Alternatives to Create ML
Compare Create ML alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Create ML in 2025. Compare features, ratings, user reviews, pricing, and more from Create ML competitors and alternatives in order to make an informed decision for your business.
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1
Vertex AI
Google
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex. -
2
RunPod
RunPod
RunPod offers a cloud-based platform designed for running AI workloads, focusing on providing scalable, on-demand GPU resources to accelerate machine learning (ML) model training and inference. With its diverse selection of powerful GPUs like the NVIDIA A100, RTX 3090, and H100, RunPod supports a wide range of AI applications, from deep learning to data processing. The platform is designed to minimize startup time, providing near-instant access to GPU pods, and ensures scalability with autoscaling capabilities for real-time AI model deployment. RunPod also offers serverless functionality, job queuing, and real-time analytics, making it an ideal solution for businesses needing flexible, cost-effective GPU resources without the hassle of managing infrastructure. -
3
CoreWeave
CoreWeave
CoreWeave is a cloud infrastructure provider specializing in GPU-based compute solutions tailored for AI workloads. The platform offers scalable, high-performance GPU clusters that optimize the training and inference of AI models, making it ideal for industries like machine learning, visual effects (VFX), and high-performance computing (HPC). CoreWeave provides flexible storage, networking, and managed services to support AI-driven businesses, with a focus on reliability, cost efficiency, and enterprise-grade security. The platform is used by AI labs, research organizations, and businesses to accelerate their AI innovations. -
4
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 -
5
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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6
3LC
3LC
Light up the black box and pip install 3LC to gain the clarity you need to make meaningful changes to your models in moments. Remove the guesswork from your model training and iterate fast. Collect per-sample metrics and visualize them in your browser. Analyze your training and eliminate issues in your dataset. Model-guided, interactive data debugging and enhancements. Find important or inefficient samples. Understand what samples work and where your model struggles. Improve your model in different ways by weighting your data. Make sparse, non-destructive edits to individual samples or in a batch. Maintain a lineage of all changes and restore any previous revisions. Dive deeper than standard experiment trackers with per-sample per epoch metrics and data tracking. Aggregate metrics by sample features, rather than just epoch, to spot hidden trends. Tie each training run to a specific dataset revision for full reproducibility. -
7
CentML
CentML
CentML accelerates Machine Learning workloads by optimizing models to utilize hardware accelerators, like GPUs or TPUs, more efficiently and without affecting model accuracy. Our technology boosts training and inference speed, lowers compute costs, increases your AI-powered product margins, and boosts your engineering team's productivity. Software is no better than the team who built it. Our team is stacked with world-class machine learning and system researchers and engineers. Focus on your AI products and let our technology take care of optimum performance and lower cost for you. -
8
AWS Neuron
Amazon Web Services
It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP). -
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FinetuneFast
FinetuneFast
FinetuneFast is your ultimate solution for finetuning AI models and deploying them quickly to start making money online with ease. Here are the key features that make FinetuneFast stand out: - Finetune your ML models in days, not weeks - The ultimate ML boilerplate for text-to-image, LLMs, and more - Build your first AI app and start earning online fast - Pre-configured training scripts for efficient model training - Efficient data loading pipelines for streamlined data processing - Hyperparameter optimization tools for improved model performance - Multi-GPU support out of the box for enhanced processing power - No-Code AI model finetuning for easy customization - One-click model deployment for quick and hassle-free deployment - Auto-scaling infrastructure for seamless scaling as your models grow - API endpoint generation for easy integration with other systems - Monitoring and logging setup for real-time performance tracking -
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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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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. -
12
Perception Platform
Intuition Machines
The Perception Platform by Intuition Machines automates the entire lifecycle of machine learning models—from training to deployment and continuous improvement. Featuring advanced active learning, the platform enables models to evolve by learning from new data and human interaction, enhancing accuracy while reducing manual oversight. Robust APIs facilitate seamless integration with existing systems, making it scalable and easy to adopt across diverse AI/ML applications. -
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neptune.ai
neptune.ai
Neptune.ai is a machine learning operations (MLOps) platform designed to streamline the tracking, organizing, and sharing of experiments and model-building processes. It provides a comprehensive environment for data scientists and machine learning engineers to log, visualize, and compare model training runs, datasets, hyperparameters, and metrics in real-time. Neptune.ai integrates easily with popular machine learning libraries, enabling teams to efficiently manage both research and production workflows. With features that support collaboration, versioning, and experiment reproducibility, Neptune.ai enhances productivity and helps ensure that machine learning projects are transparent and well-documented across their lifecycle.Starting Price: $49 per month -
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Accelerate your deep learning workload. Speed your time to value with AI model training and inference. With advancements in compute, algorithm and data access, enterprises are adopting deep learning more widely to extract and scale insight through speech recognition, natural language processing and image classification. Deep learning can interpret text, images, audio and video at scale, generating patterns for recommendation engines, sentiment analysis, financial risk modeling and anomaly detection. High computational power has been required to process neural networks due to the number of layers and the volumes of data to train the networks. Furthermore, businesses are struggling to show results from deep learning experiments implemented in silos.
