Alternatives to Graphcore

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

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

Vercel combines the best in developer experience with a laser-focused focus on end-user performance. Our platform allows frontend teams to do their best work. Next.js is a React framework Vercel created with Google and Facebook. It's loved by developers. Next.js powers some of the most popular websites, including Twilio and Washington Post. It is used for news, e-commerce and travel. Vercel is the best place for any frontend app to be deployed. Start by connecting to our global edge network with zero configuration. Scale dynamically to millions upon millions of pages without breaking a sweat. Live editing for your UI components. Connect your pages to any data source or headless CMS and make them work in every dev environment. All of our cloud primitives, from caching to Serverless functions, work perfectly on localhost.

Amazon SageMaker is a comprehensive machine learning platform that integrates powerful tools for model building, training, and deployment in one cohesive environment. It combines data processing, AI model development, and collaboration features, allowing teams to streamline the development of custom AI applications. With SageMaker, users can easily access data stored across Amazon S3 data lakes and Amazon Redshift data warehouses, facilitating faster insights and AI model development. It also supports generative AI use cases, enabling users to develop and scale applications with cutting-edge AI technologies. The platform’s governance and security features ensure that data and models are handled with precision and compliance throughout the entire ML lifecycle. Furthermore, SageMaker provides a unified development studio for real-time collaboration, speeding up data discovery and model deployment.

SambaNova is the leading purpose-built AI system for generative and agentic AI implementations, from chips to models, that gives enterprises full control over their model and private data. We take the best models, optimize them for fast tokens and higher batch sizes, the largest inputs and enable customizations to deliver value with simplicity. The full suite includes the SambaNova DataScale system, the SambaStudio software, and the innovative SambaNova Composition of Experts (CoE) model architecture. These components combine into a powerful platform that delivers unparalleled performance, ease of use, accuracy, data privacy, and the ability to power every use case across the world's largest organizations. At the heart of SambaNova innovation is the fourth generation SN40L Reconfigurable Dataflow Unit (RDU). Purpose built for AI workloads, the SN40L RDU takes advantage of a dataflow architecture and a three-tiered memory design. The dataflow architecture eliminates the challenges that GPUs have with high performance inference. The three tiers of memory enable the platform to run hundreds of models on a single node and to switch between them in microseconds. We give our customers the optionality to experience through the cloud or on-premise.

TensorFlow is a comprehensive open-source machine learning platform that covers the entire process from development to deployment. This platform boasts a rich and adaptable ecosystem featuring various tools, libraries, and community resources, empowering researchers to advance the field of machine learning while allowing developers to create and implement ML-powered applications with ease. With intuitive high-level APIs like Keras and support for eager execution, users can effortlessly build and refine ML models, facilitating quick iterations and simplifying debugging. The flexibility of TensorFlow allows for seamless training and deployment of models across various environments, whether in the cloud, on-premises, within browsers, or directly on devices, regardless of the programming language utilized. Its straightforward and versatile architecture supports the transformation of innovative ideas into practical code, enabling the development of cutting-edge models that can be published swiftly. Overall, TensorFlow provides a powerful framework that encourages experimentation and accelerates the machine learning process.

NVIDIA AI Enterprise serves as the software backbone of the NVIDIA AI platform, enhancing the data science workflow and facilitating the development and implementation of various AI applications, including generative AI, computer vision, and speech recognition. Featuring over 50 frameworks, a range of pretrained models, and an array of development tools, NVIDIA AI Enterprise aims to propel businesses to the forefront of AI innovation while making the technology accessible to all enterprises. As artificial intelligence and machine learning have become essential components of nearly every organization's competitive strategy, the challenge of managing fragmented infrastructure between cloud services and on-premises data centers has emerged as a significant hurdle. Effective AI implementation necessitates that these environments be treated as a unified platform, rather than isolated computing units, which can lead to inefficiencies and missed opportunities. Consequently, organizations must prioritize strategies that promote integration and collaboration across their technological infrastructures to fully harness AI's potential.

