Alternatives to dstack

Our decentralized platform streamlines AI data collection and labeling through a worldwide contributor network. By combining crowdsourcing with blockchain technology, we deliver high-quality, traceable datasets. Platform Highlights: Worldwide Collection: Tap into global contributors for comprehensive data gathering Blockchain Security: Every contribution tracked and verified on-chain Quality Focus: Expert validation ensures exceptional data standards Platform Benefits: Rapid scaling of data collection Complete data providence tracking Validated datasets ready for AI use Cost-efficient global operations Flexible contributor network How It Works: Define Your Needs: Create your data collection task Community Activation: Global contributors notified and start gathering data Quality Control: Human verification layer validates all contributions Sample Review: Get dataset sample for approval Full Delivery: Complete dataset delivered once approved

At competitive prices, you can access dedicated machines that are pre-configured with professional AI development tools. Apolo offers everything from HPC resources to a complete AI platform with a built-in ML toolkit. Apolo is available in a distributed architecture or as a dedicated enterprise cloud. It can also be deployed as a white-label multi-tenant solution that supports dedicated instances or self service cloud. Apolo creates a fully-fledged AI development environment, with all the tools needed at your fingertips. Apolo automates and manages the infrastructure for successful AI development. Apolo's AI services seamlessly integrate your on-prem resources and cloud resources. They also deploy pipelines and integrate your commercial and open-source development tools. Apolo provides enterprises with the resources and tools necessary to achieve breakthroughs when it comes to AI.

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.

With a fully automated cloud infrastructure, experts can explore data and write stable codes at the same time. Zerve’s data science environment gives data scientists and ML teams a unified workspace to explore, collaborate and build data science & AI project like never before. Zerve provides true language interoperability. Users can use Python, R SQL or Markdown in the same canvas and connect these code blocks. Zerve offers unlimited parallelization, allowing for code blocks and containers to run in parallel at any stage of development. Analysis artifacts can be automatically serialized, stored and preserved. This allows you to change a step without having to rerun previous steps. Selecting compute resources and memory in a fine-grained manner for complex data transformation.

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.

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.

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.

Distributed training is possible without changing the model code. Determined takes care of provisioning, networking, data load, and fault tolerance. Our open-source deep-learning platform allows you to train your models in minutes and hours, not days or weeks. You can avoid tedious tasks such as manual hyperparameter tweaking, re-running failed jobs, or worrying about hardware resources. Our distributed training implementation is more efficient than the industry standard. It requires no code changes and is fully integrated into our state-ofthe-art platform. With its built-in experiment tracker and visualization, Determined records metrics and makes your ML project reproducible. It also allows your team to work together more easily. Instead of worrying about infrastructure and errors, your researchers can focus on their domain and build upon the progress made by their team.

Our cloud-based platform automatically configures your ComfyUI work flow. Each workflow comes with all the necessary custom nodes and model, ensuring an easy start. ComfyUI Cloud GPUs are high-performance and will unlock the full potential of any creative project. Profit from faster processing rates at market-leading speeds, ensuring time and cost savings. ComfyUI cloud can be launched instantly without installation, resulting in a fully-prepared environment that is ready to use immediately. ComfyUI workflows are ready to use, with pre-set nodes and models. This eliminates the need for configuration in the cloud. Our powerful GPUs will boost productivity and efficiency for creative projects.

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.

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.

Multi-model AI can help you build a better AI. AI models are an entirely new type of building block. Sieve makes it easy to use these building block to understand audio, create video, and more. The latest models are available in just a few line of code and there is a set of production-ready applications for many different use cases. Import your favorite models like Python packages. Visualize results using auto-generated interfaces created for your entire team. Easily deploy custom code. Define your environment computation in code and deploy it with a single command. Fast, scalable infrastructure with no hassle. Sieve is designed to scale automatically as your traffic grows with no extra configuration. Package models using a simple Python decorator, and deploy them instantly. A fully-featured observability layer that allows you to see what's going on under the hood. Pay only for the seconds you use. Take full control of your costs.

OpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2.

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.

Our auto-scaling AIs allow you to access powerful generative AIs models without any management. API calls are now available for generative AI models such as stable diffusion, dreambooth and pix2pix. Our scalable Rest APIs allow you to build applications on top of generative AI models. They integrate seamlessly and cost a fraction of what other alternatives do. Integrations that are seamless with your existing systems without extensive development. Our APIs are easy to integrate into your workflow, with support for stacks such as CURL, Python Node.js, and PHP. We harness the computing power of millions decentralised crypto mining machines around the world, optimize them for machine-learning and package them with popular AI models such as Stable Diffusion. We can deliver Generative AI through APIs that are easily integrated and scalable by leveraging these decentralized resources.

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.

Emly Labs, an AI framework, is designed to make AI accessible to users of all technical levels via a user-friendly interface. It offers AI project-management with tools that automate workflows for faster execution. The platform promotes team collaboration, innovation, and data preparation without code. It also integrates external data to create robust AI models. Emly AutoML automates model evaluation and data processing, reducing the need for human input. It prioritizes transparency with AI features that are easily explained and robust auditing to ensure compliance. Data isolation, role-based accessibility, and secure integrations are all security measures. Emly's cost effective infrastructure allows for on-demand resource provisioning, policy management and risk reduction.

Public datasets can help you improve the accuracy of your machine-learning models. Use curated datasets to save time on data preparation and discovery. These datasets are ready for use in machine-learning workflows and can be accessed from Azure services. Consider real-world factors which can have an impact on business outcomes. By incorporating features of curated datasets in your machine learning model, you can improve the accuracy and reduce the time required for data preparation. Share datasets with the growing community of data analysts and developers. Azure Open Datasets can be used to deliver insights at hyperscale with Azure's machine-learning and data analytics solutions. Open Datasets are free to use. Open Datasets are free to use, but you'll only be charged for the Azure services you consume, such as virtual machines, storage, networking resources and machine learning. Open data that has been curated and made available on Azure.

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.

Choose a model from our open-source collection, run it and access the model API within your product. No matter what model you are using for Image Recognition or Language Processing, all models come pre-trained and packaged with an easy-to use API. There are many models to choose from, and the list is growing. We curate and fine-tune only the best models from HuggingFace or Github. You can either host the model yourself or get your API-Key and endpoint with just one click. Cargoship keeps up with the advancement of AI so you don’t have to. The Cargoship Model Store has a collection that can be used for any ML use case. You can test them in demos and receive detailed guidance on how to implement the model. No matter your level of expertise, our team will pick you up and provide you with detailed instructions.

IBM Watson Machine Learning, a full-service IBM Cloud offering, makes it easy for data scientists and developers to work together to integrate predictive capabilities into their applications. The Machine Learning service provides a set REST APIs that can be called from any programming language. This allows you to create applications that make better decisions, solve difficult problems, and improve user outcomes. Machine learning models management (continuous-learning system) and deployment (online batch, streaming, or online) are available. You can choose from any of the widely supported machine-learning frameworks: TensorFlow and Keras, Caffe or PyTorch. Spark MLlib, scikit Learn, xgboost, SPSS, Spark MLlib, Keras, Caffe and Keras. To manage your artifacts, you can use the Python client and command-line interface. The Watson Machine Learning REST API allows you to extend your application with artificial intelligence.

The easiest way to create serverless AI agents with memory. Start building agentic pipes and tools locally first. Deploy serverless in one command. Base AI allows developers to create high-quality AI agents that have memory (RAG) in TypeScript, and then deploy serverless using Langbase's (creators of Base AI) highly scalable API. Base AI is a web-first solution with TypeScript and a familiar API. You can integrate AI into your web stack with ease, using Next.js or Vue or vanilla Node.js. Base AI is a great tool for delivering AI features faster. Create AI features on-premises with no cloud costs. Git is integrated out of the box so you can branch AI models and merge them like code. Complete observability logs allow you to debug AI-like JavaScript and trace data points, decisions, and outputs. It's Chrome DevTools, but for AI.

