Alternatives to Prem AI

Create and manage AI applications without the need to move data to complex vector databases and pipelines. Integrate AI, vector search and real-time inference directly with your database. Python is all you need. All your AI models can be deployed in a single, scalable deployment. The AI models and APIs are automatically updated as new data is processed. You don't need to duplicate your data or create an additional database to use vector searching and build on it. SuperDuperDB allows vector search within your existing database. Integrate and combine models such as those from Sklearn PyTorch HuggingFace, with AI APIs like OpenAI, to build even the most complicated AI applications and workflows. With simple Python commands, deploy all your AI models in one environment to automatically compute outputs in your datastore (inference).

WebLLM is an in-browser, high-performance language model inference engine. It uses WebGPU to accelerate the hardware, enabling powerful LLM functions directly within web browsers, without server-side processing. It is compatible with the OpenAI API, allowing seamless integration of functionalities like JSON mode, function calling, and streaming. WebLLM supports a wide range of models including Llama Phi Gemma Mistral Qwen and RedPajama. Users can easily integrate custom models into MLC format and adapt WebLLM to their specific needs and scenarios. The platform allows for plug-and play integration via package managers such as NPM and Yarn or directly through CDN. It also includes comprehensive examples and a module design to connect with UI components. It supports real-time chat completions, which enhance interactive applications such as chatbots and virtual assistances.

The complete LLM Platform with a superior developer's experience and robust infrastructure. Build, deploy and manage trusted, hyper-personalized and streamlined generative AI applications. Langbase is a new AI tool and inference engine for any LLM. It's an OpenAI alternative that's open-source. The most "developer friendly" LLM platform that can ship hyper-personalized AI applications in seconds.

NetMind.AI, a decentralized AI ecosystem and computing platform, is designed to accelerate global AI innovations. It offers AI computing power that is affordable and accessible to individuals, companies, and organizations of any size by leveraging idle GPU resources around the world. The platform offers a variety of services including GPU rental, serverless Inference, as well as an AI ecosystem that includes data processing, model development, inference and agent development. Users can rent GPUs for competitive prices, deploy models easily with serverless inference on-demand, and access a variety of open-source AI APIs with low-latency, high-throughput performance. NetMind.AI allows contributors to add their idle graphics cards to the network and earn NetMind Tokens. These tokens are used to facilitate transactions on the platform. Users can pay for services like training, fine-tuning and inference as well as GPU rentals.

Horay.ai offers out-of-the box large model inference services, bringing an efficient user experience to generative AI applications. Horay.ai, a cutting edge cloud service platform, primarily offers APIs for large open-source models. Our platform provides a wide range of models, guarantees fast updates, and offers services at competitive rates. This allows developers to easily integrate advanced multimodal capabilities, natural language processing, and image generation into their applications. Horay.ai infrastructure allows developers to focus on innovation, rather than the complexity of model deployment and maintenance. Horay.ai was founded in 2024 by a team of AI experts. We are focused on serving generative AI developer, improving service quality and the user experience. Horay.ai offers reliable solutions for both startups and large enterprises to help them grow rapidly.

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

A system that can scale to millions of users, without requiring an engineering team. Create, customize and deploy your chat-based AI. Finance chatbot. xFinance is a 13-billion-parameter model fine-tuned using LoRA. Our goal was show that impressive results can be achieved in financial NLP without breaking the bank. Your own AI assistant to chat with documents. Single or multiple documents. Simple or complex questions. Easy-to-use deep learning platform, hardware efficient algorithms that speed up inference and lower costs. Real-time monitoring and logging of resource usage and cloud costs for deployed models. xTuring, an open-source AI software for personalization, is a powerful tool. xTuring provides a simple interface for personalizing LLMs based on your data and application.

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

Simplismart’s fastest inference engine allows you to fine-tune and deploy AI model with ease. Integrate with AWS/Azure/GCP, and many other cloud providers, for simple, scalable and cost-effective deployment. Import open-source models from popular online repositories, or deploy your custom model. Simplismart can host your model or you can use your own cloud resources. Simplismart allows you to go beyond AI model deployment. You can train, deploy and observe any ML models and achieve increased inference speed at lower costs. Import any dataset to fine-tune custom or open-source models quickly. Run multiple training experiments efficiently in parallel to speed up your workflow. Deploy any model to our endpoints, or your own VPC/premises and enjoy greater performance at lower cost. Now, streamlined and intuitive deployments are a reality. Monitor GPU utilization, and all of your node clusters on one dashboard. On the move, detect any resource constraints or model inefficiencies.

