Alternatives to NVIDIA AI Enterprise

NVIDIA Base Command™ is an enterprise-class AI software service that allows businesses and their data scientists accelerate AI development. Base Command Platform, which is part of the NVIDIA DGX™ platform provides centralized, hybrid controls for AI training projects. It is compatible with NVIDIA DGX cloud and NVIDIA DGX superPOD. Base Command Platform in conjunction with NVIDIA's accelerated AI infrastructure provides a cloud hosted solution for AI development. Users can avoid the overheads and pitfalls associated with deploying and operating a DIY platform. Base Command Platform configures and manages AI workflows, provides integrated dataset management and executes them using the right-sized resources, ranging from a single GPU up to large-scale multi-node cloud clusters or on-premises. The platform is constantly updated by NVIDIA engineers and researchers, who rely on it daily.

Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility.

Context Data is a data infrastructure for enterprises that accelerates the development of data pipelines to support Generative AI applications. The platform automates internal data processing and transform flows by using an easy to use connectivity framework. Developers and enterprises can connect to all their internal data sources and embed models and vector databases targets without the need for expensive infrastructure or engineers. The platform allows developers to schedule recurring flows of data for updated and refreshed data.

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.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

One development environment for all data science workflows. Natively analyze your data without the need to switch between services. Data to training at scale Models can be built and trained 5X faster than traditional notebooks. Scale up model development using simple connectivity to Vertex AI Services. Access to data is simplified and machine learning is made easier with BigQuery Dataproc, Spark and Vertex AI integration. Vertex AI training allows you to experiment and prototype at scale. Vertex AI Workbench allows you to manage your training and deployment workflows for Vertex AI all from one location. Fully managed, scalable and enterprise-ready, Jupyter-based, fully managed, scalable, and managed compute infrastructure with security controls. Easy connections to Google Cloud's Big Data Solutions allow you to explore data and train ML models.

The NVIDIA GPU Optimized AMI is a virtual image that accelerates your GPU-accelerated Machine Learning and Deep Learning workloads. This AMI allows you to spin up a GPU accelerated EC2 VM in minutes, with a preinstalled Ubuntu OS and GPU driver. Docker, NVIDIA container toolkit, and Docker are also included. This AMI provides access to NVIDIA’s NGC Catalog. It is a hub of GPU-optimized software for pulling and running performance-tuned docker containers that have been tested and certified by NVIDIA. The NGC Catalog provides free access to containerized AI and HPC applications. It also includes pre-trained AI models, AI SDKs, and other resources. This GPU-optimized AMI comes free, but you can purchase enterprise support through NVIDIA Enterprise. Scroll down to the 'Support information' section to find out how to get support for AMI.

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.

An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.

The world's first AI supercomputer in the cloud, NVIDIA DGX™ Cloud is an AI-training-as-a-service solution with integrated DGX infrastructure designed for the unique demands of enterprise AI. NVIDIA DGX Cloud allows businesses to access a combination software-infrastructure solution for AI training. It includes a full-stack AI development suite, a leadership-class infrastructure and concierge support. Businesses can get started immediately with predictable, all in-one pricing.

With a single command, you can train and serve large AI models in scale. You can simply point to your S3 bucket. We take care of the rest: orchestration, efficiency and node failures. Simple and scalable. MosaicML allows you to train and deploy large AI model on your data in a secure environment. Keep up with the latest techniques, recipes, and foundation models. Our research team has developed and rigorously tested these recipes. In just a few easy steps, you can deploy your private cloud. Your data and models will never leave the firewalls. You can start in one cloud and continue in another without missing a beat. Own the model trained on your data. Model decisions can be better explained by examining them. Filter content and data according to your business needs. Integrate seamlessly with your existing data pipelines and experiment trackers. We are cloud-agnostic and enterprise-proven.

Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported.

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.

The NeoPulse Product Suite contains everything a company needs to begin building custom AI solutions using their own curated data. Server application that uses a powerful AI called "the Oracle" to automate the creation of sophisticated AI models. Manages your AI infrastructure, and orchestrates workflows for automating AI generation activities. A program that has been licensed by an organization to allow any application within the enterprise to access the AI model via a web-based (REST API). NeoPulse, an automated AI platform, enables organizations to deploy, manage and train AI solutions in heterogeneous environments. NeoPulse can handle all aspects of the AI engineering workflow: design, training, deployment, managing, and retiring.