Businesses now have numerous options to efficiently train their deep learning and machine learning models without breaking the bank. AI accelerators cater to various scenarios, providing solutions that range from economical inference to robust training capabilities. Getting started is straightforward, thanks to an array of services designed for both development and deployment purposes. Custom-built ASICs known as Tensor Processing Units (TPUs) are specifically designed to train and run deep neural networks with enhanced efficiency. With these tools, organizations can develop and implement more powerful and precise models at a lower cost, achieving faster speeds and greater scalability. A diverse selection of NVIDIA GPUs is available to facilitate cost-effective inference or to enhance training capabilities, whether by scaling up or by expanding out. Furthermore, by utilizing RAPIDS and Spark alongside GPUs, users can execute deep learning tasks with remarkable efficiency. Google Cloud allows users to run GPU workloads while benefiting from top-tier storage, networking, and data analytics technologies that improve overall performance. Additionally, when initiating a VM instance on Compute Engine, users can leverage CPU platforms, which offer a variety of Intel and AMD processors to suit different computational needs. This comprehensive approach empowers businesses to harness the full potential of AI while managing costs effectively.

Autogon stands out as a premier company in the realms of AI and machine learning, dedicated to demystifying advanced technology to provide businesses with innovative and accessible solutions that enhance data-informed decision-making and strengthen their competitive edge globally. Uncover the transformative capabilities of Autogon models, which enable various industries to tap into the advantages of AI, thereby promoting innovation and accelerating growth across a multitude of fields. Step into the future of artificial intelligence with Autogon Qore, a comprehensive solution offering image classification, text generation, visual question and answer, sentiment analysis, voice cloning, and much more. By adopting these advanced AI features, your business can thrive, facilitating informed decision-making and optimizing operations while minimizing the need for deep technical knowledge. Equip engineers, analysts, and scientists with the tools necessary to fully exploit the capabilities of artificial intelligence and machine learning in their initiatives and research endeavors. Furthermore, you can develop tailored software solutions using user-friendly APIs and integration SDKs, ensuring that your unique needs are met with precision. Embrace the potential of AI to not only enhance productivity but also to transform the way your organization approaches challenges and opportunities in the marketplace.

The Tencent Cloud TI Platform serves as a comprehensive machine learning service tailored for AI engineers, facilitating the AI development journey from data preprocessing all the way to model building, training, and evaluation, as well as deployment. This platform is preloaded with a variety of algorithm components and supports a range of algorithm frameworks, ensuring it meets the needs of diverse AI applications. By providing a seamless machine learning experience that encompasses the entire workflow, the Tencent Cloud TI Platform enables users to streamline the process from initial data handling to the final assessment of models. Additionally, it empowers even those new to AI to automatically construct their models, significantly simplifying the training procedure. The platform's auto-tuning feature further boosts the efficiency of parameter optimization, enabling improved model performance. Moreover, Tencent Cloud TI Platform offers flexible CPU and GPU resources that can adapt to varying computational demands, alongside accommodating different billing options, making it a versatile choice for users with diverse needs. This adaptability ensures that users can optimize costs while efficiently managing their machine learning workflows.

Create, execute, and oversee AI models while enhancing decision-making at scale across any cloud infrastructure. IBM Watson Studio enables you to implement AI seamlessly anywhere as part of the IBM Cloud Pak® for Data, which is the comprehensive data and AI platform from IBM. Collaborate across teams, streamline the management of the AI lifecycle, and hasten the realization of value with a versatile multicloud framework. You can automate the AI lifecycles using ModelOps pipelines and expedite data science development through AutoAI. Whether preparing or constructing models, you have the option to do so visually or programmatically. Deploying and operating models is made simple with one-click integration. Additionally, promote responsible AI governance by ensuring your models are fair and explainable to strengthen business strategies. Leverage open-source frameworks such as PyTorch, TensorFlow, and scikit-learn to enhance your projects. Consolidate development tools, including leading IDEs, Jupyter notebooks, JupyterLab, and command-line interfaces, along with programming languages like Python, R, and Scala. Through the automation of AI lifecycle management, IBM Watson Studio empowers you to build and scale AI solutions with an emphasis on trust and transparency, ultimately leading to improved organizational performance and innovation.

NeuroSplit is an innovative adaptive-inferencing technology that employs a unique method of "slicing" a neural network's connections in real time, resulting in the creation of two synchronized sub-models; one that processes initial layers locally on the user's device and another that offloads the subsequent layers to cloud GPUs. This approach effectively utilizes underused local computing power and can lead to a reduction in server expenses by as much as 60%, all while maintaining high levels of performance and accuracy. Incorporated within Skymel’s Orchestrator Agent platform, NeuroSplit intelligently directs each inference request across various devices and cloud environments according to predetermined criteria such as latency, cost, or resource limitations, and it automatically implements fallback mechanisms and model selection based on user intent to ensure consistent reliability under fluctuating network conditions. Additionally, its decentralized framework provides robust security features including end-to-end encryption, role-based access controls, and separate execution contexts, which contribute to a secure user experience. To further enhance its utility, NeuroSplit also includes real-time analytics dashboards that deliver valuable insights into key performance indicators such as cost, throughput, and latency, allowing users to make informed decisions based on comprehensive data. By offering a combination of efficiency, security, and ease of use, NeuroSplit positions itself as a leading solution in the realm of adaptive inference technologies.

