Alternatives to Substrate

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.

Your ML model can be served in minutes in any cloud. Unified model packaging format that allows online and offline delivery on any platform. Our micro-batching technology allows for 100x more throughput than a regular flask-based server model server. High-quality prediction services that can speak the DevOps language, and seamlessly integrate with common infrastructure tools. Unified format for deployment. High-performance model serving. Best practices in DevOps are incorporated. The service uses the TensorFlow framework and the BERT model to predict the sentiment of movie reviews. DevOps-free BentoML workflow. This includes deployment automation, prediction service registry, and endpoint monitoring. All this is done automatically for your team. This is a solid foundation for serious ML workloads in production. Keep your team's models, deployments and changes visible. You can also control access via SSO and RBAC, client authentication and auditing logs.

The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems. Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval. Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters. Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely.

Marqo is a complete vector search engine. It's more than just a database. A single API handles vector generation, storage and retrieval. No need to embed your own embeddings. Marqo can accelerate your development cycle. In just a few lines, you can index documents and start searching. Create multimodal indexes, and search images and text combinations with ease. You can choose from a variety of open-source models or create your own. Create complex and interesting queries with ease. Marqo allows you to compose queries that include multiple weighted components. Marqo includes input pre-processing and machine learning inference as well as storage. Marqo can be run as a Docker on your laptop, or scaled up to dozens GPU inference nodes. Marqo is scalable to provide low latency searches on multi-terabyte indices. Marqo allows you to configure deep-learning models such as CLIP for semantic meaning extraction from images.

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.

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.

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).

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.

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.

We are ready to meet all your business needs, whether it is quick engineering, fine-tuning or training. The Together Inference API makes it easy to integrate your new model in your production application. Together AI's elastic scaling and fastest performance allows it to grow with you. To increase accuracy and reduce risks, you can examine how models are created and what data was used. You are the owner of the model that you fine-tune and not your cloud provider. Change providers for any reason, even if the price changes. Store data locally or on our secure cloud to maintain complete data privacy.

Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Users build recommendation applications on Vespa, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. To build production-worthy online applications that combine data and AI, you need more than point solutions: You need a platform that integrates data and compute to achieve true scalability and availability - and which does this without limiting your freedom to innovate. Only Vespa does this. Together with Vespa's proven scaling and high availability, this empowers you to create production-ready search applications at any scale and with any combination of features.

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.

We've already done the hard work of bundling, pre-configuring and integrating all the components that you need to build solutions and integrate LLMs into your business processes. ConfidentialMind allows you to jump into action. Deploy an endpoint for powerful open-source LLMs such as Llama-2 and turn it into an LLM API. Imagine ChatGPT on your own cloud. This is the most secure option available. Connects the rest with the APIs from the largest hosted LLM provider like Azure OpenAI or AWS Bedrock. ConfidentialMind deploys a Streamlit-based playground UI with a selection LLM-powered productivity tool for your company, such as writing assistants or document analysts. Includes a vector data base, which is critical for most LLM applications to efficiently navigate through large knowledge bases with thousands documents. You can control who has access to your team's solutions and what data they have.

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.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Vectorize is an open-source platform that transforms unstructured data to optimized vector search indices. This allows for retrieval-augmented generation pipelines. It allows users to import documents, or connect to external systems of knowledge management to extract natural languages suitable for LLMs. The platform evaluates chunking and embedding methods in parallel. It provides recommendations or allows users to choose the method they prefer. Vectorize automatically updates a real-time pipeline vector with any changes to data once a vector configuration has been selected. This ensures accurate search results. The platform provides connectors for various knowledge repositories and collaboration platforms as well as CRMs. This allows seamless integration of data in generative AI applications. Vectorize also supports the creation and update of vector indexes within preferred vector databases.

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!

