Business Software for Openlayer - Page 2

The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Configurable layouts and templates adapt to your dataset and workflow. Detect objects on images, boxes, polygons, circular, and key points supported. Partition the image into multiple segments. Use ML models to pre-label and optimize the process. Webhooks, Python SDK, and API allow you to authenticate, create projects, import tasks, manage model predictions, and more. Save time by using predictions to assist your labeling process with ML backend integration. Connect to cloud object storage and label data there directly with S3 and GCP. Prepare and manage your dataset in our Data Manager using advanced filters. Support multiple projects, use cases, and data types in one platform. Start typing in the config, and you can quickly preview the labeling interface. At the bottom of the page, you have live serialization updates of what Label Studio expects as an input.

Watson's skills and integrations streamline your team's efforts, making you more effective. Streamline your workflow by letting Watson automate tasks and simplify complex processes, saving you time and effort. Then you can focus on more high-level work. With Watson you can facilitate communication between team members, ensuring that everyone is on the same page and that tasks are completed on time and to the desired quality. Skill up Watson with the capabilities that make the most impact on your business. Import existing and new automation into the platform and assign it to Watson to get work done. Watson uses natural language processing to draw from a catalog of basic and advanced skills to execute your requests, in context and in the right order. No specialized training is needed. Get started in minutes using prebuilt skills designed for you and your needs. Watson connects to your various apps and tools to work seamlessly across Salesforce, Workday, Outlook, Gmail and other tools.

Launch production-ready apps with the LMOps stack for monitoring, model management, and more. Replace your OpenAI or other provider APIs with the Portkey endpoint. Manage prompts, engines, parameters, and versions in Portkey. Switch, test, and upgrade models with confidence! View your app performance & user level aggregate metics to optimise usage and API costs Keep your user data secure from attacks and inadvertent exposure. Get proactive alerts when things go bad. A/B test your models in the real world and deploy the best performers. We built apps on top of LLM APIs for the past 2 and a half years and realised that while building a PoC took a weekend, taking it to production & managing it was a pain! We're building Portkey to help you succeed in deploying large language models APIs in your applications. Regardless of you trying Portkey, we're always happy to help!

Great Expectations is a shared, open standard for data quality. It helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. We recommend deploying within a virtual environment. If you’re not familiar with pip, virtual environments, notebooks, or git, you may want to check out the Supporting. There are many amazing companies using great expectations these days. Check out some of our case studies with companies that we've worked closely with to understand how they are using great expectations in their data stack. Great expectations cloud is a fully managed SaaS offering. We're taking on new private alpha members for great expectations cloud, a fully managed SaaS offering. Alpha members get first access to new features and input to the roadmap.

Amazon Bedrock is a fully managed service that simplifies building and scaling generative AI applications by providing access to a variety of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself. Through a single API, developers can experiment with these models, customize them using techniques like fine-tuning and Retrieval Augmented Generation (RAG), and create agents that interact with enterprise systems and data sources. As a serverless platform, Amazon Bedrock eliminates the need for infrastructure management, allowing seamless integration of generative AI capabilities into applications with a focus on security, privacy, and responsible AI practices.

Deploy LLMs and other AI — commercial, homegrown or open-source — on your private network, based on an AI-optimized data foundation. AI Core is a real-time, full-fidelity representation of your business that includes all actions, decisions, and processes. Utilize the Action Graph, atop the AI Core, to set specific scopes of activity for LLMs and other models – including hand-off procedures for auditable calculations and human-in-the-loop operations. Monitor and control LLM activity and reach in real-time to help users promote compliance with legal, data sensitivity, and regulatory audit requirements.