Create your model in real-time as an interactive graph, enabling you to observe the data traversing through the visualized structure of your model. You can also modify the architecture at its most fundamental level. Graphbook offers complete transparency without hidden complexities, allowing you to see everything clearly. It performs live checks on data types and shapes, providing clear and comprehensible error messages that facilitate quick and efficient debugging. By eliminating the need to manage software dependencies and environmental setups, Graphbook enables you to concentrate on the architecture of your model and the flow of data while providing the essential computing resources. Cerbrec Graphbook serves as a visual integrated development environment (IDE) for AI modeling, simplifying what can often be a tedious development process into a more approachable experience. With an expanding community of machine learning practitioners and data scientists, Graphbook supports developers in fine-tuning language models like BERT and GPT, whether working with text or tabular data. Everything is seamlessly managed from the start, allowing you to visualize your model's behavior just as it will operate in practice, ensuring a smoother development journey. Additionally, the platform promotes collaboration by allowing users to share insights and techniques within the community.

Streamline the entire machine learning lifecycle from start to finish. Equip developers and data scientists with an extensive array of efficient tools for swiftly building, training, and deploying machine learning models. Enhance the speed of market readiness and promote collaboration among teams through leading-edge MLOps—akin to DevOps but tailored for machine learning. Drive innovation within a secure, reliable platform that prioritizes responsible AI practices. Cater to users of all expertise levels with options for both code-centric and drag-and-drop interfaces, along with automated machine learning features. Implement comprehensive MLOps functionalities that seamlessly align with existing DevOps workflows, facilitating the management of the entire machine learning lifecycle. Emphasize responsible AI by providing insights into model interpretability and fairness, securing data through differential privacy and confidential computing, and maintaining control over the machine learning lifecycle with audit trails and datasheets. Additionally, ensure exceptional compatibility with top open-source frameworks and programming languages such as MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R, thus broadening accessibility and usability for diverse projects. By fostering an environment that promotes collaboration and innovation, teams can achieve remarkable advancements in their machine learning endeavors.

DataChain serves as a bridge between unstructured data found in cloud storage and AI models alongside APIs, facilitating immediate data insights by utilizing foundational models and API interactions to swiftly analyze unstructured files stored in various locations. Its Python-centric framework significantly enhances development speed, enabling a tenfold increase in productivity by eliminating SQL data silos and facilitating seamless data manipulation in Python. Furthermore, DataChain prioritizes dataset versioning, ensuring traceability and complete reproducibility for every dataset, which fosters effective collaboration among team members while maintaining data integrity. The platform empowers users to conduct analyses right where their data resides, keeping raw data intact in storage solutions like S3, GCP, Azure, or local environments, while metadata can be stored in less efficient data warehouses. DataChain provides versatile tools and integrations that are agnostic to cloud environments for both data storage and computation. Additionally, users can efficiently query their unstructured multi-modal data, implement smart AI filters to refine datasets for training, and capture snapshots of their unstructured data along with the code used for data selection and any associated metadata. This capability enhances user control over data management, making it an invaluable asset for data-intensive projects.

The Intel® Distribution of OpenVINO™ toolkit serves as an open-source AI development resource that speeds up inference on various Intel hardware platforms. This toolkit is crafted to enhance AI workflows, enabling developers to implement refined deep learning models tailored for applications in computer vision, generative AI, and large language models (LLMs). Equipped with integrated model optimization tools, it guarantees elevated throughput and minimal latency while decreasing the model size without sacrificing accuracy. OpenVINO™ is an ideal choice for developers aiming to implement AI solutions in diverse settings, spanning from edge devices to cloud infrastructures, thereby assuring both scalability and peak performance across Intel architectures. Ultimately, its versatile design supports a wide range of AI applications, making it a valuable asset in modern AI development.

