Best AI tools for< Monitor Pipelines >

Hopsworks is an AI platform that offers a comprehensive solution for building, deploying, and monitoring machine learning systems. It provides features such as a Feature Store, real-time ML capabilities, and generative AI solutions. Hopsworks enables users to develop and deploy reliable AI systems, orchestrate and monitor models, and personalize machine learning models with private data. The platform supports batch and real-time ML tasks, with the flexibility to deploy on-premises or in the cloud.

ZeroThreat is a web app and API security scanner that helps businesses identify and fix vulnerabilities in their web applications and APIs. It uses a combination of static and dynamic analysis techniques to scan for a wide range of vulnerabilities, including OWASP Top 10, CWE Top 25, and SANS Top 25. ZeroThreat also provides continuous monitoring and alerting, so businesses can stay on top of new vulnerabilities as they emerge.

Athina AI is a comprehensive platform designed to monitor, debug, analyze, and improve the performance of Large Language Models (LLMs) in production environments. It provides a suite of tools and features that enable users to detect and fix hallucinations, evaluate output quality, analyze usage patterns, and optimize prompt management. Athina AI supports integration with various LLMs and offers a range of evaluation metrics, including context relevancy, harmfulness, summarization accuracy, and custom evaluations. It also provides a self-hosted solution for complete privacy and control, a GraphQL API for programmatic access to logs and evaluations, and support for multiple users and teams. Athina AI's mission is to empower organizations to harness the full potential of LLMs by ensuring their reliability, accuracy, and alignment with business objectives.

Haystack is a production-ready open-source AI framework designed to facilitate building AI applications. It offers a flexible components and pipelines architecture, allowing users to customize and build applications according to their specific requirements. With partnerships with leading LLM providers and AI tools, Haystack provides freedom of choice for users. The framework is built for production, with fully serializable pipelines, logging, monitoring integrations, and deployment guides for full-scale deployments on various platforms. Users can build Haystack apps faster using deepset Studio, a platform for drag-and-drop construction of pipelines, testing, debugging, and sharing prototypes.

Baseten is a machine learning infrastructure that provides a unified platform for data scientists and engineers to build, train, and deploy machine learning models. It offers a range of features to simplify the ML lifecycle, including data preparation, model training, and deployment. Baseten also provides a marketplace of pre-built models and components that can be used to accelerate the development of ML applications.

DevSecCops is an AI-driven automation platform designed to revolutionize DevSecOps processes. The platform offers solutions for cloud optimization, machine learning operations, data engineering, application modernization, infrastructure monitoring, security, compliance, and more. With features like one-click infrastructure security scan, AI engine security fixes, compliance readiness using AI engine, and observability, DevSecCops aims to enhance developer productivity, reduce cloud costs, and ensure secure and compliant infrastructure management. The platform leverages AI technology to identify and resolve security issues swiftly, optimize AI workflows, and provide cost-saving techniques for cloud architecture.

Momentic is an AI testing tool that offers automated AI testing for software applications. It streamlines regression testing, production monitoring, and UI automation, making test automation easy with its AI capabilities. Momentic is designed to be simple to set up, easy to maintain, and accelerates team productivity by creating and deploying tests faster with its intuitive low-code editor. The tool adapts to applications, saves time with automated test maintenance, and allows testing anywhere, anytime using cloud, local, or CI/CD pipelines.

StreamDeploy is an AI-powered cloud deployment platform designed to streamline and secure application deployment for agile teams. It offers a range of features to help developers maximize productivity and minimize costs, including a Dockerfile generator, automated security checks, and support for continuous integration and delivery (CI/CD) pipelines. StreamDeploy is currently in closed beta, but interested users can book a demo or follow the company on Twitter for updates.

Reach is a sales engagement platform that helps businesses generate more leads and close more deals. It uses artificial intelligence to monitor leads across multiple data sources for relevant triggers, such as job changes, company news, and social media activity. Reach then provides sales reps with daily notifications of these triggers, along with personalized icebreaker suggestions and AI-generated copy. This enables sales reps to reach out to leads at the right time with the right message, increasing their chances of success.