Fireworks works with the leading generative AI researchers in the world to provide the best models at the fastest speed. Independently benchmarked for the fastest inference providers. Use models curated by Fireworks, or our multi-modal and functionality-calling models that we have trained in-house. Fireworks is also the 2nd most popular open-source model provider, and generates more than 1M images/day. Fireworks' OpenAI-compatible interface makes it simple to get started. Dedicated deployments of your models will ensure uptime and performance. Fireworks is HIPAA-compliant and SOC2-compliant and offers secure VPC connectivity and VPN connectivity. Own your data and models. Fireworks hosts serverless models, so there's no need for hardware configuration or deployment. Fireworks.ai provides a lightning fast inference platform to help you serve generative AI model.

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

NVIDIA TensorRT provides an ecosystem of APIs to support high-performance deep learning. It includes an inference runtime, model optimizations and a model optimizer that delivers low latency and high performance for production applications. TensorRT, built on the CUDA parallel programing model, optimizes neural networks trained on all major frameworks. It calibrates them for lower precision while maintaining high accuracy and deploys them across hyperscale data centres, workstations and laptops. It uses techniques such as layer and tensor-fusion, kernel tuning, and quantization on all types NVIDIA GPUs from edge devices to data centers. TensorRT is an open-source library that optimizes the inference performance for large language models.

NVIDIA Picasso, a cloud service that allows you to build generative AI-powered visual apps, is available. Software creators, service providers, and enterprises can run inference on models, train NVIDIA Edify foundation model models on proprietary data, and start from pre-trained models to create image, video, or 3D content from text prompts. The Picasso service is optimized for GPUs. It streamlines optimization, training, and inference on NVIDIA DGX Cloud. Developers and organizations can train NVIDIA Edify models using their own data, or use models pre-trained by our premier partners. Expert denoising network to create photorealistic 4K images The novel video denoiser and temporal layers generate high-fidelity videos with consistent temporality. A novel optimization framework to generate 3D objects and meshes of high-quality geometry. Cloud service to build and deploy generative AI-powered image and video applications.

Machine learning models can be deployed at scale with greater accuracy. With more models in production, R&D can be turned into ROI. Seldon reduces time to value so models can get to work quicker. Scale with confidence and minimize risks through transparent model performance and interpretable results. Seldon Deploy cuts down on time to production by providing production-grade inference servers that are optimized for the popular ML framework and custom language wrappers to suit your use cases. Seldon Core Enterprise offers enterprise-level support and access to trusted, global-tested MLOps software. Seldon Core Enterprise is designed for organizations that require: - Coverage for any number of ML models, plus unlimited users Additional assurances for models involved in staging and production - You can be confident that their ML model deployments will be supported and protected.

ONNX defines a set of common operators - the building block of machine learning and deeper learning models – and a standard file format that allows AI developers to use their models with a wide range of frameworks, runtimes and compilers. You can use your preferred framework to develop without worrying about downstream implications. ONNX allows you to use the framework of your choice with your inference engine. ONNX simplifies the access to hardware optimizations. Use runtimes and libraries compatible with ONNX to optimize performance across hardware. Our community thrives in our open governance structure that provides transparency and inclusion. We encourage you to participate and contribute.

Wallaroo is the last mile of your machine-learning journey. It helps you integrate ML into your production environment and improve your bottom line. Wallaroo was designed from the ground up to make it easy to deploy and manage ML production-wide, unlike Apache Spark or heavy-weight containers. ML that costs up to 80% less and can scale to more data, more complex models, and more models at a fraction of the cost. Wallaroo was designed to allow data scientists to quickly deploy their ML models against live data. This can be used for testing, staging, and prod environments. Wallaroo supports the most extensive range of machine learning training frameworks. The platform will take care of deployment and inference speed and scale, so you can focus on building and iterating your models.