Ollama stands out as a cutting-edge platform that prioritizes the delivery of AI-driven tools and services, aimed at facilitating user interaction and the development of AI-enhanced applications. It allows users to run AI models directly on their local machines. By providing a diverse array of solutions, such as natural language processing capabilities and customizable AI functionalities, Ollama enables developers, businesses, and organizations to seamlessly incorporate sophisticated machine learning technologies into their operations. With a strong focus on user-friendliness and accessibility, Ollama seeks to streamline the AI experience, making it an attractive choice for those eager to leverage the power of artificial intelligence in their initiatives. This commitment to innovation not only enhances productivity but also opens doors for creative applications across various industries.

Natural Language Processing is Artificial Intelligence. It allows computers to understand and analyze human language. Pryon's AI can read, organize, and search in ways that were previously impossible for humans. This powerful ability is used in every interaction to both understand a request as well as to retrieve the correct response. The sophistication of the underlying natural languages technologies is directly related to the success of any NLP project. Your content can be used in chatbots, search engines, automations, and other ways. It must be broken down into pieces so that a user can find the exact answer, result, or snippet they are looking for. This can be done manually or by a specialist who breaks down information into intents or entities. Pryon automatically creates a dynamic model from your content to attach rich metadata to each piece. This model can be regenerated in a click when you add, modify or remove content.

PredictSense is an AI-powered machine learning platform that uses AutoML to power its end-to-end Machine Learning platform. Accelerating machine intelligence will fuel the technological revolution of tomorrow. AI is key to unlocking the value of enterprise data investments. PredictSense allows businesses to quickly create AI-driven advanced analytical solutions that can help them monetize their technology investments and critical data infrastructure. Data science and business teams can quickly develop and deploy robust technology solutions at scale. Integrate AI into your existing product ecosystem and quickly track GTM for new AI solution. AutoML's complex ML models allow you to save significant time, money and effort.

Access advanced generative AI models effortlessly through our auto-scaling APIs, requiring no management on your part. Now, models such as stable diffusion, pix2pix, and dreambooth can be utilized with just an API call. You can develop applications utilizing these generative AI models through our scalable REST APIs, which integrate smoothly and are significantly more affordable than other options available. Our system allows for seamless integration with your current infrastructure, eliminating the need for extensive development efforts. Our APIs can be easily incorporated into your workflow and support various tech stacks including CURL, Python, Node.js, and PHP. By tapping into the unused computing capacity of millions of decentralized cryptocurrency mining rigs around the globe, we enhance them for machine learning while pairing them with widely-used generative AI models like Stable Diffusion. This innovative approach not only provides a scalable and globally accessible platform for generative AI but also ensures it's cost-effective, empowering businesses to leverage powerful AI capabilities without breaking the bank. As a result, you'll be able to innovate more rapidly and efficiently in your projects.

Generative AI is transforming nearly every sector by opening up vast new avenues for knowledge and creative professionals to tackle some of the most pressing issues of our time. NVIDIA is at the forefront of this transformation, providing a robust array of cloud services, pre-trained foundation models, and leading-edge frameworks, along with optimized inference engines and APIs, to integrate intelligence into enterprise applications seamlessly. The NVIDIA AI Foundations suite offers cloud services that enhance generative AI capabilities at the enterprise level, allowing for tailored solutions in diverse fields such as text processing (NVIDIA NeMo™), visual content creation (NVIDIA Picasso), and biological research (NVIDIA BioNeMo™). By leveraging the power of NeMo, Picasso, and BioNeMo through NVIDIA DGX™ Cloud, organizations can fully realize the potential of generative AI. This technology is not just limited to creative endeavors; it also finds applications in generating marketing content, crafting narratives, translating languages globally, and synthesizing information from various sources, such as news articles and meeting notes. By harnessing these advanced tools, businesses can foster innovation and stay ahead in an ever-evolving digital landscape.

Lyzr Agent Studio provides a low-code/no code platform that allows enterprises to build, deploy and scale AI agents without requiring a lot of technical expertise. This platform is built on Lyzr’s robust Agent Framework, the first and only agent Framework to have safe and reliable AI natively integrated in the core agent architecture. The platform allows non-technical and technical users to create AI powered solutions that drive automation and improve operational efficiency while enhancing customer experiences without the need for extensive programming expertise. Lyzr Agent Studio allows you to build complex, industry-specific apps for sectors such as BFSI or deploy AI agents for Sales and Marketing, HR or Finance.