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Alibaba Cloud Machine Learning Platform for AI
Alibaba Cloud
An end-to-end platform that provides various machine learning algorithms to meet your data mining and analysis requirements. Machine Learning Platform for AI provides end-to-end machine learning services, including data processing, feature engineering, model training, model prediction, and model evaluation. Machine learning platform for AI combines all of these services to make AI more accessible than ever. Machine Learning Platform for AI provides a visualized web interface allowing you to create experiments by dragging and dropping different components to the canvas. Machine learning modeling is a simple, step-by-step procedure, improving efficiencies and reducing costs when creating an experiment. Machine Learning Platform for AI provides more than one hundred algorithm components, covering such scenarios as regression, classification, clustering, text analysis, finance, and time series.Starting Price: $1.872 per hour -
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Kraken
Big Squid
Kraken is for everyone from analysts to data scientists. Built to be the easiest-to-use, no-code automated machine learning platform. The Kraken no-code automated machine learning (AutoML) platform simplifies and automates data science tasks like data prep, data cleaning, algorithm selection, model training, and model deployment. Kraken was built with analysts and engineers in mind. If you've done data analysis before, you're ready! Kraken's no-code, easy-to-use interface and integrated SONAR© training make it easy to become a citizen data scientist. Advanced features allow data scientists to work faster and more efficiently. Whether you use Excel or flat files for day-to-day reporting or just ad-hoc analysis and exports, drag-and-drop CSV upload and the Amazon S3 connector in Kraken make it easy to start building models with a few clicks. Data Connectors in Kraken allow you to connect to your favorite data warehouse, business intelligence tools, and cloud storage.Starting Price: $100 per month -
17
Intel Tiber AI Studio
Intel
Intel® Tiber™ AI Studio is a comprehensive machine learning operating system that unifies and simplifies the AI development process. The platform supports a wide range of AI workloads, providing a hybrid and multi-cloud infrastructure that accelerates ML pipeline development, model training, and deployment. With its native Kubernetes orchestration and meta-scheduler, Tiber™ AI Studio offers complete flexibility in managing on-prem and cloud resources. Its scalable MLOps solution enables data scientists to easily experiment, collaborate, and automate their ML workflows while ensuring efficient and cost-effective utilization of resources. -
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PyTorch
PyTorch
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies. -
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Roboflow
Roboflow
Roboflow has everything you need to build and deploy computer vision models. Connect Roboflow at any step in your pipeline with APIs and SDKs, or use the end-to-end interface to automate the entire process from image to inference. Whether you’re in need of data labeling, model training, or model deployment, Roboflow gives you building blocks to bring custom computer vision solutions to your business.Starting Price: $250/month -
20
ML.NET
Microsoft
ML.NET is a free, open source, and cross-platform machine learning framework designed for .NET developers to build custom machine learning models using C# or F# without leaving the .NET ecosystem. It supports various machine learning tasks, including classification, regression, clustering, anomaly detection, and recommendation systems. ML.NET integrates with other popular ML frameworks like TensorFlow and ONNX, enabling additional scenarios such as image classification and object detection. It offers tools like Model Builder and the ML.NET CLI, which utilize Automated Machine Learning (AutoML) to simplify the process of building, training, and deploying high-quality models. These tools automatically explore different algorithms and settings to find the best-performing model for a given scenario.Starting Price: Free -
21
V7 Darwin
V7
V7 Darwin is a powerful AI-driven platform for labeling and training data that streamlines the process of annotating images, videos, and other data types. By using AI-assisted tools, V7 Darwin enables faster, more accurate labeling for a variety of use cases such as machine learning model training, object detection, and medical imaging. The platform supports multiple types of annotations, including keypoints, bounding boxes, and segmentation masks. It integrates with various workflows through APIs, SDKs, and custom integrations, making it an ideal solution for businesses seeking high-quality data for their AI projects.Starting Price: $150 -