Construct your model as a live interactive graph. View data flowing through the architecture of your visualized model. View and edit the model architecture at the atomic level. Graphbook offers X-ray transparency without black boxes. Graphbook checks data type and form in real-time, with clear error messages. This makes model debugging easy. Graphbook abstracts out software dependencies and configuration of the environment, allowing you to focus on your model architecture and data flows with the computing resources required. Cerbrec Graphbook transforms cumbersome AI modeling into a user friendly experience. Graphbook, which is backed by a growing community that includes machine learning engineers and data science experts, helps developers fine-tune their language models like BERT and GPT using text and tabular data. Everything is managed out of box, so you can preview how your model will behave.

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 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.

Instantly provide your enterprise clients with answers and insights based on their business data. You are in a race against time to launch your product and balancing accuracy, latency and costs can be a nightmare. SaaS teams can use pre-configured copilots or custom rulebooks to create the latest technology. Our agents answer questions in simple english. The output is presented in smart insights, visualisations and other formats. Our advanced LLMs detects and generates all of your proactive insights. Our advanced LLMs automatically detect & prioritize & executes actions for you.

Build complex backend agents using multiple steps with an action-based orchestration. All LLMs are supported. Build a fully customized UI without writing a line of code for your agent, and serve it on your own domain. Track your agent's every move, literally, to cope with the nondeterministic nature LLMs. Access control can be built at a finer grain for your application, data and agent. A fine-tuned, specialized model to accelerate software development. Automatically handle concurrency and rate limiting.

The first AI platform for business that does not require any code allows you to train and deploy GPT models in any use case. Our experts are ready to assist you. Our cost-effective tools can be used to fine-tune your open source models using your own data. You can deploy your open source model securely across multiple clouds, without having to rely on a third-party vendor for your valuable data. Our team of experts will create the perfect app for your needs. Our tool allows you to easily monitor your usage, and reduce costs. Let our team of experts help you solve your problems. Automate your customer service and efficiently classify your calls. Our advanced solution allows you to streamline customer interaction and improve service delivery. Build a robust system to detect fraud and anomalies based on previously flagged information.

Lunary is a platform for AI developers that helps AI teams to manage, improve and protect chatbots based on Large Language Models (LLM). It includes features like conversation and feedback tracking as well as analytics on costs and performance. There are also debugging tools and a prompt directory to facilitate team collaboration and versioning. Lunary integrates with various LLMs, frameworks, and languages, including OpenAI, LangChain and JavaScript, and offers SDKs in Python and JavaScript. Guardrails to prevent malicious prompts or sensitive data leaks. Deploy Kubernetes/Docker in your VPC. Your team can judge the responses of your LLMs. Learn what languages your users speak. Experiment with LLM models and prompts. Search and filter everything in milliseconds. Receive notifications when agents do not perform as expected. Lunary's core technology is 100% open source. Start in minutes, whether you want to self-host or use the cloud.

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.

CORE is a high performance computing platform that can be used for a variety of applications. CORE is easy to use with its point-and-click interface. You can run the most complex applications. CORE provides unlimited computing power on-demand. Cloud computing is available without the high-cost. CORE for teams offers powerful tools that allow you to sort, filter, create, connect, and create users, machines, networks, and machines. With an intuitive and simple GUI, it's easier than ever to see all of your infrastructure from one place. It is easy to add Active Directory integration or VPN through our simple but powerful management console. It's now possible to do things that used to take days, or even weeks. Even complex network configurations can be managed with just a few clicks.