SuperAGI SuperCoder is an innovative open-source autonomous platform that merges an AI-driven development environment with AI agents, facilitating fully autonomous software creation, beginning with the Python language and its frameworks. The latest iteration, SuperCoder 2.0, utilizes large language models and a Large Action Model (LAM) that has been specially fine-tuned for Python code generation, achieving remarkable accuracy in one-shot or few-shot coding scenarios, surpassing benchmarks like SWE-bench and Codebench. As a self-sufficient system, SuperCoder 2.0 incorporates tailored software guardrails specific to development frameworks, initially focusing on Flask and Django, while also utilizing SuperAGI’s Generally Intelligent Developer Agents to construct intricate real-world software solutions. Moreover, SuperCoder 2.0 offers deep integration with popular tools in the developer ecosystem, including Jira, GitHub or GitLab, Jenkins, and cloud-based QA solutions like BrowserStack and Selenium, ensuring a streamlined and efficient software development process. By combining cutting-edge technology with practical software engineering needs, SuperCoder 2.0 aims to redefine the landscape of automated software development.

NVIDIA FLARE, which stands for Federated Learning Application Runtime Environment, is a versatile, open-source SDK designed to enhance federated learning across various sectors, such as healthcare, finance, and the automotive industry. This platform enables secure and privacy-focused AI model training by allowing different parties to collaboratively develop models without the need to share sensitive raw data. Supporting a range of machine learning frameworks—including PyTorch, TensorFlow, RAPIDS, and XGBoost—FLARE seamlessly integrates into existing processes. Its modular architecture not only fosters customization but also ensures scalability, accommodating both horizontal and vertical federated learning methods. This SDK is particularly well-suited for applications that demand data privacy and adherence to regulations, including fields like medical imaging and financial analytics. Users can conveniently access and download FLARE through the NVIDIA NVFlare repository on GitHub and PyPi, making it readily available for implementation in diverse projects. Overall, FLARE represents a significant advancement in the pursuit of privacy-preserving AI solutions.

Caffe is a deep learning framework designed with a focus on expressiveness, efficiency, and modularity, developed by Berkeley AI Research (BAIR) alongside numerous community contributors. The project was initiated by Yangqing Jia during his doctoral studies at UC Berkeley and is available under the BSD 2-Clause license. For those interested, there is an engaging web image classification demo available for viewing! The framework’s expressive architecture promotes innovation and application development. Users can define models and optimizations through configuration files without the need for hard-coded elements. By simply toggling a flag, users can seamlessly switch between CPU and GPU, allowing for training on powerful GPU machines followed by deployment on standard clusters or mobile devices. The extensible nature of Caffe's codebase supports ongoing development and enhancement. In its inaugural year, Caffe was forked by more than 1,000 developers, who contributed numerous significant changes back to the project. Thanks to these community contributions, the framework remains at the forefront of state-of-the-art code and models. Caffe's speed makes it an ideal choice for both research experiments and industrial applications, with the capability to process upwards of 60 million images daily using a single NVIDIA K40 GPU, demonstrating its robustness and efficacy in handling large-scale tasks. This performance ensures that users can rely on Caffe for both experimentation and deployment in various scenarios.

Easily access dedicated machines equipped with pre-configured professional AI development tools from reliable data centers at competitive rates. Apolo offers everything from high-performance computing resources to a comprehensive AI platform featuring an integrated machine learning development toolkit. It can be implemented in various configurations, including distributed architectures, dedicated enterprise clusters, or multi-tenant white-label solutions to cater to specialized instances or self-service cloud environments. Instantly, Apolo sets up a robust AI-focused development environment, providing you with all essential tools readily accessible. The platform efficiently manages and automates both infrastructure and processes, ensuring successful AI development at scale. Apolo’s AI-driven services effectively connect your on-premises and cloud resources, streamline deployment pipelines, and synchronize both open-source and commercial development tools. By equipping enterprises with the necessary resources and tools, Apolo facilitates significant advancements in AI innovation. With its user-friendly interface and powerful capabilities, Apolo stands out as a premier choice for organizations looking to enhance their AI initiatives.

Teachable Machine offers a quick and straightforward approach to building machine learning models for websites, applications, and various other platforms, without needing any prior coding skills or technical expertise. This versatile tool allows users to either upload files or capture live examples, ensuring it fits seamlessly into your workflow. Additionally, it prioritizes user privacy by enabling on-device usage, meaning no data from your webcam or microphone is sent off your computer. As a web-based resource, Teachable Machine is designed to be user-friendly and inclusive, catering to a diverse audience that includes educators, artists, students, and innovators alike. Anyone with a creative idea can utilize this tool to train a computer to identify images, sounds, and poses, all without delving into complex programming. Once your model is trained, you can easily incorporate it into your personal projects and applications, expanding the possibilities of what you can create. The platform empowers users to explore and experiment with machine learning in a way that feels natural and manageable.