22
Flyte
Union.ai
The workflow automation platform for complex, mission-critical data and ML processes at scale. Flyte makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing. Flyte is used in production at Lyft, Spotify, Freenome, and others. At Lyft, Flyte has been serving production model training and data processing for over four years, becoming the de-facto platform for teams like pricing, locations, ETA, mapping, autonomous, and more. In fact, Flyte manages over 10,000 unique workflows at Lyft, totaling over 1,000,000 executions every month, 20 million tasks, and 40 million containers. Flyte has been battle-tested at Lyft, Spotify, Freenome, and others. It is entirely open-source with an Apache 2.0 license under the Linux Foundation with a cross-industry overseeing committee. Configuring machine learning and data workflows can get complex and error-prone with YAML.Starting Price: Free -
23
Deepgram
Deepgram
Deploy accurate speech recognition at scale while continuously improving model performance by labeling data and training from a single console. We deliver state-of-the-art speech recognition and understanding at scale. We do it by providing cutting-edge model training and data-labeling alongside flexible deployment options. Our platform recognizes multiple languages, accents, and words, dynamically tuning to the needs of your business with every training session. The fastest, most accurate, most reliable, most scalable speech transcription, with understanding — rebuilt just for enterprise. We’ve reinvented ASR with 100% deep learning that allows companies to continuously improve accuracy. Stop waiting for the big tech players to improve their software and forcing your developers to manually boost accuracy with keywords in every API call. Start training your speech model and reaping the benefits in weeks, not months or years.Starting Price: $0 -
24
Apache Mahout
Apache Software Foundation
Apache Mahout is a powerful, scalable, and versatile machine learning library designed for distributed data processing. It offers a comprehensive set of algorithms for various tasks, including classification, clustering, recommendation, and pattern mining. Built on top of the Apache Hadoop ecosystem, Mahout leverages MapReduce and Spark to enable data processing on large-scale datasets. Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end or can be extended to other distributed backends. Matrix computations are a fundamental part of many scientific and engineering applications, including machine learning, computer vision, and data analysis. Apache Mahout is designed to handle large-scale data processing by leveraging the power of Hadoop and Spark. -
25
Huawei Cloud ModelArts
Huawei Cloud
ModelArts is a comprehensive AI development platform provided by Huawei Cloud, designed to streamline the entire AI workflow for developers and data scientists. It offers a full-lifecycle toolchain that includes data preprocessing, semi-automated data labeling, distributed training, automated model building, and flexible deployment options across cloud, edge, and on-premises environments. It supports popular open source AI frameworks such as TensorFlow, PyTorch, and MindSpore, and allows for the integration of custom algorithms tailored to specific needs. ModelArts features an end-to-end development pipeline that enhances collaboration across DataOps, MLOps, and DevOps, boosting development efficiency by up to 50%. It provides cost-effective AI computing resources with diverse specifications, enabling large-scale distributed training and inference acceleration. -
26
Modelbit
Modelbit
Don't change your day-to-day, works with Jupyter Notebooks and any other Python environment. Simply call modelbi.deploy to deploy your model, and let Modelbit carry it — and all its dependencies — to production. ML models deployed with Modelbit can be called directly from your warehouse as easily as calling a SQL function. They can also be called as a REST endpoint directly from your product. Modelbit is backed by your git repo. GitHub, GitLab, or home grown. Code review. CI/CD pipelines. PRs and merge requests. Bring your whole git workflow to your Python ML models. Modelbit integrates seamlessly with Hex, DeepNote, Noteable and more. Take your model straight from your favorite cloud notebook into production. Sick of VPC configurations and IAM roles? Seamlessly redeploy your SageMaker models to Modelbit. Immediately reap the benefits of Modelbit's platform with the models you've already built. -
27
Nebius
Nebius