We power customized and scalable generative artificial intelligence for the world's largest institutions. We provide institutions with category-leading tools and infrastructure to integrate their private and relevant public data, allowing them to leverage commercial generative AI in a way they could not before. Access in-depth analytics and understand how your AI can add value. Autonomous model tuning: Watch as your AI improves itself, fine-tuning performance based on data in real-time and user interactions. Control AI costs precisely, from the overall budget to the individual API key usage. Chi Core will transform your AI journey, simplify and increase the value of AI roadmaps, while seamlessly integrating cutting edge AI into your business technology stack.

Martian outperforms GPT-4 across OpenAI's evals (open/evals). Martian outperforms GPT-4 in all OpenAI's evaluations (open/evals). We transform opaque black boxes into interpretable visual representations. Our router is our first tool built using our model mapping method. Model mapping is being used in many other applications, including transforming transformers from unintelligible matrices to human-readable programs. Automatically reroute your customers to other providers if a company has an outage or a high latency period. Calculate how much money you could save using the Martian Model Router by using our interactive cost calculator. Enter the number of users and tokens per session. Also, specify how you want to trade off between cost and quality.

Granica AI efficiency platform lowers costs for storing and accessing data, while maintaining its privacy so that it can be used to train. Granica is native to AWS/GCP, a petabyte-scale platform, and developer-first. Granica makes AI pipelines faster, more efficient, and more performant. Efficiency is a layer of the AI stack. Byte-granular reduction of data uses novel compression algorithms to reduce costs for storing and transferring objects in Amazon S3 or Google Cloud Storage up to 80%, and API costs up to 90%. Estimate your cloud environment in 30 minutes, using a sample of read-only data from your S3/GCS. No need to allocate budgets or perform a total cost of ownership. Granica deploys to your environment and VPC while respecting your security policies. Granica supports many data types for AI/ML/analytics. It also offers lossy and lossless compression options. Detect and protect sensitive information even before it's persisted in your cloud object store.

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.

Llama Stack is a flexible framework designed to simplify the development of applications utilizing Meta’s Llama language models. It features a modular client-server architecture that allows developers to customize their setup by integrating different providers for inference, memory, agents, telemetry, and evaluations. With pre-configured distributions optimized for various deployment scenarios, Llama Stack enables a smooth transition from local development to production. It supports multiple programming languages, including Python, Node.js, Swift, and Kotlin, making it accessible across different tech stacks. Additionally, the framework provides extensive documentation and sample applications to help developers efficiently build and deploy Llama-powered solutions.

The prompt engineering platform allows you to experiment with different prompt versions. You can also evaluate and compare prompts in a series of tests, optimize prompts by one click, share and more. Optimize your AI development workflow. Key features that help you identify and get the best prompts for production use cases. Evaluation allows for a side-by-side comparison between prompts in test cases. Import test cases from CSV and define custom metrics for evaluation. Automatic template and prompt optimization can improve LLM results. View and manage all versions of the prompt and create OpenAI Functions. You can access all your prompts programmatically. This includes observability and analytics. Calculate the cost, latency and effectiveness of each prompt. Parea can help you improve your prompt engineering workflow. Parea helps developers improve the performance of LLM apps by implementing rigorous testing and versioning.

Experience JinaChat - a LLM service designed for professionals. JinaChat is a multimodal chat service that goes beyond text and includes images. Enjoy our free short interactions below 100 tokens. Our API allows developers to build complex applications by leveraging long conversation histories. JinaChat is the future of LLM, with multimodal conversations that are long-memory and affordable. Modern LLM applications are often based on long prompts or large memory, which can lead to high costs if the same prompts are sent repeatedly to the server. JinaChat API solves this issue by allowing you to carry forward previous conversations, without having to resend the entire prompt. This is a great way to save both time and money when developing complex applications such as AutoGPT.

Declarative machine-learning systems offer the best combination of flexibility and simplicity, allowing for the fastest way to implement state-of-the art models. The system works by asking users to specify the "what" and then the system will figure out the "how". Start with smart defaults and iterate down to the code level on parameters. With Ludwig at Uber, and Overton from Apple, our team pioneered declarative machine-learning systems in industry. You can choose from our pre-built data connectors to support your databases, data warehouses and lakehouses as well as object storage. You can train state-of the-art deep learning models without having to manage infrastructure. Automated Machine Learning achieves the right balance between flexibility and control in a declarative manner. You can train and deploy models quickly using a declarative approach.

