3 Model, which provides faster and more accurate responses to your questions, compared to Codex apps. Create documentation, refactor, debug, and generate code with the click of a button. We use a fine-tuned AI model trained on GPT3, which provides faster and more accurate responses to your questions, compared to Codex apps. Save your code from your IDE straight into your library with our VSCode extension. Search snippets by language, name, or folder. Create your own folder structure to suit your needs. We use ChatGPT & our fine-tuned GPT3 Model, which provides faster and more accurate responses.Starting Price: $2 per month -
34
Keepsake
Replicate
Keepsake is an open-source Python library designed to provide version control for machine learning experiments and models. It enables users to automatically track code, hyperparameters, training data, model weights, metrics, and Python dependencies, ensuring that all aspects of the machine learning workflow are recorded and reproducible. Keepsake integrates seamlessly with existing workflows by requiring minimal code additions, allowing users to continue training as usual while Keepsake saves code and weights to Amazon S3 or Google Cloud Storage. This facilitates the retrieval of code and weights from any checkpoint, aiding in re-training or model deployment. Keepsake supports various machine learning frameworks, including TensorFlow, PyTorch, scikit-learn, and XGBoost, by saving files and dictionaries in a straightforward manner. It also offers features such as experiment comparison, enabling users to analyze differences in parameters, metrics, and dependencies across experiments.Starting Price: Free -
35
prompteasy.ai
prompteasy.ai
You can now fine-tune GPT with absolutely zero technical skills. Enhance AI models by tailoring them to your specific needs. Prompteasy.ai helps you fine-tune AI models in a matter of seconds. We make AI tailored to your needs by helping you fine-tune it. The best part is, that you don't even have to know AI fine-tuning. Our AI models will take care of everything. We will be offering prompteasy for free as part of our initial launch. We'll be rolling out pricing plans later this year. Our vision is to make AI smart and easily accessible to anyone. We believe that the true power of AI lies in how we train and orchestrate the foundational models, as opposed to just using them off the shelf. Forget generating massive datasets, just upload relevant materials and interact with our AI through natural language. We take care of building the dataset ready for fine-tuning. You just chat with the AI, download the dataset, and fine-tune GPT.Starting Price: Free -
36
Orpheus TTS
Canopy Labs
Canopy Labs has introduced Orpheus, a family of state-of-the-art speech large language models (LLMs) designed for human-level speech generation. These models are built on the Llama-3 architecture and are trained on over 100,000 hours of English speech data, enabling them to produce natural intonation, emotion, and rhythm that surpasses current state-of-the-art closed source models. Orpheus supports zero-shot voice cloning, allowing users to replicate voices without prior fine-tuning, and offers guided emotion and intonation control through simple tags. The models achieve low latency, with approximately 200ms streaming latency for real-time applications, reducible to around 100ms with input streaming. Canopy Labs has released both pre-trained and fine-tuned 3B-parameter models under the permissive Apache 2.0 license, with plans to release smaller models of 1B, 400M, and 150M parameters for use on resource-constrained devices. -
37
Tencent Cloud TI Platform
Tencent
Tencent Cloud TI Platform is a one-stop machine learning service platform designed for AI engineers. It empowers AI development throughout the entire process from data preprocessing to model building, model training, model evaluation, and model service. Preconfigured with diverse algorithm components, it supports multiple algorithm frameworks to adapt to different AI use cases. Tencent Cloud TI Platform delivers a one-stop machine learning experience that covers a complete and closed-loop workflow from data preprocessing to model building, model training, and model evaluation. With Tencent Cloud TI Platform, even AI beginners can have their models constructed automatically, making it much easier to complete the entire training process. Tencent Cloud TI Platform's auto-tuning tool can also further enhance the efficiency of parameter tuning. Tencent Cloud TI Platform allows CPU/GPU resources to elastically respond to different computing power needs with flexible billing modes. -
38
FinetuneDB
FinetuneDB
Capture production data, evaluate outputs collaboratively, and fine-tune your LLM's performance. Know exactly what goes on in production with an in-depth log overview. Collaborate with product managers, domain experts and engineers to build reliable model outputs. Track AI metrics such as speed, quality scores, and token usage. Copilot automates evaluations and model improvements for your use case. Create, manage, and optimize prompts to achieve precise and relevant interactions between users and AI models. Compare foundation models, and fine-tuned versions to improve prompt performance and save tokens. Collaborate with your team to build a proprietary fine-tuning dataset for your AI models. Build custom fine-tuning datasets to optimize model performance for specific use cases. -