VIDOC is an AI-powered security engineer that automates code review and penetration testing. It continuously scans and reviews code to detect and fix security issues, helping developers deliver secure software faster. VIDOC is easy to use, requiring only two lines of code to be added to a GitHub Actions workflow. It then takes care of the rest, providing developers with a tailored code solution to fix any issues found.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

Browse AI is an AI-powered data extraction and monitoring platform that allows users to scrape and monitor data from any website without the need for coding. It offers a full suite of features for stress-free data extraction, including turning websites into APIs, monitoring for changes, and creating prebuilt robots for various use cases. With over 7,000 integrations, Browse AI ensures reliable and scalable data extraction with no coding required. The platform is trusted by over 558,000 users worldwide and is designed to simplify the process of turning any website into a reliable data pipeline.

Factors.AI is a B2B account intelligence, analytics, and attribution tool designed to help businesses grow their pipeline by identifying and engaging with high-intent accounts. The platform offers features such as revenue attribution, customer journey tracking, unified reporting, and integrations with sales and marketing tools. Factors.AI provides AI-led insights to refine marketing strategies, optimize sales performance, and maximize ROI. With a focus on intent-driven outreach and account identification, Factors.AI empowers users to make data-driven decisions and drive revenue growth.

Amazon Bedrock is a cloud-based platform that enables developers to build, deploy, and manage serverless applications. It provides a fully managed environment that takes care of the infrastructure and operations, so developers can focus on writing code. Bedrock also offers a variety of tools and services to help developers build and deploy their applications, including a code editor, a debugger, and a deployment pipeline.

Letterdrop is a platform designed to help sales and marketing teams find warm leads efficiently and effectively. It enables users to attract and engage with potential buyers through automated thought leadership content sourced from sales calls. With a focus on nurturing warm leads and building trust with buyers, Letterdrop aims to streamline the lead generation process and improve conversion rates for sales teams. The platform offers proven playbooks, easy adoption, and results-driven strategies to help users achieve their sales targets and boost pipeline growth.

Fetcher is an AI-powered recruitment platform that simplifies the hiring process by providing curated batches of talented candidates tailored to specific needs. It eliminates manual sourcing by leveraging advanced AI technology and human expertise to deliver high-quality candidate profiles directly to the user's inbox. With features like personalized email outreach, recruitment analytics, and diversity metrics monitoring, Fetcher optimizes the recruitment pipeline and enhances candidate engagement. Trusted by top recruiting teams worldwide, Fetcher offers unlimited candidate connections and email performance analytics to streamline the hiring strategy.

Pipeless Agents is a platform that allows users to convert any video feed into an actionable data stream, enabling automation of tasks based on visual inputs. It serves as a serverless platform for Vision AI, offering the ability to create projects, connect video sources, and customize agents for specific needs. With a focus on simplicity and efficiency, Pipeless Agents empowers users to extract structured data from various video sources and automate processes with minimal coding requirements.

Boomerang is an AI-powered sales pipeline tool that helps businesses turn happy customers into revenue opportunities by tracking job changes, automating follow-ups, and generating warm intros. It offers features such as contact tracking, account tracking, warm intros, auto org charts, and CRM data quality. Boomerang outperforms competitors in pipeline conversion and job change tracking speed. The tool is designed to boost revenue growth, increase repeat buyers, and enhance customer engagement through personalized interactions.

LLMMM is an AI tool designed to monitor how AI models perceive and present brands. It offers real-time monitoring and cross-model insights to help brands understand their digital presence across various leading AI platforms. With automated analysis and lightning-fast results, LLMMM provides immediate visibility into how AI chatbots interpret brands. The tool focuses on brand intelligence, brand safety monitoring, misalignment detection, and cross-model brand intelligence. Users can create an account in minutes and access a range of features to track and analyze their brand's performance in the AI landscape.

New Relic is an AI monitoring platform that offers an all-in-one observability solution for monitoring, debugging, and improving the entire technology stack. With over 30 capabilities and 750+ integrations, New Relic provides the power of AI to help users gain insights and optimize performance across various aspects of their infrastructure, applications, and digital experiences.

Sematic is an open-source ML development platform that allows ML Engineers and Data Scientists to write complex end-to-end pipelines with Python. It can be executed locally, on a cloud VM, or on a Kubernetes cluster. Sematic enables chaining data processing jobs with model training into reproducible pipelines that can be monitored and visualized in a web dashboard. It offers features like easy onboarding, local-to-cloud parity, end-to-end traceability, access to heterogeneous compute resources, and reproducibility.