Protopia AI’s Stained Glass Transform (SGT) is a revolutionary privacy layer designed to secure sensitive enterprise data during AI model inference and training. It empowers organizations to unlock the full potential of their data by securely transmitting and processing information without exposing confidential details. SGT is highly versatile, working seamlessly across various infrastructure setups, including on-premises, hybrid clouds, and multi-tenant environments, while optimizing GPU performance for fast AI workloads. By running up to 14,000 times faster than cryptographic techniques, it minimizes inference delays to mere milliseconds, enabling real-time AI applications. The solution targets industries where data privacy is paramount, such as financial services, government defense, and regulated healthcare sectors. Protopia also partners with leading platforms like AWS, Lambda, and vLLM to enhance AI deployment and data protection capabilities. Additionally, it offers specialized features like feature-level data obfuscation and prompt protection for large language models. This combination of speed, security, and flexibility positions SGT as a critical tool for enterprises striving to adopt AI responsibly and efficiently.

Our team has developed the quickest AI accelerator, utilizing the most extensive processor available in the market, and have ensured its user-friendliness. With Cerebras, you can experience rapid training speeds, extremely low latency for inference, and an unprecedented time-to-solution that empowers you to reach your most daring AI objectives. Just how bold can these objectives be? We not only make it feasible but also convenient to train language models with billions or even trillions of parameters continuously, achieving nearly flawless scaling from a single CS-2 system to expansive Cerebras Wafer-Scale Clusters like Andromeda, which stands as one of the largest AI supercomputers ever constructed. This capability allows researchers and developers to push the boundaries of AI innovation like never before.

Enhance the precision of your machine learning models by leveraging publicly accessible datasets. Streamline the process of data discovery and preparation with curated datasets that are not only readily available for machine learning applications but also easily integrable through Azure services. It is essential to consider real-world factors that could influence business performance. By integrating features from these curated datasets into your machine learning models, you can significantly boost the accuracy of your predictions while minimizing the time spent on data preparation. Collaborate and share datasets with an expanding network of data scientists and developers. Utilize Azure Open Datasets alongside Azure’s machine learning and data analytics solutions to generate insights at an unprecedented scale. Most Open Datasets come at no extra cost, allowing you to pay solely for the Azure services utilized, including virtual machine instances, storage, networking, and machine learning resources. This curated open data is designed for seamless access on Azure, empowering users to focus on innovation and analysis. In this way, organizations can unlock new opportunities and drive informed decision-making.

Create and Share Engaging Machine Learning Applications. Gradio offers the quickest way to showcase your machine learning model through a user-friendly web interface, enabling anyone to access it from anywhere! You can easily install Gradio using pip. Setting up a Gradio interface involves just a few lines of code in your project. There are various interface types available to connect your function effectively. Gradio can be utilized in Python notebooks or displayed as a standalone webpage. Once you create an interface, it can automatically generate a public link that allows your colleagues to interact with the model remotely from their devices. Moreover, after developing your interface, you can host it permanently on Hugging Face. Hugging Face Spaces will take care of hosting the interface on their servers and provide you with a shareable link, ensuring your work is accessible to a wider audience. With Gradio, sharing your machine learning solutions becomes an effortless task!

Tune Studio is a highly accessible and adaptable platform that facilitates the effortless fine-tuning of AI models. It enables users to modify pre-trained machine learning models to meet their individual requirements, all without the need for deep technical knowledge. Featuring a user-friendly design, Tune Studio makes it easy to upload datasets, adjust settings, and deploy refined models quickly and effectively. Regardless of whether your focus is on natural language processing, computer vision, or various other AI applications, Tune Studio provides powerful tools to enhance performance, shorten training durations, and speed up AI development. This makes it an excellent choice for both novices and experienced practitioners in the AI field, ensuring that everyone can harness the power of AI effectively. The platform's versatility positions it as a critical asset in the ever-evolving landscape of artificial intelligence.

Substrate serves as the foundation for agentic AI, featuring sophisticated abstractions and high-performance elements, including optimized models, a vector database, a code interpreter, and a model router. It stands out as the sole compute engine crafted specifically to handle complex multi-step AI tasks. By merely describing your task and linking components, Substrate can execute it at remarkable speed. Your workload is assessed as a directed acyclic graph, which is then optimized; for instance, it consolidates nodes that are suitable for batch processing. The Substrate inference engine efficiently organizes your workflow graph, employing enhanced parallelism to simplify the process of integrating various inference APIs. Forget about asynchronous programming—just connect the nodes and allow Substrate to handle the parallelization of your workload seamlessly. Our robust infrastructure ensures that your entire workload operates within the same cluster, often utilizing a single machine, thereby eliminating delays caused by unnecessary data transfers and cross-region HTTP requests. This streamlined approach not only enhances efficiency but also significantly accelerates task execution times.