Training-ready platform with NVIDIA® H100 Tensor Core GPUs. Competitive pricing. Dedicated support. Built for large-scale ML workloads: Get the most out of multihost training on thousands of H100 GPUs of full mesh connection with latest InfiniBand network up to 3.2Tb/s per host. Best value for money: Save at least 50% on your GPU compute compared to major public cloud providers*. Save even more with reserves and volumes of GPUs. Onboarding assistance: We guarantee a dedicated engineer support to ensure seamless platform adoption. Get your infrastructure optimized and k8s deployed. Fully managed Kubernetes: Simplify the deployment, scaling and management of ML frameworks on Kubernetes and use Managed Kubernetes for multi-node GPU training. Marketplace with ML frameworks: Explore our Marketplace with its ML-focused libraries, applications, frameworks and tools to streamline your model training. Easy to use. We provide all our new users with a 1-month trial period.Starting Price: $2.66/hour -
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MXNet
The Apache Software Foundation
A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed. Scalable distributed training and performance optimization in research and production is enabled by the dual parameter server and Horovod support. Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl. A thriving ecosystem of tools and libraries extends MXNet and enables use-cases in computer vision, NLP, time series and more. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision-making process have stabilized in a manner consistent with other successful ASF projects. Join the MXNet scientific community to contribute, learn, and get answers to your questions. -
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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. -
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NVIDIA NeMo
NVIDIA
NVIDIA NeMo LLM is a service that provides a fast path to customizing and using large language models trained on several frameworks. Developers can deploy enterprise AI applications using NeMo LLM on private and public clouds. They can also experience Megatron 530B—one of the largest language models—through the cloud API or experiment via the LLM service. Customize your choice of various NVIDIA or community-developed models that work best for your AI applications. Within minutes to hours, get better responses by providing context for specific use cases using prompt learning techniques. Leverage the power of NVIDIA Megatron 530B, one of the largest language models, through the NeMo LLM Service or the cloud API. Take advantage of models for drug discovery, including in the cloud API and NVIDIA BioNeMo framework. -
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alwaysAI
alwaysAI
alwaysAI provides developers with a simple and flexible way to build, train, and deploy computer vision applications to a wide variety of IoT devices. Select from a catalog of deep learning models or upload your own. Use our flexible and customizable APIs to quickly enable core computer vision services. Quickly prototype, test and iterate with a variety of camera-enabled ARM-32, ARM-64 and x86 devices. Identify objects in an image by name or classification. Identify and count objects appearing in a real-time video feed. Follow the same object across a series of frames. Find faces or full bodies in a scene to count or track. Locate and define borders around separate objects. Separate key objects in an image from background visuals. Determine human body poses, fall detection, emotions. Use our model training toolkit to train an object detection model to identify virtually any object. Create a model tailored to your specific use-case. -
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Gensim
Radim Řehůřek
Gensim is a free, open source Python library designed for unsupervised topic modeling and natural language processing, focusing on large-scale semantic modeling. It enables the training of models like Word2Vec, FastText, Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), facilitating the representation of documents as semantic vectors and the discovery of semantically related documents. Gensim is optimized for performance with highly efficient implementations in Python and Cython, allowing it to process arbitrarily large corpora using data streaming and incremental algorithms without loading the entire dataset into RAM. It is platform-independent, running on Linux, Windows, and macOS, and is licensed under the GNU LGPL, promoting both personal and commercial use. The library is widely adopted, with thousands of companies utilizing it daily, over 2,600 academic citations, and more than 1 million downloads per week.Starting Price: Free -
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Chooch
Chooch