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

The 'ai-dev-2024-ml-workshop' repository contains materials for the Deploy and Monitor ML Pipelines workshop at the AI_dev 2024 conference in Paris, focusing on deployment designs of machine learning pipelines using open-source applications and free-tier tools. It demonstrates automating data refresh and forecasting using GitHub Actions and Docker, monitoring with MLflow and YData Profiling, and setting up a monitoring dashboard with Quarto doc on GitHub Pages.

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

Evidently is an open-source Python library designed for evaluating, testing, and monitoring machine learning (ML) and large language model (LLM) powered systems. It offers a wide range of functionalities, including working with tabular, text data, and embeddings, supporting predictive and generative systems, providing over 100 built-in metrics for data drift detection and LLM evaluation, allowing for custom metrics and tests, enabling both offline evaluations and live monitoring, and offering an open architecture for easy data export and integration with existing tools. Users can utilize Evidently for one-off evaluations using Reports or Test Suites in Python, or opt for real-time monitoring through the Dashboard service.

Tonic Validate is a framework for the evaluation of LLM outputs, such as Retrieval Augmented Generation (RAG) pipelines. Validate makes it easy to evaluate, track, and monitor your LLM and RAG applications. Validate allows you to evaluate your LLM outputs through the use of our provided metrics which measure everything from answer correctness to LLM hallucination. Additionally, Validate has an optional UI to visualize your evaluation results for easy tracking and monitoring.

Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

sfdx-hardis is a toolbox for Salesforce DX, developed by Cloudity, that simplifies tasks which would otherwise take minutes or hours to complete manually. It enables users to define complete CI/CD pipelines for Salesforce projects, backup metadata, and monitor any Salesforce org. The tool offers a wide range of commands that can be accessed via the command line interface or through a Visual Studio Code extension. Additionally, sfdx-hardis provides Docker images for easy integration into CI workflows. The tool is designed to be natively compliant with various platforms and tools, making it a versatile solution for Salesforce developers.

Backend.AI Web UI is a user-friendly web and app interface designed to make AI accessible for end-users, DevOps, and SysAdmins. It provides features for session management, inference service management, pipeline management, storage management, node management, statistics, configurations, license checking, plugins, help & manuals, kernel management, user management, keypair management, manager settings, proxy mode support, service information, and integration with the Backend.AI Web Server. The tool supports various devices, offers a built-in websocket proxy feature, and allows for versatile usage across different platforms. Users can easily manage resources, run environment-supported apps, access a web-based terminal, use Visual Studio Code editor, manage experiments, set up autoscaling, manage pipelines, handle storage, monitor nodes, view statistics, configure settings, and more.

Instill Core is an open-source orchestrator comprising a collection of source-available projects designed to streamline every aspect of building versatile AI features with unstructured data. It includes Instill VDP (Versatile Data Pipeline) for unstructured data, AI, and pipeline orchestration, Instill Model for scalable MLOps and LLMOps for open-source or custom AI models, and Instill Artifact for unified unstructured data management. Instill Core can be used for tasks such as building, testing, and sharing pipelines, importing, serving, fine-tuning, and monitoring ML models, and transforming documents, images, audio, and video into a unified AI-ready format.

ZenML is an extensible, open-source MLOps framework for creating portable, production-ready machine learning pipelines. By decoupling infrastructure from code, ZenML enables developers across your organization to collaborate more effectively as they develop to production.

Open-Source Evaluation for LLM Applications. `continuous-eval` is an open-source package created for granular and holistic evaluation of GenAI application pipelines. It offers modularized evaluation, a comprehensive metric library covering various LLM use cases, the ability to leverage user feedback in evaluation, and synthetic dataset generation for testing pipelines. Users can define their own metrics by extending the Metric class. The tool allows running evaluation on a pipeline defined with modules and corresponding metrics. Additionally, it provides synthetic data generation capabilities to create user interaction data for evaluation or training purposes.

Flyte is an open-source orchestrator that facilitates building production-grade data and ML pipelines. It is built for scalability and reproducibility, leveraging Kubernetes as its underlying platform. With Flyte, user teams can construct pipelines using the Python SDK, and seamlessly deploy them on both cloud and on-premises environments, enabling distributed processing and efficient resource utilization.