Amazon EC2 Inf1 instances were designed to deliver high-performance, cost-effective machine-learning inference. Amazon EC2 Inf1 instances offer up to 2.3x higher throughput, and up to 70% less cost per inference compared with other Amazon EC2 instance. Inf1 instances are powered by up to 16 AWS inference accelerators, designed by AWS. They also feature Intel Xeon Scalable 2nd generation processors, and up to 100 Gbps of networking bandwidth, to support large-scale ML apps. These instances are perfect for deploying applications like search engines, recommendation system, computer vision and speech recognition, natural-language processing, personalization and fraud detection. Developers can deploy ML models to Inf1 instances by using the AWS Neuron SDK. This SDK integrates with popular ML Frameworks such as TensorFlow PyTorch and Apache MXNet.

Nendo, the AI audio tool suite, allows you to easily develop and use audio apps. This will increase efficiency and creativity in all aspects of audio production. Machine learning and audio processing codes are no longer a problem. AI is a game-changer for audio production. It amplifies efficiency and creativity across industries that rely on audio. But creating custom AI Audio solutions is difficult and operating them at a large scale is even more challenging. Nendo Cloud empowers developers and business to deploy Nendo apps, use premium AI audio models via APIs, and manage workloads efficiently at scale. Nendo Cloud is the solution for batch processing, model inference, training, and more.

We have the infrastructure to meet all your computing needs, whether you need one or thousands GPUs or one vCPU or tens of thousand vCPUs. Our resources are available whenever you need them. Our platform makes switching between GPU and CPU instances a breeze. You can easily deploy, modify and rescale instances to meet your needs. You can get outstanding machine learning performance without breaking your bank. The latest technology for a much lower price. Modern GPUs are built to meet your workload demands. Get access to computing resources that are tailored for your models. Our OblivusAI OS allows you to access libraries and leverage our infrastructure for large-scale inference. Use our robust infrastructure to unleash the full potential of gaming by playing games in settings of your choosing.

You can deploy Mystic in your own Azure/AWS/GCP accounts or in our shared GPU cluster. All Mystic features can be accessed directly from your cloud. In just a few steps, you can get the most cost-effective way to run ML inference. Our shared cluster of graphics cards is used by hundreds of users at once. Low cost, but performance may vary depending on GPU availability in real time. We solve the infrastructure problem. A Kubernetes platform fully managed that runs on your own cloud. Open-source Python API and library to simplify your AI workflow. You get a platform that is high-performance to serve your AI models. Mystic will automatically scale GPUs up or down based on the number API calls that your models receive. You can easily view and edit your infrastructure using the Mystic dashboard, APIs, and CLI.

Qubrid AI is a company that specializes in Artificial Intelligence. Its mission is to solve complex real-world problems across multiple industries. Qubrid AI’s software suite consists of AI Hub, an all-in-one shop for AI models, AI Compute GPU cloud and On-Prem appliances, and AI Data Connector. You can train infer-leading models, or your own custom creations. All within a streamlined and user-friendly interface. Test and refine models with ease. Then, deploy them seamlessly to unlock the power AI in your projects. AI Hub enables you to embark on a journey of AI, from conception to implementation, in a single powerful platform. Our cutting-edge AI Compute Platform harnesses the power from GPU Cloud and On Prem Server Appliances in order to efficiently develop and operate next generation AI applications. Qubrid is a team of AI developers, research teams and partner teams focused on enhancing the unique platform to advance scientific applications.

Here is where the future of AI development begins. Modular is a composable, integrated suite of tools which simplifies your AI infrastructure, allowing your team to develop, deploy and innovate faster. Modular's inference engines unify AI industry frameworks with hardware. This allows you to deploy into any cloud or on-prem environments with minimal code changes, unlocking unmatched portability, performance and usability. Move your workloads seamlessly to the best hardware without rewriting your models or recompiling them. Avoid lock-in, and take advantage of cloud performance and price improvements without migration costs.

The Intel Distribution of OpenVINO makes it easy to adopt and maintain your code. Open Model Zoo offers optimized, pre-trained models. Model Optimizer API parameters make conversions easier and prepare them for inferencing. The runtime (inference engines) allows you tune for performance by compiling an optimized network and managing inference operations across specific devices. It auto-optimizes by device discovery, load balancencing, inferencing parallelism across CPU and GPU, and many other functions. You can deploy the same application to multiple host processors and accelerators (CPUs. GPUs. VPUs.) and environments (on-premise or in the browser).

