Alternatives to Sieve

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.

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.

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

Unexpected outcomes happen all the time. You can pinpoint the source of errors or surprises in real-time with surgical precision when you have full visibility of the entire chain of calls. Unit testing is a key component of software engineering to create production-ready, performant applications. LangSmith offers the same functionality for LLM apps. LangSmith allows you to create test datasets, execute your applications on them, and view results without leaving the application. LangSmith allows mission-critical observability in just a few lines. LangSmith was designed to help developers harness LLMs' power and manage their complexity. We don't just build tools. We are establishing best practices that you can rely upon. Build and deploy LLM apps with confidence. Stats on application-level usage. Feedback collection. Filter traces and cost measurement. Dataset curation - compare chain performance - AI-assisted assessment & embrace best practices.

Dynamiq was built for engineers and data scientist to build, deploy and test Large Language Models, and to monitor and fine tune them for any enterprise use case. Key Features: Workflows: Create GenAI workflows using a low-code interface for automating tasks at scale Knowledge & RAG - Create custom RAG knowledge bases in minutes and deploy vector DBs Agents Ops - Create custom LLM agents for complex tasks and connect them to internal APIs Observability: Logging all interactions and using large-scale LLM evaluations of quality Guardrails: Accurate and reliable LLM outputs, with pre-built validators and detection of sensitive content. Fine-tuning : Customize proprietary LLM models by fine-tuning them to your liking

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.

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.

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

Semantic Kernel, a lightweight open-source development tool, allows you to easily build AI agents, and integrate the latest AI model into your C# or Python codebase. It is a middleware that allows rapid delivery of enterprise grade solutions. Semantic Kernel is flexible, modular and observable, which is why Microsoft and other Fortune 500 firms use it. With security-enhancing features like hooks, filters, and telemetry, you can be confident that you are delivering responsible AI at scale. It's reliable and committed to non-breaking changes. Version 1.0+ is supported across C# Python and Java. Existing chat-based APIs can be easily extended to support other modalities such as voice and video. Semantic Kernel is future-proof and connects your code with the latest AI models that evolve as technology advances.

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.

A managed PaaS that includes a low-code visual editor, VectorDB and integrations with apps and models to build, test, and deploy high-performance AI applications on the edge. Eliminate costly and error-prone work. Drag and drop agents, apps, data and models to find the best solution. Deployment in less than 60 seconds, and a 50% reduction in latency. Observe, iterate, and test seamlessly. Visibility and tools are essential for accuracy and reliability. Use data-driven decision making with reports on usage, LLM and request. View real-time traces per node. Experiments allow you to optimize embeddings and prompts, models and more. All you need to launch and iterate at large scale. Community of smart-minded builders who share their insights, experiences & feedback. Distilling the most useful tips, tricks and techniques for AI application developers. A platform that allows you to build agentic systems as if you were a 100-person team. A simple and intuitive frontend for managing AI applications and collaborating with them.

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.

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.

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.

Exspanse simplifies the path between development and business value. Build, train and rapidly deploy powerful machine-learning models using a single interface that scales with your business. Exspanse Notebook allows you to train, tune and prototype models with the help from high-powered GPUs, processors & AI code assistant. Rapid deploy allows you to go beyond training and modeling by deploying models as APIs directly from an Exspanse Notebook. Clone and publish unique AI project to DeepSpace AI Marketplace to advance the AI Community. Power, efficiency and collaboration all in one platform. You can maximize your impact as a data scientist working alone. Our integrated platform allows you to manage and accelerate the AI development process. Turn your innovative ideas quickly and efficiently into working models. You can seamlessly transition from building AI solutions to deploying them, without needing extensive DevOps expertise.

BenchLLM allows you to evaluate your code in real-time. Create test suites and quality reports for your models. Choose from automated, interactive, or custom evaluation strategies. We are a group of engineers who enjoy building AI products. We don't want a compromise between the power, flexibility and predictability of AI. We have created the open and flexible LLM tool that we always wanted. CLI commands are simple and elegant. Use the CLI to test your CI/CD pipeline. Monitor model performance and detect regressions during production. Test your code in real-time. BenchLLM supports OpenAI (Langchain), and any other APIs out of the box. Visualize insightful reports and use multiple evaluation strategies.

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.

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.

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.

Fine-tuning is when you take a dataset, and create a model to use AI. This is not always possible without programming so we created a solution in RIku that handles everything in a very easy format. Fine-tuning unlocks an entirely new level of power for artificial intelligence and we are excited to help you explore this. Public Share Links are landing pages you can create for any of the prompts. These can be designed with your brand in mind, including colors and adding your logo. These links can be shared with anyone, and if they have access to the password to unlock it they will be able make generations. No-code assistant builder for your audience. We found that projects using multiple large languages models have a lot of problems. They all return their outputs in a slightly different way.