Metal is a fully-managed, production-ready ML retrieval platform. Metal embeddings can help you find meaning in unstructured data. Metal is a managed services that allows you build AI products without having to worry about managing infrastructure. Integrations with OpenAI and CLIP. Easy processing & chunking of your documents. Profit from our system in production. MetalRetriever is easily pluggable. Simple /search endpoint to run ANN queries. Get started for free. Metal API Keys are required to use our API and SDKs. Authenticate by populating headers with your API Key. Learn how to integrate Metal into your application using our Typescript SDK. You can use this library in JavaScript as well, even though we love TypeScript. Fine-tune spp programmatically. Indexed vector data of your embeddings. Resources that are specific to your ML use case.

Vald is a distributed, fast, dense and highly scalable vector search engine that approximates nearest neighbors. Vald was designed and implemented using the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT for searching neighbors. Vald supports automatic vector indexing, index backup, horizontal scaling, which allows you to search from billions upon billions of feature vector data. Vald is simple to use, rich in features, and highly customizable. Usually, the graph must be locked during indexing. This can cause stop-the world. Vald uses distributed index graphs so that it continues to work while indexing. Vald has its own highly customizable Ingress/Egress filter. This can be configured to work with the gRPC interface. Horizontal scaling is available on memory and cpu according to your needs. Vald supports disaster recovery by enabling auto backup using Persistent Volume or Object Storage.

Azure Managed Redis offers the latest Redis innovations and industry-leading availability. It also has a cost-effective Total Cost Of Ownership (TCO) that is designed for hyperscale clouds. Azure Managed Redis provides these capabilities on a trusted platform, empowering businesses with the ability to scale and optimize generative AI applications in a seamless manner. Azure Managed Redis uses the latest Redis innovations for high-performance and scalable AI applications. Its features, such as in-memory storage, vector similarity searches, and real-time computing, allow developers to handle large datasets, accelerate machine-learning, and build faster AI applications. Its interoperability to Azure OpenAI Service allows AI workloads that are ready for mission-critical applications to be faster, more scalable and more reliable.

A vector database designed for scalable similarity searches. Open-source, highly scalable and lightning fast. Massive embedding vectors created by deep neural networks or other machine learning (ML), can be stored, indexed, and managed. Milvus vector database makes it easy to create large-scale similarity search services in under a minute. For a variety languages, there are simple and intuitive SDKs. Milvus is highly efficient on hardware and offers advanced indexing algorithms that provide a 10x speed boost in retrieval speed. Milvus vector database is used in a variety a use cases by more than a thousand enterprises. Milvus is extremely resilient and reliable due to its isolation of individual components. Milvus' distributed and high-throughput nature makes it an ideal choice for large-scale vector data. Milvus vector database uses a systemic approach for cloud-nativity that separates compute and storage.

Vector search can give you a competitive edge. Streamline your AI/ML workflows, reduce costs and stay ahead with up to a 10x faster time-to market. ApertureDB’s unified multimodal management of data will free your AI teams from data silos and allow them to innovate. Setup and scale complex multimodal infrastructure for billions objects across your enterprise in days instead of months. Unifying multimodal data with advanced vector search and innovative knowledge graph, combined with a powerful querying engine, allows you to build AI applications at enterprise scale faster. ApertureDB will increase the productivity of your AI/ML team and accelerate returns on AI investment by using all your data. You can try it for free, or schedule a demonstration to see it in action. Find relevant images using labels, geolocation and regions of interest. Prepare large-scale, multi-modal medical scanning for ML and Clinical studies.

Use user feedback and semantic relevance to reliably retrieve optimal document chunks for your retrieval-augmented generation system. In your search system, combine semantic relevance with document freshness because recent results are more accurate. Create a personalized ecommerce feed in real-time using user vectors based on the SKU embeddings that were viewed by the user. A vector index in your warehouse can be used to discover behavioral clusters among your customers. Use spaces to build your indices, and run queries all within a Python Notebook.

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).