Chooch is an industry-leading, full lifecycle AI-powered computer vision platform that detects visuals, objects, and actions in video images and responds with pre-programmed actions using customizable alerts. It services the entire machine learning AI workflow from data augmentation tools, model training and hosting, edge device deployment, real-time inferencing, and smart analytics. This provides organizations with the ability to apply computer vision in the broadest variety of use cases from a single platform. Chooch AI Vision can be deployed quickly with ReadyNow models for the most common use cases like fall detection and workplace safety, face recognition, demographics, weapon detection, and more. Using existing cameras and edge infrastructure, models can be deployed to video streams detecting patterns and anomalies and witness real-time insights in seconds.Starting Price: Free -
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VirtuousAI VirtueStack
VirtuousAI
VirtuousAI offers a comprehensive AI platform, VirtueStack™, designed to help businesses implement and scale AI solutions quickly. With a focus on explainability, security, and ease of use, VirtuousAI enables businesses to launch AI models in as little as 90 days. The platform features advanced tools for data management, knowledge graph architecture, and model training, all while ensuring transparency and compliance. Whether you're just starting with AI or looking to scale, VirtuousAI provides a turnkey solution to accelerate time-to-value and improve business outcomes. -
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Distributed AI is a computing paradigm that bypasses the need to move vast amounts of data and provides the ability to analyze data at the source. Distributed AI APIs built by IBM Research is a set of RESTful web services with data and AI algorithms to support AI applications across hybrid cloud, distributed, and edge computing environments. Each Distributed AI API addresses the challenges in enabling AI in distributed and edge environments with APIs. The Distributed AI APIs do not focus on the basic requirements of creating and deploying AI pipelines, for example, model training and model serving. You would use your favorite open-source packages such as TensorFlow or PyTorch. Then, you can containerize your application, including the AI pipeline, and deploy these containers at the distributed locations. In many cases, it’s useful to use a container orchestrator such as Kubernetes or OpenShift operators to automate the deployment process.
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NeevCloud
NeevCloud
NeevCloud delivers cutting-edge GPU cloud solutions powered by NVIDIA GPUs like the H200, H100, GB200 NVL72, and many more offering unmatched performance for AI, HPC, and data-intensive workloads. Scale dynamically with flexible pricing and energy-efficient GPUs that reduce costs while maximizing output. Ideal for AI model training, scientific research, media production, and real-time analytics, NeevCloud ensures seamless integration and global accessibility. Experience unparalleled speed, scalability, and sustainability with NeevCloud GPU cloud solutions.Starting Price: $1.69/GPU/hour -
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TensorWave
TensorWave
TensorWave is an AI and high-performance computing (HPC) cloud platform purpose-built for performance, powered exclusively by AMD Instinct Series GPUs. It delivers high-bandwidth, memory-optimized infrastructure that scales with your most demanding models, training, or inference. TensorWave offers access to AMD’s top-tier GPUs within seconds, including the MI300X and MI325X accelerators, which feature industry-leading memory capacity and bandwidth, with up to 256GB of HBM3E supporting 6.0TB/s. TensorWave's architecture includes UEC-ready capabilities that optimize the next generation of Ethernet for AI and HPC networking, and direct liquid cooling that delivers exceptional total cost of ownership with up to 51% data center energy cost savings. TensorWave provides high-speed network storage, ensuring game-changing performance, security, and scalability for AI pipelines. It offers plug-and-play compatibility with a wide range of tools and platforms, supporting models, libraries, etc. -
38
Automaton AI
Automaton AI
With Automaton AI’s ADVIT, create, manage and develop high-quality training data and DNN models all in one place. Optimize the data automatically and prepare it for each phase of the computer vision pipeline. Automate the data labeling processes and streamline data pipelines in-house. Manage the structured and unstructured video/image/text datasets in runtime and perform automatic functions that refine your data in preparation for each step of the deep learning pipeline. Upon accurate data labeling and QA, you can train your own model. DNN training needs hyperparameter tuning like batch size, learning, rate, etc. Optimize and transfer learning on trained models to increase accuracy. Post-training, take the model to production. ADVIT also does model versioning. Model development and accuracy parameters can be tracked in run-time. Increase the model accuracy with a pre-trained DNN model for auto-labeling. -