NVIDIA GPU Cloud is a GPU-accelerated cloud platform that is optimized for scientific computing and deep learning. NGC is responsible for a catalogue of fully integrated and optimized deep-learning framework containers that take full benefit of NVIDIA GPUs in single and multi-GPU configurations.

Ray's creators have created a fully-managed platform. The best way to create, scale, deploy, and maintain AI apps on Ray. You can accelerate development and deployment of any AI app, at any scale. Ray has everything you love, but without the DevOps burden. Let us manage Ray for you. Ray is hosted on our cloud infrastructure. This allows you to focus on what you do best: creating great products. Anyscale automatically scales your infrastructure to meet the dynamic demands from your workloads. It doesn't matter if you need to execute a production workflow according to a schedule (e.g. Retraining and updating a model with new data every week or running a highly scalable, low-latency production service (for example. Anyscale makes it easy for machine learning models to be served in production. Anyscale will automatically create a job cluster and run it until it succeeds.

You can use advanced language models and coding to solve a variety of problems. To build cutting-edge applications, leverage large-scale, generative AI models that have deep understandings of code and language to allow for new reasoning and comprehension. These coding and language models can be applied to a variety use cases, including writing assistance, code generation, reasoning over data, and code generation. Access enterprise-grade Azure security and detect and mitigate harmful use. Access generative models that have been pretrained with trillions upon trillions of words. You can use them to create new scenarios, including code, reasoning, inferencing and comprehension. A simple REST API allows you to customize generative models with labeled information for your particular scenario. To improve the accuracy of your outputs, fine-tune the hyperparameters of your model. You can use the API's few-shot learning capability for more relevant results and to provide examples.

The RAPIDS software library, which is built on CUDAX AI, allows you to run end-to-end data science pipelines and analytics entirely on GPUs. It uses NVIDIA®, CUDA®, primitives for low level compute optimization. However, it exposes GPU parallelism through Python interfaces and high-bandwidth memories speed through user-friendly Python interfaces. RAPIDS also focuses its attention on data preparation tasks that are common for data science and analytics. This includes a familiar DataFrame API, which integrates with a variety machine learning algorithms for pipeline accelerations without having to pay serialization fees. RAPIDS supports multi-node, multiple-GPU deployments. This allows for greatly accelerated processing and training with larger datasets. You can accelerate your Python data science toolchain by making minimal code changes and learning no new tools. Machine learning models can be improved by being more accurate and deploying them faster.

Watsonx is a new enterprise-ready AI platform that will multiply the impact of AI in your business. The platform consists of three powerful components, including the watsonx.ai Studio for new foundation models, machine learning, and generative AI; the watsonx.data Fit-for-Purpose Store for the flexibility and performance of a warehouse; and the watsonx.governance Toolkit to enable AI workflows built with responsibility, transparency, and explainability. The foundation models allow AI to be fine-tuned to the unique data and domain expertise of an enterprise with a specificity previously impossible. Use all your data, no matter where it is located. Take advantage of a hybrid cloud infrastructure that provides the foundation data for extending AI into your business. Improve data access, implement governance, reduce costs, and put quality models into production quicker.

Now available: a next-generation enterprise studio for AI developers to train, validate and tune AI models IBM® Watsonx.ai™ AI Studio is part of IBM watsonx™ AI platform. It combines generative AI capabilities powered by foundational models with traditional machine learning into a powerful AI studio that spans the AI lifecycle. With easy-to-use tools, you can build and refine performant prompts to tune and guide models based on your enterprise data. With watsonx.ai you can build AI apps in a fraction the time with a fraction the data. Watsonx.ai offers: End-to end AI governance: Enterprises are able to scale and accelerate AI's impact by using trusted data from across the business. IBM offers the flexibility to integrate your AI workloads and deploy them into your hybrid cloud stack of choice.

Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.