39
thinkdeeply
Think Deeply
Discover from a variety of assets to jump-start your AI project. The AI hub provides a rich collection of artifacts that your project may need - industry AI starter kits, datasets, notebooks, pre-trained models, deployment-ready solutions & pipelines. Get access to the best resources from external parties, or created by your organization. Prepare and manage your data for model training. Collect, organize, tag, or select features, and prepare datasets for training with simple drag and drop UI. Collaborate with multiple team members to tag large datasets. Implement a quality control process to ensure dataset quality. Build models with simple clicks using the model wizards. No data science knowledge required. The system selects the best models for the problem and optimizes their training parameters. Advanced users, however, can fine-tune the models and their hyper-parameters. One-click deployment to production inference enviornments. -
40
Teachable Machine
Teachable Machine
A fast, easy way to create machine learning models for your sites, apps, and more – no expertise or coding required. Teachable Machine is flexible – use files or capture examples live. It’s respectful of the way you work. You can even choose to use it entirely on-device, without any webcam or microphone data leaving your computer. Teachable Machine is a web-based tool that makes creating machine learning models fast, easy, and accessible to everyone. Educators, artists, students, innovators, makers of all kinds – really, anyone who has an idea they want to explore. No prerequisite machine learning knowledge required. You train a computer to recognize your images, sounds, and poses without writing any machine learning code. Then, use your model in your own projects, sites, apps, and more. -
41
Open R1
Open R1
Open R1 is a community-driven, open-source initiative aimed at replicating the advanced AI capabilities of DeepSeek-R1 through transparent methodologies. You can try Open R1 AI model or DeepSeek R1 free online chat on Open R1. The project offers a comprehensive implementation of DeepSeek-R1's reasoning-optimized training pipeline, including tools for GRPO training, SFT fine-tuning, and synthetic data generation, all under the MIT license. While the original training data remains proprietary, Open R1 provides the complete toolchain for users to develop and fine-tune their own models.Starting Price: Free -
42
Oxen.ai
Oxen.ai
Oxen.ai is a collaborative data platform built to help teams manage, version, and operationalize machine learning datasets from initial curation through model deployment. At its core, the system provides a high-performance data version control engine optimized for large and complex datasets, allowing teams to version, branch, and share datasets, model weights, and experiments efficiently. It enables stakeholders across machine learning engineering, data science, product, and legal teams to review, edit, and collaborate on data within a unified workflow. Users can query, modify, and manage datasets through an intuitive web interface, command line tools, or a Python library, making it flexible for different technical workflows. Oxen.ai supports the full AI lifecycle by allowing teams to curate datasets, fine-tune models, and deploy them at scale while maintaining full ownership and traceability.Starting Price: $30 per month -
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Waifu Diffusion
Waifu Diffusion
Waifu Diffusion is an AI image model that creates anime images from text descriptions. It's based on the Stable Diffusion model, which is a latent text-to-image model. Waifu Diffusion is trained on a large number of high-quality anime images. Waifu Diffusion can be used for entertainment purposes and as a generative art assistant. It continuously learns from user feedback, fine-tuning its image generation process. This iterative approach ensures that the model adapts and improves over time, enhancing the quality and accuracy of the generated waifus.Starting Price: Free -
44
Syncfusion Essential Studio
Syncfusion
Includes more than 1,600 components and frameworks for Windows Forms, WPF, ASP.NET (Web Forms, MVC, Core), UWP, WinUI, Xamarin, Flutter, JavaScript, Angular, Blazor, Vue and React. Includes top requested components such as charts, grids, schedulers, diagrams, maps, gauges, docking, ribbons, and many more! Working with the industry’s best and brightest minds to streamline your business. Includes more than 1,700 components and frameworks for major platforms. A wide range of product demos and training, including video tutorials, documentation, and KBs. Every control is fine-tuned to work with a high volume of data. Create powerful apps by viewing and editing Excel, PDF, Word, and PowerPoint files. Truly unlimited dedicated support system via the public forum, feature & feedback page, live chat, and support tickets. Easy integration of tools to blend Syncfusion controls with your project. -