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.

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).

The next-generation low-code solution combines AI-driven customisation with full code access to provide ultimate flexibility. You can easily customize, style and launch your SaaS MVP. Our low-code framework allows you to build your site quickly and easily. AI will help you create a fully functional SaaS MVP. Our platform, which uses Next.js, Tailwind and other technologies, integrates seamlessly with NextAuth Stripe and more. This ensures a robust, modern development experience. You have full access to the source code, allowing you to customize, scale and modify your SaaS app to align perfectly with your vision. You can save time and effort by simply entering your information into the MVP. Use generative AI to automate the writing of copy, styling, and customizing every aspect of your SaaS MVP. This will streamline the entire development process.

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

LMOps is a stack that allows you to launch production-ready applications for monitoring, model management and more. Portkey is a replacement for OpenAI or any other provider APIs. Portkey allows you to manage engines, parameters and versions. Switch, upgrade, and test models with confidence. View aggregate metrics for your app and users to optimize usage and API costs Protect your user data from malicious attacks and accidental exposure. Receive proactive alerts if things go wrong. Test your models in real-world conditions and deploy the best performers. We have been building apps on top of LLM's APIs for over 2 1/2 years. While building a PoC only took a weekend, bringing it to production and managing it was a hassle! We built Portkey to help you successfully deploy large language models APIs into your applications. We're happy to help you, regardless of whether or not you try Portkey!

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.

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.

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.

Create & Share Delightful Apps for Machine Learning. Gradio allows you to quickly and easily demo your machine-learning model. It has a friendly interface that anyone can use, anywhere. Installing Gradio is easy with pip. It only takes a few lines of code to create a Gradio Interface. You can choose between a variety interface types to interface with your function. Gradio is available as a webpage or embedded into Python notebooks. Gradio can generate a link that you can share publicly with colleagues to allow them to interact with your model remotely using their own devices. Once you have created an interface, it can be permanently hosted on Hugging Face. Hugging Face Spaces hosts the interface on their servers and provides you with a shareable link.

The open-source ML observability Platform. From validation to production, evaluate, test, and track ML models. From tabular data up to NLP and LLM. Built for data scientists and ML Engineers. All you need to run ML systems reliably in production. Start with simple ad-hoc checks. Scale up to the full monitoring platform. All in one tool with consistent APIs and metrics. Useful, beautiful and shareable. Explore and debug a comprehensive view on data and ML models. Start in a matter of seconds. Test before shipping, validate in production, and run checks with every model update. By generating test conditions based on a reference dataset, you can skip the manual setup. Monitor all aspects of your data, models and test results. Proactively identify and resolve production model problems, ensure optimal performance and continually improve it.

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.

Gen App Builder is unique because, unlike other generative AI solutions for developers, it provides an orchestration layer which abstracts the complexity involved in combining enterprise systems and generative AI tools, resulting in a smooth and helpful user experience. Gen App Builder offers step-by-step orchestration for search and conversational apps with pre-built workflows to help developers set up and deploy their applications. Gen App Builder allows developers to: Build in minutes or even hours. Google's conversational and search tools powered with foundation models allow organizations to quickly create high-quality experiences which can be integrated into applications and websites.

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.

We'll host your website so you can focus on building. Our rest API is easy to use. No limits, no headaches. We have all the information you need. Don't pay for nothing. We only charge for use. Our support team is also our tech team. You'll get support directly, not through a series of hoops. Our flexible infrastructure allows us scale with you as your business grows and can handle spikes in activity. Our stable diffusion API allows you to easily create images and art from text to image, or image to image. Additional models allow you to change the output's style. MJ v4, Any v3, Analog and Redshift, and many more. Other stable diffusion versions such as 2.0+ will also include. You can train your own stable diffusion model (fine tuning) and then deploy on Evoke via an API. In the future, we will have models such as Whisper, Yolo and GPT-J. We also plan to offer training and deployment on many other models.

Connect AI models to create AI apps in minutes with no code. Your efforts will create the next generation of AI-powered applications. In minutes, you can create AI-powered apps. It's easier than ever to create AI-powered applications. Make money and share your creations around the world. Profit from your unique creations' earning potential. Brancher.ai has over 100 templates that can help you increase your creativity and productivity, from a spark of inspiration to an instant start to a new app.

TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.