The Databricks Data Intelligence Platform empowers every member of your organization to leverage data and artificial intelligence effectively. Constructed on a lakehouse architecture, it establishes a cohesive and transparent foundation for all aspects of data management and governance, enhanced by a Data Intelligence Engine that recognizes the distinct characteristics of your data. Companies that excel across various sectors will be those that harness the power of data and AI. Covering everything from ETL processes to data warehousing and generative AI, Databricks facilitates the streamlining and acceleration of your data and AI objectives. By merging generative AI with the integrative advantages of a lakehouse, Databricks fuels a Data Intelligence Engine that comprehends the specific semantics of your data. This functionality enables the platform to optimize performance automatically and manage infrastructure in a manner tailored to your organization's needs. Additionally, the Data Intelligence Engine is designed to grasp the unique language of your enterprise, making the search and exploration of new data as straightforward as posing a question to a colleague, thus fostering collaboration and efficiency. Ultimately, this innovative approach transforms the way organizations interact with their data, driving better decision-making and insights.

The Intel Open Edge Platform streamlines the process of developing, deploying, and scaling AI and edge computing solutions using conventional hardware while achieving cloud-like efficiency. It offers a carefully selected array of components and workflows designed to expedite the creation, optimization, and development of AI models. Covering a range of applications from vision models to generative AI and large language models, the platform equips developers with the necessary tools to facilitate seamless model training and inference. By incorporating Intel’s OpenVINO toolkit, it guarantees improved performance across Intel CPUs, GPUs, and VPUs, enabling organizations to effortlessly implement AI applications at the edge. This comprehensive approach not only enhances productivity but also fosters innovation in the rapidly evolving landscape of edge computing.

No coding is required to create AI voice assistants that can make outbound calls and answer inbound calls. They can also schedule appointments 24 hours a day. Forget expensive machine learning teams and lengthy development cycles. Synthflow allows you to create sophisticated, tailored AI agents with no technical knowledge or coding. All you need is your data and your ideas. Over a dozen AI agents are available for use in a variety of applications, including document search, process automaton, and answering questions. You can use an agent as is or customize it according to your needs. Upload data instantly using PDFs, CSVs PPTs URLs and more. Every new piece of information makes your agent smarter. No limits on storage or computing resources. Pinecone allows you to store unlimited vector data. You can control and monitor how your agent learns. Connect your AI agent to any data source or services and give it superpowers.

Our platform allows for quick setup of a pipeline consisting of various layers, where models equipped with computer vision capabilities relay their outputs to one another, enabling you to assemble the specific functionalities you need by combining our existing features. In the event that you encounter a specialized scenario that our adaptable prebuilt options do not address, you can contact us to have it added, or you can take advantage of our custom model creation feature to design your own solution and incorporate it into the pipeline. Furthermore, you can seamlessly integrate your setup into your application using ezML libraries that are compatible with a wide range of frameworks and programming languages, which cater to both standard use cases and real-time streaming via TCP, WebRTC, and RTMP. Additionally, our deployments are designed to automatically scale, ensuring that your service operates smoothly regardless of the growth in user demand. This flexibility and ease of integration empower you to develop powerful applications with minimal hassle.

We are builders on a mission. We're obsessed with building products that make the world a more secure place. Over the course of our careers we've built countless enterprise products at both startups and companies like Splunk, Cisco, Symantec, and McAfee. In every case we had to write security features from scratch. Pangea offers the first Security Platform as a Service (SPaaS) which unifies the fragmented world of security into a simple set of APIs for developers to call directly into their apps.

Effortlessly switch between eager and graph modes using TorchScript, while accelerating your journey to production with TorchServe. The torch-distributed backend facilitates scalable distributed training and enhances performance optimization for both research and production environments. A comprehensive suite of tools and libraries enriches the PyTorch ecosystem, supporting development across fields like computer vision and natural language processing. Additionally, PyTorch is compatible with major cloud platforms, simplifying development processes and enabling seamless scaling. You can easily choose your preferences and execute the installation command. The stable version signifies the most recently tested and endorsed iteration of PyTorch, which is typically adequate for a broad range of users. For those seeking the cutting-edge, a preview is offered, featuring the latest nightly builds of version 1.10, although these may not be fully tested or supported. It is crucial to verify that you meet all prerequisites, such as having numpy installed, based on your selected package manager. Anaconda is highly recommended as the package manager of choice, as it effectively installs all necessary dependencies, ensuring a smooth installation experience for users. This comprehensive approach not only enhances productivity but also ensures a robust foundation for development.