AI Cloud is a new approach that addresses the challenges and opportunities presented by AI today. A single system of records that accelerates the delivery of AI to production in every organization. All users can collaborate in a single environment that optimizes the entire AI lifecycle. The AI Catalog facilitates seamlessly finding, sharing and tagging data. This helps to increase collaboration and speed up time to production. The catalog makes it easy to find the data you need to solve a business problem. It also ensures security, compliance, consistency, and consistency. Contact Support if your database is protected by a network rule that allows connections only from certain IP addresses. An administrator will need to add addresses to your whitelist.

D-I-Y can be used in certain situations, such as weed control. It's a different story to build your AI infrastructure. ONTAP AI consolidates the data center's worth in analytics, training, inference computation, and training into one, 5-petaflop AI system. NetApp ONTAP AI is powered by NVIDIA's DGX™, and NetApp's cloud-connected all flash storage. This allows you to fully realize the promise and potential of deep learning (DL). With the proven ONTAP AI architecture, you can simplify, accelerate and integrate your data pipeline. Your data fabric, which spans from the edge to the core to the cloud, will streamline data flow and improve analytics, training, inference, and performance. NetApp ONTAPAI is the first converged infrastructure platform to include NVIDIA DGX A100 (the world's first 5-petaflop AIO system) and NVIDIA Mellanox®, high-performance Ethernet switches. You get unified AI workloads and simplified deployment.

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.

Generative AI has a profound impact on virtually every industry. It opens up new opportunities for creative workers and knowledge to solve the world's most pressing problems. NVIDIA is empowering generative AI with a powerful suite of cloud services, pretrained foundation models, cutting-edge frameworks and optimized inference engines. NVIDIA AI Foundations is an array of cloud services that enable customization across use cases in areas like text (NVIDIA NeMo™, NVIDIA Picasso), or biology (NVIDIA BIONeMo™. Enjoy the full potential of NeMo, Picasso and BioNeMo cloud-based services powered by NVIDIA DGX™ Cloud, an AI supercomputer. Marketing copy, storyline creation and global translation in many different languages. News, email, meeting minutes and information synthesis.

Setup should be simple. We've listened carefully to the feedback of our community in order to simplify the developer experience. Our billing model was designed from the ground up for cloud-native. You only pay for what you need and there are no surprises. Launch times that are industry-leading will boost productivity. Accelerate the development cycle, innovate and deliver faster results. Blazing fast, simplified, managed Kubernetes. Host applications and scale them as you need, with a 90-second cluster launch time and a free controller plane. Kubernetes-powered enterprise-class compute instances. Multi-region support, DDoS Protection, bandwidth pooling and all the developer tool you need. Fully managed, auto-scaling machine-learning environment. No Kubernetes, ML or Kubernetes expertise is required. Setup and scale managed databases easily from your Civo dashboard, or our developer API. Scale up or down as needed, and only pay for the resources you use.

Katonic Generative AI Platform allows you to build powerful enterprise-grade AI applications in minutes without any coding. Generative AI can boost your employees' productivity and improve your customer service. Create AI-powered digital assistants and chatbots that can access, process and refresh information from documents and dynamic content automatically using pre-built connectors. You can extract information from unstructured texts or uncover insights in specialized domains without creating templates. Transform dense text, such as financial reports, meeting transcripts, etc., into a personalized executive summary, capturing key information. Build recommendation systems to suggest products, content, or services based on past behavior and preferences.

Create, design, prototype and deploy custom AI workflows. Enhance customer engagement and team/personal productivity. Create and embed your website in just minutes. Connect your chatbot to your knowledge base. Instantly summarize and answer questions about audio, video, and website files. Create marketing copy, personalized emails, call summaries and graphics at large scale. Save time with a library of prebuilt pipelines, such as those for chatbots or document search. Share your pipelines to help the marketplace grow. Your data will not be stored on model providers' servers due to our zero-day retention policy and secure infrastructure. Our partnership begins with a free diagnostic, where we assess if your organization is AI-ready. We then create a roadmap to create a turnkey solution that fits into your processes.

Your platform for developing generative AI and custom copilots. Use pre-built and customizable AI model on your data to build solutions faster. Explore a growing collection of models, both open-source and frontier-built, that are pre-built and customizable. Create AI models using a code first experience and an accessible UI validated for accessibility by developers with disabilities. Integrate all your OneLake data into Microsoft Fabric. Integrate with GitHub codespaces, Semantic Kernel and LangChain. Build apps quickly with prebuilt capabilities. Reduce wait times by personalizing content and interactions. Reduce the risk for your organization and help them discover new things. Reduce the risk of human error by using data and tools. Automate operations so that employees can focus on more important tasks.