Nebula enables you to scale and deploy your AI projects quickly and easily2 on a highly robust GPU infrastructure. Nebula Cloud powered by Nvidia GPUs on demand allows you to train and infer models easily and securely. You can also create and manage containerized workloads using Nebula's easy-to-use orchestration layer. Access Nebula’s MLOps, low-code/no code engines and AI-powered applications to quickly and seamlessly deploy AI-powered apps for business teams. Choose from the Nebula containerized AI Cloud, your on-prem or any cloud. The Nebula Unify platform allows you to build and scale AI-enabled use cases for business in a matter weeks, not months.

Self-service machine learning platform that allows you to turn models into APIs with just a few mouse clicks. Sign up for a Deep Infra Account using GitHub, or login using GitHub. Choose from hundreds of popular ML models. Call your model using a simple REST API. Our serverless GPUs allow you to deploy models faster and cheaper than if you were to build the infrastructure yourself. Depending on the model, we have different pricing models. Some of our models have token-based pricing. The majority of models are charged by the time it takes to execute an inference. This pricing model allows you to only pay for the services you use. You can easily scale your business as your needs change. There are no upfront costs or long-term contracts. All models are optimized for low latency and inference performance on A100 GPUs. Our system will automatically scale up the model based on your requirements.

Your software can see objects in video and images. A few dozen images can be used to train a computer vision model. This takes less than 24 hours. We support innovators just like you in applying computer vision. Upload files via API or manually, including images, annotations, videos, and audio. There are many annotation formats that we support and it is easy to add training data as you gather it. Roboflow Annotate was designed to make labeling quick and easy. Your team can quickly annotate hundreds upon images in a matter of minutes. You can assess the quality of your data and prepare them for training. Use transformation tools to create new training data. See what configurations result in better model performance. All your experiments can be managed from one central location. You can quickly annotate images right from your browser. Your model can be deployed to the cloud, the edge or the browser. Predict where you need them, in half the time.

OpenAI compatible, fast, lightweight, portable and powered by rust. We work with cloud providers to support microservices in web apps, especially edge cloud/CDN computing providers. Use cases include AI inferences, database accesses, CRM, ecommerce and workflow management. We work with streaming frameworks, databases and data to support embedded functions for data filtering. The serverless functions may be database UDFs. They could be embedded into data ingest streams or query results. Write once and run anywhere. Take full advantage of GPUs. In just 5 minutes, you can get started with the Llama 2 models on your device. Retrieval - Argumented Generation (RAG) has become a popular way to build AI agents using external knowledge bases. Create an HTTP microservice to classify images. It runs YOLO models and Mediapipe models natively at GPU speed.

Pipelines are permanent, models are temporary. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform to automate everything, from data extraction to model deployment. Automate everything, from data extraction to model installation. Automatically store every model, experiment, and artifact. Monitor and deploy models in a Kubernetes cluster. Just point to your code and hit "run". Valohai launches workers and runs your experiments. Then, Valohai shuts down the instances. You can create notebooks, scripts, or shared git projects using any language or framework. Our API allows you to expand endlessly. Track each experiment and trace back to the original training data. All data can be audited and shared.

You can build and scale your computer vision model quickly and easily. NeuCore makes it easy to develop, train, and deploy your computer vision model in just minutes. You can scale it up to millions of times. One-stop platform that manages all aspects of the model lifecycle including training, development, deployment, maintenance, and maintenance. Advanced data encryption is used to protect your information throughout the entire process, from training to inference. Fully integrated vision AI models can be easily integrated into existing systems and workflows, or even onto edge devices. Seamless scaling allows for your evolving business needs and business requirements. Splits an image into sections that contain different objects. Machine-readable text extracted from images. This model can also be used to read handwriting. NeuCore makes it easy to build computer vision models. It's as simple as one-click and drag-and-drop. Advanced users can access code scripts and watch tutorial videos to customize the software.