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C3 AI Suite
C3.ai
Build, deploy, and operate Enterprise AI applications. The C3 AI® Suite uses a unique model-driven architecture to accelerate delivery and reduce the complexities of developing enterprise AI applications. The C3 AI model-driven architecture provides an “abstraction layer,” that allows developers to build enterprise AI applications by using conceptual models of all the elements an application requires, instead of writing lengthy code. This provides significant benefits: Use AI applications and models that optimize processes for every product, asset, customer, or transaction across all regions and businesses. Deploy AI applications and see results in 1-2 quarters – rapidly roll out additional applications and new capabilities. Unlock sustained value – hundreds of millions to billions of dollars per year – from reduced costs, increased revenue, and higher margins. Ensure systematic, enterprise-wide governance of AI with C3.ai’s unified platform that offers data lineage and governance. -
40
Accord.NET Framework
Accord.NET Framework
The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive documentation and wiki helps fill in the details. -
41
DeepSpeed
Microsoft
DeepSpeed is an open source deep learning optimization library for PyTorch. It's designed to reduce computing power and memory use, and to train large distributed models with better parallelism on existing computer hardware. DeepSpeed is optimized for low latency, high throughput training. DeepSpeed can train DL models with over a hundred billion parameters on the current generation of GPU clusters. It can also train up to 13 billion parameters in a single GPU. DeepSpeed is developed by Microsoft and aims to offer distributed training for large-scale models. It's built on top of PyTorch, which specializes in data parallelism.Starting Price: Free -
42
01.AI
01.AI
01.AI offers a comprehensive AI/ML model deployment platform that simplifies the process of training, deploying, and managing machine learning models at scale. It provides powerful tools for businesses to integrate AI into their operations with minimal technical complexity. 01.AI supports end-to-end AI solutions, including model training, fine-tuning, inference, and monitoring. 01. AI's services help businesses optimize their AI workflows, allowing teams to focus on model performance rather than infrastructure. It is designed to support various industries, including finance, healthcare, and manufacturing, offering scalable solutions that enhance decision-making and automate complex tasks. -
43
Baidu Qianfan
Baidu
One-stop enterprise-level large model platform, providing advanced generation AI production and application process development toolchain. Provides data labels, model training and evaluation, reasoning services, and application-integrated comprehensive functional services. Training and reasoning performance greatly improved. Perfect authentication and flow control safety mechanism, self-proclaimed content review and sensitive word filtering, multi-safety mechanism escort enterprise application. Extensive and mature practice landed, building the next generation of smart applications. Online quick test service effect, convenient smart cloud reasoning service. One-stop model customization, full process visualization operation. Large model of knowledge enhancement, unified paradigm to support multi-category downstream tasks. An advanced parallel strategy that supports large model training, compression, and deployment. -
44
Horovod
Horovod
Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve.Starting Price: Free -
45
Neutone Morpho
Neutone
We’re pleased to present Neutone Morpho, a real-time tone morphing plugin. Our cutting-edge machine-learning technology can transform any sound into something new and inspiring. Neutone Morpho directly processes audio, capturing even the subtlest details from your input. With our pre-trained AI models, you can transform any incoming audio into the characteristics, or “style”, of the sounds that the model is based on. In real-time. Sometimes this leads to surprising outcomes. At the core of Neutone Morpho are the Morpho AI models, where the magic happens. You can interact with a loaded Morpho model in two modes to influence the tone-morphing process. We're giving you a fully working version for free to test out. There is no time limit, so feel free to play around with it as much as you want. If you enjoy it and want to use more models or try out custom model training, go ahead and upgrade to the full version.Starting Price: $99 one-time payment -