Substrate is a platform for agentic AI. Elegant abstractions, high-performance components such as optimized models, vector databases, code interpreter and model router, as well as vector databases, code interpreter and model router. Substrate was designed to run multistep AI workloads. Substrate will run your task as fast as it can by connecting components. We analyze your workload in the form of a directed acyclic network and optimize it, for example merging nodes which can be run as a batch. Substrate's inference engine schedules your workflow graph automatically with optimized parallelism. This reduces the complexity of chaining several inference APIs. Substrate will parallelize your workload without any async programming. Just connect nodes to let Substrate do the work. Our infrastructure ensures that your entire workload runs on the same cluster and often on the same computer. You won't waste fractions of a sec per task on unnecessary data transport and cross-regional HTTP transport.

OORT DataHub, a blockchain-powered platform, allows people to contribute to AI development around the world by collecting and preprocessing AI data using smartphones and PCs. AI data transparency and safety built for an "OpenAI". All AI data will be automatically stored on OORT Cloud Storage - a global, decentralized storage network - and verified on the blockchain. DataHub allows users to submit data such as images, audio or video. These data are then used to improve AI models and machine learning models. Users can complete daily challenges, earn $OORT tokens and accumulate points to receive additional benefits. The more tasks you accomplish, the better your chances are of receiving one of our Profit Sharing Certificates. You can easily join and get started with just a few easy steps!

Toolhouse is the platform that offers the best developer experience for AI. Our universal SDK only requires 3 lines of code, and is compatible with all major LLMs. The same syntax is used for streaming or plain responses. Work in any environment - from your local laptop or cloud to your favorite cloud. Install tools in your LLM using the tool store just as you would apps on your smartphone. With just one click, you can deploy semantic search, RAG email sending, sandboxed execution and interpreting of code, sandboxed execution and interpreting of code, and much more. Toolhouse's 3-line SDK is powerful and eliminates all boilerplate for tool use. Developers can save weeks with Toolhouse. It is easy to improve the performance of your LLM with a high-quality infrastructure. Toolhouse offers low-latency tools hosted on reliable and scalable hardware.

Hyperstack, the ultimate self-service GPUaaS Platform, offers the H100 and A100 as well as the L40, and delivers its services to the most promising AI start ups in the world. Hyperstack was built for enterprise-grade GPU acceleration and optimised for AI workloads. NexGen Cloud offers enterprise-grade infrastructure for a wide range of users from SMEs, Blue-Chip corporations to Managed Service Providers and tech enthusiasts. Hyperstack, powered by NVIDIA architecture and running on 100% renewable energy, offers its services up to 75% cheaper than Legacy Cloud Providers. The platform supports diverse high-intensity workloads such as Generative AI and Large Language Modeling, machine learning and rendering.

Enterprise AI applications can be built, deployed, and operated. C3 AI®, Suite uses a unique model driven architecture to speed delivery and reduce the complexity of developing enterprise AI apps. The C3 AI model-driven architecture allows developers to create enterprise AI applications using conceptual models, rather than long code. This has significant benefits: AI applications and models can be used to optimize processes for every product or customer across all regions and businesses. You will see results in just 1-2 quarters. Also, you can quickly roll out new applications and capabilities. You can unlock sustained value - hundreds to billions of dollars annually - through lower costs, higher revenue and higher margins. C3.ai's unified platform, which offers data lineage as well as governance, ensures enterprise-wide governance for AI.

You can quickly provision a VM with everything you need for your deep learning project on Google Cloud. Deep Learning VM Image makes it simple and quick to create a VM image containing all the most popular AI frameworks for a Google Compute Engine instance. Compute Engine instances can be launched pre-installed in TensorFlow and PyTorch. Cloud GPU and Cloud TPU support can be easily added. Deep Learning VM Image supports all the most popular and current machine learning frameworks like TensorFlow, PyTorch, and more. Deep Learning VM Images can be used to accelerate model training and deployment. They are optimized with the most recent NVIDIA®, CUDA-X AI drivers and libraries, and the Intel®, Math Kernel Library. All the necessary frameworks, libraries and drivers are pre-installed, tested and approved for compatibility. Deep Learning VM Image provides seamless notebook experience with integrated JupyterLab support.