Groq's mission is to set the standard in GenAI inference speeds, enabling real-time AI applications to be developed today. LPU, or Language Processing Unit, inference engines are a new end-to-end system that can provide the fastest inference possible for computationally intensive applications, including AI language applications. The LPU was designed to overcome two bottlenecks in LLMs: compute density and memory bandwidth. In terms of LLMs, an LPU has a greater computing capacity than both a GPU and a CPU. This reduces the time it takes to calculate each word, allowing text sequences to be generated faster. LPU's inference engine can also deliver orders of magnitude higher performance on LLMs than GPUs by eliminating external memory bottlenecks. Groq supports machine learning frameworks like PyTorch TensorFlow and ONNX.

We take the hard work out of AI processing on the edge. The Latent AI Efficient Inference Platform (LEIP) enables adaptive AI at edge by optimizing compute, energy, and memory without requiring modifications to existing AI/ML infrastructure or frameworks. LEIP is a fully-integrated modular workflow that can be used to build, quantify, and deploy edge AI neural network. Latent AI believes in a vibrant and sustainable future driven by the power of AI. Our mission is to enable the vast potential of AI that is efficient, practical and useful. We reduce the time to market with a Robust, Repeatable, and Reproducible workflow for edge AI. We help companies transform into an AI factory to make better products and services.

DeepCube is a company that focuses on deep learning technologies. This technology can be used to improve the deployment of AI systems in real-world situations. The company's many patent innovations include faster, more accurate training of deep-learning models and significantly improved inference performance. DeepCube's proprietary framework is compatible with any hardware, datacenters or edge devices. This allows for over 10x speed improvements and memory reductions. DeepCube is the only technology that allows for efficient deployment of deep-learning models on intelligent edge devices. The model is typically very complex and requires a lot of memory. Deep learning deployments today are restricted to the cloud because of the large amount of memory and processing requirements.

NVIDIA Modulus, a neural network framework, combines the power of Physics in the form of governing partial differential equations (PDEs), with data to create high-fidelity surrogate models with near real-time latency. NVIDIA Modulus is a tool that can help you solve complex, nonlinear, multiphysics problems using AI. This tool provides the foundation for building physics machine learning surrogate models that combine physics and data. This framework can be applied to many domains and uses, including engineering simulations and life sciences. It can also be used to solve forward and inverse/data assimilation issues. Parameterized system representation that solves multiple scenarios in near real-time, allowing you to train once offline and infer in real-time repeatedly.

Cloud-hosted AI packages that are managed and cloud-hosted will make it easier to ship AI faster. GPT-4 support is fully integrated. API tokens do not need to be used. Use our low-code framework to build. All major models can be integrated. Get an instant API by deploying. Scale and share your API without having to manage infrastructure. Make prompts, prompt chains, basic Python, and managed APIs. A clever prompt can be turned into a publicly available API that you can share. Python allows you to add logic and routing smarts. Steamship connects with your favorite models and services, so you don't need to learn a different API for each provider. Steamship maintains model output in a standard format. Consolidate training and inference, vector search, endpoint hosting. Import, transcribe or generate text. It can run all the models that you need. ShipQL allows you to query across all the results. Packages are fully-stack, cloud-hosted AI applications. Each instance you create gives you an API and private data workspace.

Amazon EC2 instances G5 are the latest generation NVIDIA GPU instances. They can be used to run a variety of graphics-intensive applications and machine learning use cases. They offer up to 3x faster performance for graphics-intensive apps and machine learning inference, and up to 3.33x faster performance for machine learning learning training when compared to Amazon G4dn instances. Customers can use G5 instance for graphics-intensive apps such as video rendering, gaming, and remote workstations to produce high-fidelity graphics real-time. Machine learning customers can use G5 instances to get a high-performance, cost-efficient infrastructure for training and deploying larger and more sophisticated models in natural language processing, computer visualisation, and recommender engines. G5 instances offer up to three times higher graphics performance, and up to forty percent better price performance compared to G4dn instances. They have more ray tracing processor cores than any other GPU based EC2 instance.

It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).

UbiOps provides an AI infrastructure platform to help teams run AI & ML workloads quickly as reliable and secure Microservices without disrupting their existing workflows. UbiOps can be integrated seamlessly into your data-science workbench in minutes. This will save you time and money by avoiding the hassle of setting up expensive cloud infrastructure. You can use UbiOps as a data science team in a large company or a start-up to launch an AI product. UbiOps is a reliable backbone to any AI or ML services. Scale AI workloads dynamically based on usage, without paying for idle times. Instantly access powerful GPUs for model training and inference, enhanced by serverless, multicloud workload distribution.