It enhances the efficiency of both development and deployment processes, cuts down on cloud expenses, and liberates users from being tied to a specific vendor. You can set up the required hardware resources, including GPU and memory, and choose between spot instances or on-demand options. dstack streamlines the entire process by automatically provisioning cloud resources, retrieving your code, and ensuring secure access through port forwarding. You can conveniently utilize your local desktop IDE to access the cloud development environment. Specify the hardware configurations you need, such as GPU and memory, while indicating your preference for instance types. dstack handles resource provisioning and port forwarding automatically for a seamless experience. You can pre-train and fine-tune advanced models easily and affordably in any cloud infrastructure. With dstack, cloud resources are provisioned based on your specifications, allowing you to access data and manage output artifacts using either declarative configuration or the Python SDK, thus simplifying the entire workflow. This flexibility significantly enhances productivity and reduces overhead in cloud-based projects.

Vertex AI Notebooks offers a comprehensive, end-to-end solution for machine learning development within Google Cloud. It combines the power of Colab Enterprise and Vertex AI Workbench to give data scientists and developers the tools to accelerate model training and deployment. This fully managed platform provides seamless integration with BigQuery, Dataproc, and other Google Cloud services, enabling efficient data exploration, visualization, and advanced ML model development. With built-in features like automated infrastructure management, users can focus on model building without worrying about backend maintenance. Vertex AI Notebooks also supports collaborative workflows, making it ideal for teams to work on complex AI projects together.

Build, deploy and scale AI applications from end-to-end in the cloud. Griptape provides developers with everything they need from the development framework up to the execution runtime to build, deploy and scale retrieval driven AI-powered applications. Griptape, a Python framework that is modular and flexible, allows you to build AI-powered apps that securely connect with your enterprise data. It allows developers to maintain control and flexibility throughout the development process. Griptape Cloud hosts your AI structures whether they were built with Griptape or another framework. You can also call directly to LLMs. To get started, simply point your GitHub repository. You can run your hosted code using a basic API layer, from wherever you are. This will allow you to offload the expensive tasks associated with AI development. Automatically scale your workload to meet your needs.

Daria's innovative automated capabilities enable users to swiftly and effectively develop predictive models, drastically reducing the lengthy iterative processes typically associated with conventional machine learning methods. It eliminates both financial and technological obstacles, allowing enterprises to create AI systems from the ground up. By automating machine learning workflows, Daria helps data professionals save weeks of effort typically spent on repetitive tasks. The platform also offers a user-friendly graphical interface, making it accessible for those new to data science to gain practical experience in machine learning. With a suite of data transformation tools at their disposal, users can effortlessly create various feature sets. Daria conducts an extensive exploration of millions of potential algorithm combinations, modeling strategies, and hyperparameter configurations to identify the most effective predictive model. Moreover, models generated using Daria can be seamlessly deployed into production with just a single line of code through its RESTful API. This streamlined process not only enhances productivity but also empowers businesses to leverage AI more effectively in their operations.

Llama Stack is an innovative modular framework aimed at simplifying the creation of applications that utilize Meta's Llama language models. It features a client-server architecture with adaptable configurations, giving developers the ability to combine various providers for essential components like inference, memory, agents, telemetry, and evaluations. This framework comes with pre-configured distributions optimized for a range of deployment scenarios, facilitating smooth transitions from local development to live production settings. Developers can engage with the Llama Stack server through client SDKs that support numerous programming languages, including Python, Node.js, Swift, and Kotlin. In addition, comprehensive documentation and sample applications are made available to help users efficiently construct and deploy applications based on the Llama framework. The combination of these resources aims to empower developers to build robust, scalable applications with ease.

Omni is an AI framework that allows you to connect Prompts and Tools to LLM Agents. Agents are built on the ReAct paradigm, which is Reason + Act. They allow LLM models and tools to interact to complete a task. Automate customer service, document processing, qualification of leads, and more. You can easily switch between LLM architectures and prompts to optimize performance. Your workflows are hosted as APIs, so you can instantly access AI.