GroqCloud is a high-performance AI inference platform built specifically for developers who need speed, scale, and predictable costs. It delivers ultra-fast responses for leading generative AI models across text, audio, and vision workloads. Powered by Groq’s purpose-built LPU (Language Processing Unit), the platform is designed for inference from the ground up, not adapted from training hardware. GroqCloud supports popular LLMs, speech-to-text, text-to-speech, and image-to-text models through industry-standard APIs. Developers can start for free and scale seamlessly as usage grows, with clear usage-based pricing. The platform is available in public, private, or co-cloud deployments to match different security and performance needs. GroqCloud combines consistent low latency with enterprise-grade reliability.

Azure AI Content Understanding helps enterprises transform unstructured multimodal data into insights. Derive meaningful insights from diverse types of input data, ranging from text, audio, images, and video. Achieve precise, high-quality data for downstream applications with sophisticated AI methods such as scheme extraction and grounding. Streamline and unify pipelines of varied data types into a single streamlined workflow, reducing overall costs and accelerating time to value. See how businesses and call center operators generate valuable insights from call recordings to track essential KPIs, enhance product experiences, and respond to customer inquiries more swiftly and accurately. Ingest a range of modalities, such as documents, images, audio, or video, and use a range of AI models available in Azure AI to transform input data into structured output that can be easily processed and analyzed by downstream applications.

Langflow is a low-code AI builder designed to create agentic and retrieval-augmented generation applications. It offers a visual interface that allows developers to construct complex AI workflows through drag-and-drop components, facilitating rapid experimentation and prototyping. The platform is Python-based and agnostic to any model, API, or database, enabling seamless integration with various tools and stacks. Langflow supports the development of intelligent chatbots, document analysis systems, and multi-agent applications. It provides features such as dynamic input variables, fine-tuning capabilities, and the ability to create custom components. Additionally, Langflow integrates with numerous services, including Cohere, Bing, Anthropic, HuggingFace, OpenAI, and Pinecone, among others. Developers can utilize pre-built components or code their own, enhancing flexibility in AI application development. The platform also offers a free cloud service for quick deployment and test

Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It supports various LLM runners like Ollama and OpenAI-compatible APIs, with a built-in inference engine for Retrieval Augmented Generation (RAG), making it a powerful AI deployment solution. Key features include effortless setup via Docker or Kubernetes, seamless integration with OpenAI-compatible APIs, granular permissions and user groups for enhanced security, responsive design across devices, and full Markdown and LaTeX support for enriched interactions. Additionally, Open WebUI offers a Progressive Web App (PWA) for mobile devices, providing offline access and a native app-like experience. The platform also includes a Model Builder, allowing users to create custom models from base Ollama models directly within the interface. With over 156,000 users, Open WebUI is a versatile solution for deploying and managing AI models in a secure, offline environment.

Unity AI Gateway provides centralized governance, observability, and spend controls across enterprise AI systems, helping organizations manage agents, tools, models, MCPs, and AI frameworks from a single governed layer. It applies consistent governance across Databricks-hosted AI, external models, coding agents, agent harnesses, and other AI services without locking teams into a single provider or stack. Identity-aware policies control what agents can access, which actions they can take, and which tools they can use, while built-in, custom, and third-party guardrails enforce safety and compliance across prompts, responses, and interactions. It captures prompts, traces, tool calls, payload logs, audit logs, token usage, and policy decisions to monitor behavior, investigate incidents, and support compliance. Centralized cost controls track consumption across users, teams, applications, agents, and providers, with budgets, rate limits, and hard spend caps.

High-quality, ubiquitous, and portable telemetry to enable effective observability. OpenTelemetry is a collection of tools, APIs, and SDKs. Use it to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) to help you analyze your software’s performance and behavior. OpenTelemetry is generally available across several languages and is suitable for use. Create and collect telemetry data from your services and software, then forward them to a variety of analysis tools. OpenTelemetry integrates with popular libraries and frameworks such as Spring, ASP.NET Core, Express, Quarkus, and more! Installation and integration can be as simple as a few lines of code. 100% Free and Open Source, OpenTelemetry is adopted and supported by industry leaders in the observability space.