AI Studio provides AI-driven, end-to-end data operations (DataOps), software development operations (DevOps), as well as Machine Learning operations tools (MLOps). Our AI Software Platform reduces dependency on crucial resources such as Data Scientists and Machine Learning Engineers, reduces time from development to deployment, and makes managing edge AI systems easier over the product's life span. AI Studio is intended for deployment to edge inference accelerators and systems on-premises. It can also be used for cloud-based applications. With powerful data-labeling functions and annotation functions, you can reduce the time between data capture to AI deployment at Edge. Automated process that leverages AI knowledge base, MarketPlace, and guided strategies, enabling Business Experts to add AI expertise and solutions.

Amazon SageMaker makes it easy for you to deploy ML models to make predictions (also called inference) at the best price and performance for your use case. It offers a wide range of ML infrastructure options and model deployment options to meet your ML inference requirements. It integrates with MLOps tools to allow you to scale your model deployment, reduce costs, manage models more efficiently in production, and reduce operational load. Amazon SageMaker can handle all your inference requirements, including low latency (a few seconds) and high throughput (hundreds upon thousands of requests per hour).

InferKit provides a web interface as well as an API to create AI-based text generators. There's something for everyone, whether you're an app developer or a novelist looking to find inspiration. InferKit's text generator takes the text you provide and generates what it thinks is next using a state of the art neural network. It can generate any length of text on virtually any topic and is configurable. You can use the tool via the web interface or through the developer API. Register now to get started. You can also use the network to write poetry or stories. Marketing and auto-completion are other possible uses. The generator can only understand a limited amount of text at once (currently, at most 3000 characters), so if you give it a longer prompt it will not use the beginning. The network is already trained and doesn't learn from inputs. Each request must contain at least 100 characters

Make AI an integral part of your business. With the flexibility and power of a cloud native platform, you can build better, deploy faster and audit easier. Import models and search cataloged model from across your organization. Save time by quickly annotating data with model-in the-loop hinting. Flyte's integration with Chariot allows you to quickly create and launch custom workflows. Understand the full origin of your data, models and workflows. Deploy models wherever you need them. This includes edge and IoT applications. Data scientists are not the only ones who can get valuable insights from their data. With Chariot's low code interface, teams can collaborate effectively.

Kubernetes is a highly scalable platform for model inference that uses standards-based models. Trusted AI. KServe, a Kubernetes standard model inference platform, is designed for highly scalable applications. Provides a standardized, performant inference protocol that works across all ML frameworks. Modern serverless inference workloads supported by autoscaling, including a scale up to zero on GPU. High scalability, density packing, intelligent routing with ModelMesh. Production ML serving is simple and pluggable. Pre/post-processing, monitoring and explainability are all possible. Advanced deployments using the canary rollout, experiments and ensembles as well as transformers. ModelMesh was designed for high-scale, high density, and often-changing model use cases. ModelMesh intelligently loads, unloads and transfers AI models to and fro memory. This allows for a smart trade-off between user responsiveness and computational footprint.

A platform that manages model, project, and hosting. This platform allows companies to transform data and algorithms into comprehensive, execution-ready AI strategies. Securely build, train, and manage models. You can create products that use AI models from anywhere and at any time. Reduce the risks associated with AI investments while increasing strategic flexibility. Fully automated model testing, evaluation deployment and scaling. From real-time, low latency, high-throughput, inference to batch-running inference. Pay-per-second-of-use model, with an SLA, and full governance, monitoring and auditing tools. A user-friendly interface that serves as a central hub for managing projects, visualizing data, and training models through collaborative and reproducible workflows.

Fully managed infrastructure, tools and workflows allow you to build, train and deploy models faster. Scale inference and deploy custom AI & LLMs in seconds on any infrastructure. Schedule batch jobs to handle your most demanding tasks, and only pay per second. Optimize costs by utilizing GPUs, spot instances, and automatic failover. YAML simplifies complex infrastructure setups by allowing you to train with a single command. Automate the scaling up of workers during periods of high traffic, and scaling down to zero when inactive. Deploy cutting edge models with persistent endpoints within a serverless environment to optimize resource usage. Monitor system and inference metrics, including worker counts, GPU utilization, throughput, and latency in real-time. Split traffic between multiple models to evaluate.

