Alternatives to Orkes

Camunda enables organizations to orchestrate processes across people, systems, and devices to continuously overcome complexity and increase efficiency

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. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

Unqork was founded in 2017 and is the industry's pioneer enterprise no-code platform. It helps large companies create, deploy, manage, and maintain complex applications without writing any code. Companies such as Liberty Mutual, Goldman Sachs and John Hancock use Unqork's drag and drop interface to create enterprise applications faster, with better quality and at lower costs than traditional approaches.

Bonitasoft fully supports digital operations with Bonita, an extensible open-source platform for automation of business processes and IT modernization. The Bonita platform speeds development and production. It clearly separates capabilities for visual programming from those for coding. Bonita integrates into existing information systems, orchestrates heterogeneous system, and provides deep visibility to all processes within the organization.

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.

Now is the time to build. You'll be ready for anything that comes next. Pega makes it easy to collaborate no matter where you are working with its intuitive and inclusive approach to app authoring. Unify users on one platform with low-code tools and developer-grade options to quickly respond to changing needs. From one dashboard, you can foster innovation and manage low-code across your entire organization. Optimize efficiency by providing business users, developers, IT, and IT with the information they need, whenever and wherever they need it. You can accelerate application development by quickly and easily defining core elements. From one dashboard, you can manage low-code across your entire organization. IT can be empowered to ensure that every app is built within the organizational guardrails. App experiences that are relevant today and ready for future growth. Pega's UX framework was designed for developers, employees, customers, and partners.

AWS Step Functions, a serverless function orchestrator, makes it easy to sequence AWS Lambda and multiple AWS services into business critical applications. It allows you to create and manage a series event-driven and checkpointed workflows that maintain the application's state. The output of each step acts as an input for the next. Your business logic dictates that each step of your application runs in the right order. It can be difficult to manage a series serverless applications, manage retries, or debugging errors. The complexity of managing distributed applications increases as they become more complex. Step Functions, which has built-in operational controls manages state, sequencing, error handling and retry logic. This removes a significant operational burden from your staff. AWS Step Functions allows you to create visual workflows that allow for fast translation of business requirements into technical specifications.

Appian, the digital platform for digital transformation, allows teams to create powerful applications 10x faster. Appian combines industry-leading process management with low-code development speed to help organizations stay on the right track in their digital transformation journey. Appian offers a low-code development platform with drag-and drop, declarative, and visual development, consistent user experiences across all devices, integrations and instant deployment.

Enhance your stack Open-source tool for developers to quickly integrate and orchestrate processes. The integration layer simplifies microservices and applications to work together. It provides seamless communication and efficient control of data flow between services. You can easily create custom integrations tailored to meet your needs with a wide variety of pre-built components and a simple way to build them yourself. Easy creation of asynchronous process between all integrated services. Orchesty offers robust tools for modeling and managing running processes, as well as scheduling, controlling, and monitoring them.

Cloud-native applications can be used to automate business processes and decisions. Red Hat®, Process Automation Manager is a platform that allows you to develop containerized microservices, and applications that automate business processes and decisions. Process Automation Manager includes business rule management (BPM), business rule management (BRM), business resource optimization (CEP) and complex event processing technologies. It also features a user interface platform that allows you to create engaging user interfaces for decision services and process management with minimal coding. Everything business users need in order to model flows and policies: Business Process Model and Notation models, Decision Model and Notation models (DMN), and domain-specific rules languages. Built in the cloud, for cloud. Red Hat OpenShift allows you to deploy completed models as microservices in containers. Drools is a powerful open-source rules engine that is widely used.

SAP Process Orchestration (SAP PO formerly SAP PI software) supports custom process applications as well as integration scenarios. It is the process orchestration layer within SAP's Business Technology Platform. It can help you improve process efficiency and respond to changing requirements. You can quickly and easily model, implement, integrate, monitor, and manage custom process applications and integration scenarios. You can innovate faster and be more responsive to changing requirements by creating more efficient, flexible processes. By creating custom process applications, you can increase the speed and flexibility in your business operations. Reduce development time and costs by integrating processes, rules and integration management into one integrated solution. Automated rules can help you improve the enforcement of your corporate policies and legal regulations.