45
Amazon SageMaker Clarify
Amazon
Amazon SageMaker Clarify provides machine learning (ML) developers with purpose-built tools to gain greater insights into their ML training data and models. SageMaker Clarify detects and measures potential bias using a variety of metrics so that ML developers can address potential bias and explain model predictions. SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model. For instance, you can check for bias related to age in your dataset or in your trained model and receive a detailed report that quantifies different types of potential bias. SageMaker Clarify also includes feature importance scores that help you explain how your model makes predictions and produces explainability reports in bulk or real time through online explainability. You can use these reports to support customer or internal presentations or to identify potential issues with your model. -
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Google Cloud AutoML
Google
Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology. Cloud AutoML leverages more than 10 years of proprietary Google Research technology to help your machine learning models achieve faster performance and more accurate predictions. Use Cloud AutoML’s simple graphical user interface to train, evaluate, improve, and deploy models based on your data. You’re only a few minutes away from your own custom machine learning model. Google’s human labeling service can put a team of people to work annotating or cleaning your labels to make sure your models are being trained on high-quality data. -
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Tencent Cloud Face Recognition
Tencent
Based on the 3rd generation Tencent YouTu Grandmother Model, Face Recognition was optimized by various training methods such as metric learning, transfer learning, and multi-task learning. Its customized fine-tuning or distilling models can meet performance and latency requirements in different use cases. Face Recognition was tested by Tencent products' massive user base and complex use cases and is proven to be highly reliable and robust with an over 99.9% availability rate. Face Recognition features high concurrence, high throughput, and low latency. It processes millions of faces in just a few hundred milliseconds, satisfying your real-time usage demands. Face Recognition is used in a wide variety of use cases, such as online photo albums, smart retail, security surveillance, face recognition access control and attendance, face recognition login, face effects, online proctoring and more. -
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Amazon SageMaker Model Training reduces the time and cost to train and tune machine learning (ML) models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, SageMaker distributed training libraries can automatically split large models and training datasets across AWS GPU instances, or you can use third-party libraries, such as DeepSpeed, Horovod, or Megatron. Efficiently manage system resources with a wide choice of GPUs and CPUs including P4d.24xl instances, which are the fastest training instances currently available in the cloud. Specify the location of data, indicate the type of SageMaker instances, and get started with a single click.
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Gaia
Gaia
Train, deploy, and commercialize your neural machine translator with just a few clicks, no coding required. Upload your parallel data CSV file with a simple drag-and-drop interface. Fine-tune your model with advanced settings for optimal performance. Start training instantly with our powerful NVIDIA GPU infrastructure. Train models for a wide range of language pairs, including low-resource languages. Track training progress and performance metrics in real time. Easily integrate your trained model with our comprehensive API. Configure your model parameters and hyperparameters. Upload your parallel data CSV file to the dashboard. Review training metrics and BLEU scores. Use your deployed model via dashboard or API. Click "start training" and let our GPUs do the work. It's often beneficial to start with default values and then experiment with different configurations. Keep track of your experiments and their results to find the optimal settings for your specific translation task. -
50
Instill Core
Instill AI
Instill Core is an all-in-one AI infrastructure tool for data, model, and pipeline orchestration, streamlining the creation of AI-first applications. Access is easy via Instill Cloud or by self-hosting from the instill-core GitHub repository. Instill Core includes: Instill VDP: The Versatile Data Pipeline (VDP), designed for unstructured data ETL challenges, providing robust pipeline orchestration. Instill Model: An MLOps/LLMOps platform that ensures seamless model serving, fine-tuning, and monitoring for optimal performance with unstructured data ETL. Instill Artifact: Facilitates data orchestration for unified unstructured data representation. Instill Core simplifies the development and management of sophisticated AI workflows, making it indispensable for developers and data scientists leveraging AI technologies.Starting Price: $19/month/user