AI-FLOW, an innovative open-source software platform, simplifies the way creators and innovators harness artificial intelligence. AI-FLOW's drag-and-drop user interface allows you to easily connect and combine AI models to create custom AI tools tailored to meet your needs. Key Features: Diverse AI Model Integration: Access a range of top-tier AI Models, including GPT-4 and DALL-E 3. 2. Drag-and-Drop: Create complex AI workflows without coding thanks to our intuitive design. 3. Custom AI Tool Creation: Create AI solutions that are tailored to your needs, from image generation through to language processing. 4. Local Data Storage: Take full control of your data by storing it locally and exporting it as JSON files.

Create generative AI apps for engineers and not just ML practitioners. Focus on creating instead of configuring. No more switching platforms or wrestling with APIs. Use a familiar interface for AI and to prompt engineers. Workbooks can be easily streamlined into templates by using parameters. Create workflows using model outputs from LLMs and image and audio models. Create groups to manage workbooks between your teammates. Share your workbook with your team or the public, or to specific organizations that you define. Workbooks can be commented on and compared with your team. Create templates for you, your team or the developer community. Get started quickly by using templates to see what others are building.

Diaflow is a platform that allows you to scale AI across your entire organization. It enables everyone to deploy AI workflows which drive innovation. Create powerful apps and workflows using any data source to automate manual processes or create fully automated ones. Automate manual business processes in a way that your team will enjoy. Diaflow's intuitive components and interfaces allow you to create powerful AI-driven apps for your business that you can be proud of. Diaflow AI-powered editor offers a new way to create and edit documents. Your expertise can be leveraged to provide 24/7 support. With a built-in AI spreadsheet solution, you can easily manage and transform your data. Diaflow makes it easy to create amazing products for your business. Diaflow allows you to create apps and workflows without coding in just minutes.

All the steps involved in building machine learning algorithms and predicting results, all in one click. Data Dialog allows you to easily shape your data without having to wrangle your files. Your prediction reports can be shared with your team members or made public. Let anyone make predictions on your model. Our low-code API allows you to integrate dynamic ML predictions directly into your app. Real-time prediction of willingness to pay, score leads, and many other things. AI gives you access to the most advanced algorithms in the world, without compromising on performance. Forecast revenue, optimize supply chain, personalize your marketing. Now you can see what the next steps are. In minutes, you can add a CSV file or integrate with your favorite data sources. Select your prediction column from the dropdown and we'll automatically build the AI. Visualize the top drivers, predicted results, and simulate "what-if?" scenarios.

Our platform is the only one that offers an end-to–end platform for AI-powered innovation. It can handle everything from data cleansing and structuring to creating and deploying advanced data-science models to infusing advanced analytics algorithms, to infusing Recommendation AI, to deducing outcomes with simple-to-deduce visualization dashboards as well as Explainable AI to track how the outcomes were calculated. Our platform provides a seamless, holistic data science experience. FutureAnalytica offers key features such as a robust Data Lakehouse and an AI Studio. There is also a comprehensive AI Marketplace. You can also get support from a world-class team of data-science experts (on a case-by-case basis). FutureAnalytica will help you save time, effort, and money on your data-science and AI journey. Start discussions with the leadership and then a quick technology assessment within 1-3 days. In 10-18 days, you can create ready-to-integrate AI solutions with FA's fully-automated data science & AI platform.

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.

Platform for AI agents testing and debugging by the industry's leading developers. We developed the tools, so you don't need to. Visually track events, such as LLM, tools, and agent interactions. Rewind and playback agent runs with pinpoint precision. Keep a complete data trail from prototype to production of logs, errors and prompt injection attacks. Native integrations with top agent frameworks. Track, save and monitor each token that your agent sees. Monitor and manage agent spending using the most recent price monitoring. Save up to 25x on specialized LLMs by fine-tuning them based on completed completions. Build your next agent using evals and replays. You can visualize the behavior of your agents in your AgentOps dashboard with just two lines of coding. After you set up AgentOps each execution of your program will be recorded as a "session" and the data will automatically be recorded for you.

With our intuitive interface, you can create an AI-powered application in minutes. Connect GPT-3 with the internet using a Web Search Block, pull data in with an HTTP Request Block, or chain multiple Large Language Model blocks. Launch your app with a UI or bring the power to language into your community by deploying your app as a discord bot.

With just one line of code, you can deploy all major ML frameworks like Pytorch and Onnx. Do you not have your own models? Prebuilt models can be deployed to reduce latency and cost. You can fine-tune models for specific tasks to reduce latency and costs while increasing performance. It's easy to do and you don't have to worry about infrastructure. Integrate with the top ML observability platform to be alerted on feature or prediction drift, compare models versions, and resolve issues quickly. To resolve model performance problems, discover the root causes of prediction and feature drift. Find out which features contribute the most to your model's performance.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

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.

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.

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.

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.