VectorDB is a lightweight Python program for storing and retrieving texts using chunking techniques, embedding techniques, and vector search. It offers an easy-to use interface for searching, managing, and saving textual data, along with metadata, and is designed to be used in situations where low latency and speed are essential. When working with large language model datasets, vector search and embeddings become essential. They allow for efficient and accurate retrieval relevant information. These techniques enable quick comparisons and search, even with millions of documents. This allows you to find the most relevant search results in a fraction the time of traditional text-based methods. The embeddings also capture the semantic meaning in the text. This helps improve the search results, and allows for more advanced natural-language processing tasks.

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.

GMI GPU Cloud allows you to create generative AI applications within minutes. GMI Cloud offers more than just bare metal. Train, fine-tune and infer the latest models. Our clusters come preconfigured with popular ML frameworks and scalable GPU containers. Instantly access the latest GPUs to power your AI workloads. We can provide you with flexible GPUs on-demand or dedicated private cloud instances. Our turnkey Kubernetes solution maximizes GPU resources. Our advanced orchestration tools make it easy to allocate, deploy and monitor GPUs or other nodes. Create AI applications based on your data by customizing and serving models. GMI Cloud allows you to deploy any GPU workload quickly, so that you can focus on running your ML models and not managing infrastructure. Launch pre-configured environment and save time building container images, downloading models, installing software and configuring variables. You can also create your own Docker images to suit your needs.

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.

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).

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.

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.

AutoTrain is a new way to automatically evaluate, deploy and train state-of-the art Machine Learning models. AutoTrain, seamlessly integrated into the Hugging Face ecosystem, is an automated way to develop and deploy state of-the-art Machine Learning model. Your account is protected from all data, including your training data. All data transfers are encrypted. Today's options include text classification, text scoring and entity recognition. Files in CSV, TSV, or JSON can be hosted anywhere. After training is completed, we delete all training data. Hugging Face also has an AI-generated content detection tool.

We offer a set of world-class tools and assets to convert ideas into production ready AI solutions. Build and deploy custom Generative AI end-to-end solutions on our unified Platform, and avoid the hassle of tool fragmentation or platform switching. Launch your next AI-based solution using a single API endpoint. It has never been easier to create, maintain, and improve AI systems. Subscribe to models and datasets on aiXplain’s marketplace. Subscribe to models and data sets to use with aiXplain's no-code/low code tools or the SDK.

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.

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 AI Enterprise is the software layer of NVIDIA AI Platform. It accelerates the data science pipeline, streamlines development and deployments of production AI including generative AI, machine vision, speech AI, and more. NVIDIA AI Enterprise has over 50 frameworks, pre-trained models, and development tools. It is designed to help enterprises get to the forefront of AI while simplifying AI to make it more accessible to all. Artificial intelligence and machine learning are now mainstream and a key part of every company's competitive strategy. Enterprises face the greatest challenges when it comes to managing siloed infrastructure in the cloud and on-premises. AI requires that their environments be managed as a single platform and not as isolated clusters of compute.

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.

Astra DB from DataStax is a real-time vector database as a service for developers that need to get accurate Generative AI applications into production, fast. Astra DB gives you a set of elegant APIs supporting multiple languages and standards, powerful data pipelines and complete ecosystem integrations. Astra DB enables you to quickly build Gen AI applications on your real-time data for more accurate AI that you can deploy in production. Built on Apache Cassandra, Astra DB is the only vector database that can make vector updates immediately available to applications and scale to the largest real-time data and streaming workloads, securely on any cloud. Astra DB offers unprecedented serverless, pay as you go pricing and the flexibility of multi-cloud and open-source. You can store up to 80GB and/or perform 20 million operations per month. Securely connect to VPC peering and private links. Manage your encryption keys with your own key management. SAML SSO secure account accessibility. You can deploy on Amazon, Google Cloud, or Microsoft Azure while still compatible with open-source Apache Cassandra.