Burncloud is one of the leading cloud computing providers, focusing on providing businesses with efficient, reliable and secure GPU rental services. Our platform is based on a systemized design that meets the high-performance computing requirements of different enterprises. Core Services Online GPU Rental Services - We offer a wide range of GPU models to rent, including data-center-grade devices and edge consumer computing equipment, in order to meet the diverse computing needs of businesses. Our best-selling products include: RTX4070, RTX3070 Ti, H100PCIe, RTX3090 Ti, RTX3060, NVIDIA4090, L40 RTX3080 Ti, L40S RTX4090, RTX3090, A10, H100 SXM, H100 NVL, A100PCIe 80GB, and many more. Our technical team has a vast experience in IB networking and has successfully set up five 256-node Clusters. Contact the Burncloud customer service team for cluster setup services.

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

Platform with NVIDIA H100 Tensor core GPUs. Competitive pricing. Support from a dedicated team. Built for large-scale ML workloads. Get the most from multihost training with thousands of H100 GPUs in full mesh connections using the latest InfiniBand networks up to 3.2Tb/s. Best value: Save up to 50% on GPU compute when compared with major public cloud providers*. You can save even more by purchasing GPUs in large quantities and reserving GPUs. Onboarding assistance: We provide a dedicated engineer to ensure smooth platform adoption. Get your infrastructure optimized, and k8s installed. Fully managed Kubernetes - Simplify the deployment and scaling of ML frameworks using Kubernetes. Use Managed Kubernetes to train GPUs on multiple nodes. Marketplace with ML Frameworks: Browse our Marketplace to find ML-focused libraries and applications, frameworks, and tools that will streamline your model training. Easy to use. All new users are entitled to a one-month free trial.

AWS Inferentia Accelerators are designed by AWS for high performance and low cost for deep learning (DL), inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud, Amazon EC2 Inf1 instances. These instances deliver up to 2.3x more throughput and up 70% lower cost per input than comparable GPU-based Amazon EC2 instances. Inf1 instances have been adopted by many customers including Snap, Sprinklr and Money Forward. They have seen the performance and cost savings. The first-generation Inferentia features 8 GB of DDR4 memory per accelerator, as well as a large amount on-chip memory. Inferentia2 has 32 GB of HBM2e, which increases the total memory by 4x and memory bandwidth 10x more than Inferentia.

DataChain connects your unstructured cloud files with AI models, APIs and foundational models to enable instant data insights. Its Pythonic stack accelerates the development by tenfold when switching to Python-based data wrangling, without SQL data islands. DataChain provides dataset versioning to ensure full reproducibility and traceability for each dataset. This helps streamline team collaboration while ensuring data integrity. It allows you analyze your data wherever it is stored, storing raw data (S3, GCP or Azure) and metadata in inefficient datawarehouses. DataChain provides tools and integrations which are cloud-agnostic in terms of both storage and computing. DataChain allows you to query your multi-modal unstructured data. You can also apply intelligent AI filters for training data and snapshot your unstructured dataset, the code used for data selection and any stored or computed meta data.

Deep learning, 3D modelling, simulations and distributed analytics take days or even weeks. GPUonCLOUD’s dedicated GPU servers can do it in a matter hours. You may choose pre-configured or pre-built instances that feature GPUs with deep learning frameworks such as TensorFlow and PyTorch. MXNet and TensorRT are also available. OpenCV is a real-time computer-vision library that accelerates AI/ML model building. Some of the GPUs we have are the best for graphics workstations or multi-player accelerated games. Instant jumpstart frameworks improve the speed and agility in the AI/ML environment through effective and efficient management of the environment lifecycle.