46
Aquarium
Aquarium
Aquarium's embedding technology surfaces the biggest problems in your model performance and finds the right data to solve them. Unlock the power of neural network embeddings without worrying about maintaining infrastructure or debugging embedding models. Automatically find the most critical patterns of model failures in your dataset. Understand the long tail of edge cases and triage which issues to solve first. Trawl through massive unlabeled datasets to find edge-case scenarios. Bootstrap new classes with a handful of examples using few-shot learning technology. The more data you have, the more value we offer. Aquarium reliably scales to datasets containing hundreds of millions of data points. Aquarium offers solutions engineering resources, customer success syncs, and user training to help customers get value. We also offer an anonymous mode for organizations who want to use Aquarium without exposing any sensitive data.Starting Price: $1,250 per month -
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Centific
Centific
Centific’s frontier AI data foundry platform, powered by NVIDIA edge computing, is purpose-built to accelerate AI deployments by increasing flexibility, security, and scalability through comprehensive workflow orchestration. It centralizes AI project management in a unified AI Workbench, overseeing pipelines, model training, deployment, and reporting within a single, streamlined environment, while it handles data ingestion, preprocessing, and transformation. RAG Studio simplifies retrieval-augmented generation workflows, the Product Catalog organizes reusable assets, and Safe AI Studio embeds built-in safeguards to ensure compliance, reduce hallucinations, and protect sensitive data. Its plugin-based modular architecture supports both PaaS and SaaS models with metering to monitor consumption, and a centralized model catalog offers version control, compliance checks, and flexible deployment options. -
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NetApp AIPod
NetApp
NetApp AIPod is a comprehensive AI infrastructure solution designed to streamline the deployment and management of artificial intelligence workloads. By integrating NVIDIA-validated turnkey solutions, such as NVIDIA DGX BasePOD™ and NetApp's cloud-connected all-flash storage, AIPod consolidates analytics, training, and inference capabilities into a single, scalable system. This convergence enables organizations to rapidly implement AI workflows, from model training to fine-tuning and inference, while ensuring robust data management and security. With preconfigured infrastructure optimized for AI tasks, NetApp AIPod reduces complexity, accelerates time to insights, and supports seamless integration into hybrid cloud environments. -
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ML Console
ML Console
ML Console is a web-based application that enables users to build powerful machine learning models without writing a single line of code. Designed for accessibility, it allows individuals from various backgrounds, including marketing professionals, e-commerce store owners, and larger enterprises, to create AI models in less than a minute. It operates entirely within the user's browser, ensuring that data remains local and secure. By leveraging modern web technologies like WebAssembly and WebGL, ML Console achieves training speeds comparable to traditional Python-based methods. Its user-friendly interface simplifies the machine learning process, making it approachable for users with no advanced AI expertise. Additionally, ML Console is free to use, eliminating barriers to entry for those interested in exploring machine learning solutions. Starting Price: Free -
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Nendo
Nendo
Nendo is the AI audio tool suite that allows you to effortlessly develop & use audio apps that amplify efficiency & creativity across all aspects of audio production. Time-consuming issues with machine learning and audio processing code are a thing of the past. AI is a transformative leap for audio production, amplifying efficiency and creativity in industries where audio is key. But building custom AI Audio solutions and operating them at scale is challenging. Nendo cloud empowers developers and businesses to seamlessly deploy Nendo applications, utilize premium AI audio models through APIs, and efficiently manage workloads at scale. From batch processing, model training, and inference to library management, and beyond - Nendo cloud is your solution.