RPA is the future of modern enterprise. Are you interested in RPA to automate manual tasks? Include your people. Automating end-to-end processes that include both your data and people is essential to improve business efficiency. Tonkean's aRPA platform combines no code RPA, integrations and AI-powered coordinationbots into a single platform. This allows you to automate, orchestrate, and coordinate end-to-end processes across multiple systems and people. Our powerful Workflow Builder makes it easy to train your Bots in order to coordinate and execute any business workflow. This includes data manipulation and people coordination. Tonkean puts your employees at the center of the process by reaching them wherever they are: Slack or MS Teams, email, or even via email. Tonkean InvoicesGPT fully automates the handling of all incoming invoices. Simply connect your email inbox or Google Drive in one click, and Tonkean will immediately analyze any PDF/invoice files to extract relevant fields, complete a three-way-matching verification, provide visibility into spend across vendors and departments, and update existing finance systems.

Enate allows you to run workflows from start to finish under one roof. You can seamlessly orchestrate each step of your workflow and leave behind the tangled webs of manual work. It has been difficult to deploy AI and RPA technology. Enate lets you instantly plug-and-play automation into your workflows to improve efficiency. Real-time metrics and data allow you to spot operational gaps and manage and assign work according to skills and competencies. From operational chaos to excellence. Create unlimited and customizable workflows in different geographies using features like sentiment analysis and peer reviews to help you thrive. Enate lets you create drag-and-drop-based workflows without expensive development work. Our platform is so simple to use that anyone can be trained.

Outstanding customer service and operational excellence can help you grow your business and attract new customers. Leading organizations worldwide are turning to Flowable's Intelligent Business Automation solutions to transform their business processes in today's competitive market. Delivering exceptional customer service is key to increasing customer retention and acquisition. Operational Excellence is achieved by improving business efficiency and reducing costs. Increasing Business Agility to adapt and respond to changing market conditions. To ensure business continuity, enforce Business Compliance. Flowable's conversational engagement capabilities allow you to deliver a compelling combination of automated and personal service via popular chat platforms like WhatsApp - even in highly-regulated sectors. Flowable is lightning fast and has many years of experience in real-world applications. It supports decision, case, and process modeling and can handle complex case management situations.

Maxim is a enterprise-grade stack that enables AI teams to build applications with speed, reliability, and quality. Bring the best practices from traditional software development to your non-deterministic AI work flows. Playground for your rapid engineering needs. Iterate quickly and systematically with your team. Organise and version prompts away from the codebase. Test, iterate and deploy prompts with no code changes. Connect to your data, RAG Pipelines, and prompt tools. Chain prompts, other components and workflows together to create and test workflows. Unified framework for machine- and human-evaluation. Quantify improvements and regressions to deploy with confidence. Visualize the evaluation of large test suites and multiple versions. Simplify and scale human assessment pipelines. Integrate seamlessly into your CI/CD workflows. Monitor AI system usage in real-time and optimize it with speed.

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.

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

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.

Get a complete picture of the performance of your model. Log inputs and out-puts, and enrich them with metadata. Find out what your model is doing and where it can be improved. Monitor for errors, and identify underperforming cohorts or use cases. The best models are based on user data. To retrain your model, you can programmatically gather examples that are unusual or underperforming. When changing your model or prompt, stop manually reviewing thousands outputs. Apps powered by LLM can be evaluated programmatically. Detect and fix degradations fast. Monitor new deployments and edit your app in real-time. Connect your data sources to your self-hosted model or third-party model. Our serverless streaming dataflow engines can handle large amounts of data. Gantry is SOC-2-compliant and built using enterprise-grade authentication.

Stagehand is a web browsing framework powered by AI that allows developers to automate browsers with natural language instructions. Browserbase's Stagehand introduces three intuitive APIs: act, extract, observe, on Playwright base page class. This enables web automation with simple commands. Developers can, for example, navigate to a page, identify elements such as search bars, extract data, such as product prices, or perform actions, like adding items to the cart, using natural language directives. This approach simplifies the creation and maintenance of repeatable, durable, and self-healing web automation workflows. It reduces the fragility and complexity often associated with traditional methods. Stagehand is compatible with existing Playwright codes, allowing seamless integration into existing projects. By leveraging AI it offers a more efficient and intuitive way to handle browser automating tasks.

Entry Point AI is a modern AI optimization platform that optimizes proprietary and open-source language models. Manage prompts and fine-tunes in one place. We make it easy to fine-tune models when you reach the limits. Fine-tuning involves showing a model what to do, not telling it. It works in conjunction with prompt engineering and retrieval augmented generation (RAG) in order to maximize the potential of AI models. Fine-tuning your prompts can help you improve their quality. Imagine it as an upgrade to a few-shot model that incorporates the examples. You can train a model to perform at the same level as a high-quality model for simpler tasks. This will reduce latency and costs. For safety, to protect the brand, or to get the formatting correct, train your model to not respond in a certain way to users. Add examples to your dataset to cover edge cases and guide model behavior.