Use AI algorithms to receive intelligent suggestions about your API design. Automated recommendations tailored to your project will accelerate development. AI can automatically generate your database. Streamline deployment and increase productivity. Create and implement automated workflows to ensure a smooth, efficient development cycle. Customize automation processes to meet your project requirements. Workflows that are adaptable will allow you to create a personalized experience. Enjoy the flexibility to manage diverse data sources in a single, organized workspace. Workspaces can be designed to reflect the structure of projects. Create dedicated workspaces that can house multiple data sources seamlessly. Streamlining tasks by automating processes, increasing efficiency, and reducing the amount of manual effort. Each user has their own instance(s). Custom logic can be used to handle complex data operations.

SuperAGI SuperCoder combines AI-native development platform & AI agents in order to enable fully autonomous software creation starting with the python programming language & frameworks. SuperCoder 2.0 leverages Large Action Models (LAMs) and LLMs fine-tuned to python code creation leading to one-shot or few-shot python functional programming with significantly higher accuracy on SWE-bench and Codebench SuperCoder 2.0 is an autonomous system that combines software guardrails for Flask & Django development frameworks with SuperAGI's Generally Intelligent Developer Agents in order to deliver complex real-world software systems SuperCoder 2.0 integrates deeply with existing developer stacks such as Jira or Github, Jenkins, CSPs, and QA solutions like BrowserStack/Selenium Clouds, to ensure a seamless experience in software development.

You can build, deploy and monitor AI-powered applications in just a few simple clicks. No coding or machine learning expertise is required. Stop puzzling together disjointed tools, featuring-engineering your way to production, and calling in favors to get results. With a platform that is designed to build, monitor and improve AI solutions throughout their entire lifecycle, managing all your AI initiatives will be a breeze. No more hyperparameter tuning and feature engineering. Graft guarantees that everything you build will work in production because the platform is production. Your AI solution should be tailored to your business. You retain control over the AI solution, from foundation models to pretraining and fine-tuning. Unlock the value in your unstructured data, such as text, images, videos, audios, and graphs. Control and customize solutions at scale.

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.

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.

Optimizing continuous pre-training to enrich models with proprietary data. Assuring domain-specific models provide a smooth user experience. Create a production-friendly RAG pipeline that offers ongoing support. With Arcee's SLM Adaptation system, you do not have to worry about fine-tuning, infrastructure set-up, and all the other complexities involved in stitching together solutions using a plethora of not-built-for-purpose tools. Our product's domain adaptability allows you to train and deploy SLMs for a variety of use cases. Arcee's VPC service allows you to train and deploy your SLMs while ensuring that what belongs to you, stays yours.

With our enterprise Gen AI stack you can go beyond your imagination. You can instantly offload manual tasks and give them to powerful assistants. The sky is the limit. For enterprises that place data security first, fine-tune generative AI models and deploy them on your own cloud securely.

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).

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.

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.

Vishwa.ai, an AutoOps Platform for AI and ML Use Cases. It offers expert delivery, fine-tuning and monitoring of Large Language Models. Features: Expert Prompt Delivery : Tailored prompts tailored to various applications. Create LLM Apps without Coding: Create LLM workflows with our drag-and-drop UI. Advanced Fine-Tuning : Customization AI models. LLM Monitoring: Comprehensive monitoring of model performance. Integration and Security Cloud Integration: Supports Google Cloud (AWS, Azure), Azure, and Google Cloud. Secure LLM Integration - Safe connection with LLM providers Automated Observability for efficient LLM Management Managed Self Hosting: Dedicated hosting solutions. Access Control and Audits - Ensure secure and compliant operations.