You can easily build, deploy, manage, and monitor computer vision applications using a fully managed, end to end application development environment. This reduces the time it takes to build computer vision apps from days to minutes, at a fraction of the cost of current offerings. You can quickly and easily ingest real-time video streams and images on a global scale. Drag-and-drop interface makes it easy to create computer vision applications. With built-in AI capabilities, you can store and search petabytes worth of data. Vertex AI Vision provides all the tools necessary to manage the lifecycle of computer vision applications. This includes ingestion, analysis and storage, as well as deployment. Connect application output to a data destination such as BigQuery for analytics or live streaming to drive business actions. You can import thousands of video streams from all over the world. Enjoy a monthly pricing structure that allows you to enjoy up-to one-tenth less than the previous offerings.

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

AWS Trainium, the second-generation machine-learning (ML) accelerator, is specifically designed by AWS for deep learning training with 100B+ parameter model. Each Amazon Elastic Comput Cloud (EC2) Trn1 example deploys up to sixteen AWS Trainium accelerations to deliver a low-cost, high-performance solution for deep-learning (DL) in the cloud. The use of deep-learning is increasing, but many development teams have fixed budgets that limit the scope and frequency at which they can train to improve their models and apps. Trainium based EC2 Trn1 instance solves this challenge by delivering a faster time to train and offering up to 50% savings on cost-to-train compared to comparable Amazon EC2 instances.

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.

Infosys Nia™, an enterprise-grade AI platform, simplifies the AI adoption process for IT and Business. Infosys Nia supports the entire enterprise AI journey, from data management, digitization and image capture, model development, and operationalization. Nia's modular, scalable and advanced capabilities meet enterprise needs. Nia Data provides highly efficient tools and frameworks to support further ML experimentation using the Nia AML Workbench. The Nia DocAI platform automates all aspects of document processing, from ingestion to consumption. It uses AI capabilities like InfoExtractor and NLP, cognitive search, and computer vision.

Amazon SageMaker Studio Lab provides a free environment for machine learning (ML), which includes storage up to 15GB and security. Anyone can use it to learn and experiment with ML. You only need a valid email address to get started. You don't have to set up infrastructure, manage access or even sign-up for an AWS account. SageMaker Studio Lab enables model building via GitHub integration. It comes preconfigured and includes the most popular ML tools and frameworks to get you started right away. SageMaker Studio Lab automatically saves all your work, so you don’t have to restart between sessions. It's as simple as closing your computer and returning later. Machine learning development environment free of charge that offers computing, storage, security, and the ability to learn and experiment using ML. Integration with GitHub and preconfigured to work immediately with the most popular ML frameworks, tools, and libraries.

Google Cloud allows you to quickly build your deep learning project. You can quickly prototype your AI applications using Deep Learning Containers. These Docker images are compatible with popular frameworks, optimized for performance, and ready to be deployed. Deep Learning Containers create a consistent environment across Google Cloud Services, making it easy for you to scale in the cloud and shift from on-premises. You can deploy on Google Kubernetes Engine, AI Platform, Cloud Run and Compute Engine as well as Docker Swarm and Kubernetes Engine.

The most complex AI, ML, Deep Learning models can be trained. With just a few clicks, you can scale from a single machine up to a whole fleet of VMs. Lambda Cloud makes it easy to scale up or start your Deep Learning project. You can get started quickly, save compute costs, and scale up to hundreds of GPUs. Every VM is pre-installed with the most recent version of Lambda Stack. This includes major deep learning frameworks as well as CUDA®. drivers. You can access the cloud dashboard to instantly access a Jupyter Notebook development environment on each machine. You can connect directly via the Web Terminal or use SSH directly using one of your SSH keys. Lambda can make significant savings by building scaled compute infrastructure to meet the needs of deep learning researchers. Cloud computing allows you to be flexible and save money, even when your workloads increase rapidly.

RagaAI is a leading AI testing platform which helps enterprises to mitigate AI risks, and make their models reliable and secure. Intelligent recommendations will reduce AI risk across cloud or edge deployments, and optimize MLOps cost. A foundation model designed specifically to revolutionize AI testing. You can easily identify the next steps for fixing dataset and model problems. AI-testing methods are used by many today, and they increase time commitments and reduce productivity when building models. They also leave unforeseen risks and perform poorly after deployment, wasting both time and money. We have created an end-toend AI testing platform to help enterprises improve their AI pipeline and prevent inefficiencies. 300+ tests to identify, fix, and accelerate AI development by identifying and fixing every model, data and operational issue.