Zyphra, an artificial intelligence company with offices in Palo Alto and Montreal, is growing in London. We're developing MaiaOS, an agent system that combines advanced research in next-gen neuronal network architectures (SSM-hybrids), long-term memories & reinforcement learning. We believe that the future of AGI is a combination of cloud-based and on-device strategies, with an increasing shift towards local inference. MaiaOS was built around a deployment platform that maximizes the efficiency of inference for real-time Intelligence. Our AI and product teams are drawn from top organizations and institutions, including Google DeepMind and Anthropic. They also come from Qualcomm, Neuralink and Apple. We have deep expertise across AI models, learning algorithms, and systems/infrastructure with a focus on inference efficiency and AI silicon performance. The Zyphra team is dedicated to democratizing advanced artificial intelligence systems.

Exafunction optimizes deep learning inference workloads, up to a 10% improvement in resource utilization and cost. Instead of worrying about cluster management and fine-tuning performance, focus on building your deep-learning application. Poor utilization of GPU hardware is a common problem in deep learning applications. Exafunction allows any GPU code to be moved to remote resources. This includes spot instances. Your core logic is still an inexpensive CPU instance. Exafunction has been proven to be effective in large-scale autonomous vehicle simulation. These workloads require complex custom models, high numerical reproducibility, and thousands of GPUs simultaneously. Exafunction supports models of major deep learning frameworks. Versioning models and dependencies, such as custom operators, allows you to be certain you are getting the correct results.

Breaking the limits of AI processors and edge AI acceleration. EdgeCortix AI cores are the answer to AI inference acceleration that requires more TOPS, less latency, greater area and power efficiency and scalability. Developers can choose from a variety of general-purpose processor cores including CPUs and GPUs. These general-purpose cores are not suited to deep neural network workloads. EdgeCortix was founded with the mission of redefining AI processing at the edge from scratch. EdgeCortix technology, which includes a full-stack AI-inference software development environment, reconfigurable edge AI-inference IP at run-time, and edge AI-chips for boards and systems, allows designers to deploy AI performance near cloud-level at the edge. Imagine what this could do for these applications and others. Finding threats, increasing situational awareness, making vehicles smarter.

Amazon SageMaker Feature Store can be used to store, share and manage features for machine-learning (ML) models. Features are inputs to machine learning models that are used for training and inference. In an example, features might include song ratings, listening time, and listener demographics. Multiple teams may use the same features repeatedly, so it is important to ensure that the feature quality is high-quality. It can be difficult to keep the feature stores synchronized when features are used to train models offline in batches. SageMaker Feature Store is a secure and unified place for feature use throughout the ML lifecycle. To encourage feature reuse across ML applications, you can store, share, and manage ML-model features for training and inference. Any data source, streaming or batch, can be used to import features, such as application logs and service logs, clickstreams and sensors, etc.

Your deep learning workload can be accelerated. AI model training and inference can speed up your time to value. Deep learning is becoming more popular as enterprises adopt it to gain and scale insight through speech recognition and natural language processing. Deep learning can read text, images and video at scale and generate patterns for recommendation engines. It can also model financial risk and detect anomalies. Due to the sheer number of layers and volumes of data required to train neural networks, it has been necessary to use high computational power. Businesses are finding it difficult to demonstrate results from deep learning experiments that were implemented in silos.

There are options for every business to train deep and machine learning models efficiently. There are AI accelerators that can be used for any purpose, from low-cost inference to high performance training. It is easy to get started with a variety of services for development or deployment. Tensor Processing Units are ASICs that are custom-built to train and execute deep neural network. You can train and run more powerful, accurate models at a lower cost and with greater speed and scale. NVIDIA GPUs are available to assist with cost-effective inference and scale-up/scale-out training. Deep learning can be achieved by leveraging RAPID and Spark with GPUs. You can run GPU workloads on Google Cloud, which offers industry-leading storage, networking and data analytics technologies. Compute Engine allows you to access CPU platforms when you create a VM instance. Compute Engine provides a variety of Intel and AMD processors to support your VMs.