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.

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.

Lyzr Agent Studio provides a low-code/no code platform that allows enterprises to build, deploy and scale AI agents without requiring a lot of technical expertise. This platform is built on Lyzr’s robust Agent Framework, the first and only agent Framework to have safe and reliable AI natively integrated in the core agent architecture. The platform allows non-technical and technical users to create AI powered solutions that drive automation and improve operational efficiency while enhancing customer experiences without the need for extensive programming expertise. Lyzr Agent Studio allows you to build complex, industry-specific apps for sectors such as BFSI or deploy AI agents for Sales and Marketing, HR or Finance.

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.

MakerSuite simplifies this process. MakerSuite allows you to easily tune custom models, iterate on prompts and augment your data with synthetic data. MakerSuite allows you to export your prompts as code in your favorite languages, such as Python and Node.js, when you are ready to move on to code.

You can easily create a pipeline on our platform by layering prebuilt functionality that matches your desired behavior. If you need a custom model that doesn't fit into our prebuilts, you can either contact us to have it added for you or create your own using our custom model creation. The ezML libraries are available in a wide range of frameworks and languages. They support the most common cases, as well as realtime streaming using TCP, WebRTC, or RTMP. Deployments automatically scale to meet the demand of your product, ensuring uninterrupted operation no matter how large your user base becomes.

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.

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.

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.

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.

Intelligently activate processes to accelerate the flow. Alfresco's platform provides comprehensive cloud-native services for content. Check out some of its key features to see why it is such a powerful tool for any organization. Alfresco allows you to quickly access and find the information you need from anywhere using web-based tools. The tightly integrated capabilities of process and content services streamline content-centric processes, enabling faster and more informed decision-making. Teams can extend the benefits of Microsoft 365 to Google Docs and boost productivity with enterprise collaboration tools. Alfresco Governance Services automates information lifecycles with minimal user intervention, reducing risk and strengthening compliance.

Vercel combines the best in developer experience with a laser-focused focus on end-user performance. Our platform allows frontend teams to do their best work. Next.js is a React framework Vercel created with Google and Facebook. It's loved by developers. Next.js powers some of the most popular websites, including Twilio and Washington Post. It is used for news, e-commerce and travel. Vercel is the best place for any frontend app to be deployed. Start by connecting to our global edge network with zero configuration. Scale dynamically to millions upon millions of pages without breaking a sweat. Live editing for your UI components. Connect your pages to any data source or headless CMS and make them work in every dev environment. All of our cloud primitives, from caching to Serverless functions, work perfectly on localhost.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

Composio is a platform for integration that enhances AI agents and Large Language Models by providing seamless connections with over 150 tools. It supports a variety of agentic frameworks, LLM providers and function calling for efficient task completion. Composio provides a wide range of tools including GitHub and Salesforce, file management and code execution environments. This allows AI agents to perform a variety of actions and subscribe to different triggers. The platform offers managed authentication that allows users to manage authentication processes for users and agents through a central dashboard. Composio's core features include a developer first integration approach, built in authentication management, and an expanding catalog with over 90 ready to connect tools. It also includes a 30% reliability increase through simplified JSON structure and improved error handling.

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.

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.

LlamaIndex, a "dataframework", is designed to help you create LLM apps. Connect semi-structured API data like Slack or Salesforce. LlamaIndex provides a flexible and simple data framework to connect custom data sources with large language models. LlamaIndex is a powerful tool to enhance your LLM applications. Connect your existing data formats and sources (APIs, PDFs, documents, SQL etc.). Use with a large-scale language model application. Store and index data for different uses. Integrate downstream vector stores and database providers. LlamaIndex is a query interface which accepts any input prompts over your data, and returns a knowledge augmented response. Connect unstructured data sources, such as PDFs, raw text files and images. Integrate structured data sources such as Excel, SQL etc. It provides ways to structure data (indices, charts) so that it can be used with LLMs.

Our platform allows you to create AI products, train, fine-tune, and deploy models on the cloud. You don't have to worry about scaling, infrastructure, cost management, or other technical issues. Prebuilt, fully customizable modular components make it easy to train, fine tune, and deploy models. The science, not the engineering, should be your focus. Lightning components organize code to run on the cloud and manage its own infrastructure, cloud cost, and other details. 50+ optimizations to lower cloud cost and deliver AI in weeks, not months. Enterprise-grade control combined with consumer-level simplicity allows you to optimize performance, reduce costs, and take on less risk. Get more than a demo. In days, not months, you can launch your next GPT startup, diffusion startup or cloud SaaSML service.

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.