Azure OpenAI Service is a central capability with Azure API Management, providing guidance and tools for organizations to implement Azure OpenAI in a production environment with an emphasis on cost control, secure access, and usage monitoring. It includes infrastructure-as-code templates, CI/CD pipelines, secure access management, usage monitoring, load balancing, streaming requests, and end-to-end samples like ChatApp and Azure Dashboards.

Hailo TAPPAS is a set of full application examples that implement pipeline elements and pre-trained AI tasks. It demonstrates Hailo's system integration scenarios on predefined systems, aiming to accelerate time to market, simplify integration with Hailo's runtime SW stack, and provide a starting point for customers to fine-tune their applications. The tool supports both Hailo-15 and Hailo-8, offering various example applications optimized for different common hosts. TAPPAS includes pipelines for single network, two network, and multi-stream processing, as well as high-resolution processing via tiling. It also provides example use case pipelines like License Plate Recognition and Multi-Person Multi-Camera Tracking. The tool is regularly updated with new features, bug fixes, and platform support.

Langfuse is a powerful tool that helps you develop, monitor, and test your LLM applications. With Langfuse, you can: * **Develop:** Instrument your app and start ingesting traces to Langfuse, inspect and debug complex logs, and manage, version, and deploy prompts from within Langfuse. * **Monitor:** Track metrics (cost, latency, quality) and gain insights from dashboards & data exports, collect and calculate scores for your LLM completions, run model-based evaluations, collect user feedback, and manually score observations in Langfuse. * **Test:** Track and test app behaviour before deploying a new version, test expected in and output pairs and benchmark performance before deploying, and track versions and releases in your application. Langfuse is easy to get started with and offers a generous free tier. You can sign up for Langfuse Cloud or deploy Langfuse locally or on your own infrastructure. Langfuse also offers a variety of integrations to make it easy to connect to your LLM applications.

LangWatch is a monitoring and analytics platform designed to track, visualize, and analyze interactions with Large Language Models (LLMs). It offers real-time telemetry to optimize LLM cost and latency, a user-friendly interface for deep insights into LLM behavior, user analytics for engagement metrics, detailed debugging capabilities, and guardrails to monitor LLM outputs for issues like PII leaks and toxic language. The platform supports OpenAI and LangChain integrations, simplifying the process of tracing LLM calls and generating API keys for usage. LangWatch also provides documentation for easy integration and self-hosting options for interested users.

This open-source course teaches how to build an advanced RAG and LLM system using LLMOps and ML systems best practices. It helps you create an AI assistant that leverages your personal knowledge base to answer questions, summarize documents, and provide insights. The course covers topics such as LLM system architecture, pipeline orchestration, large-scale web crawling, model fine-tuning, and advanced RAG features. It is suitable for ML/AI engineers and data/software engineers & data scientists looking to level up to production AI systems. The course is free, with minimal costs for tools like OpenAI's API and Hugging Face's Dedicated Endpoints. Participants will build two separate Python applications for offline ML pipelines and online inference pipeline.

The LLM Engineer's Handbook is an official repository containing a comprehensive guide on creating an end-to-end LLM-based system using best practices. It covers data collection & generation, LLM training pipeline, a simple RAG system, production-ready AWS deployment, comprehensive monitoring, and testing and evaluation framework. The repository includes detailed instructions on setting up local and cloud dependencies, project structure, installation steps, infrastructure setup, pipelines for data processing, training, and inference, as well as QA, tests, and running the project end-to-end.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment interfaces and libraries for understanding AI systems. It empowers developers and stakeholders to develop and monitor AI responsibly, enabling better data-driven actions. The toolbox includes visualization widgets for model assessment, error analysis, interpretability, fairness assessment, and mitigations library. It also offers a JupyterLab extension for managing machine learning experiments and a library for measuring gender bias in NLP datasets.

A specialist in DataOps and Data Observability, aiding in data management and monitoring.

An expert MLOps engineer assisting in DevOps and pipeline optimization.

Approachable GitLab CI/CD expert, balancing technicality with clarity.

Expert in CI/CD integration for various tools and languages.

Expert in CI/CD pipeline configurations and DevOps best practices.

Stay informed on AGI - with the latest, most relevant news.

Seismic and volcanic monitor with in-depth data and visuals.

Frost Protection System is an AI bot optimizing open field farming of fruits, vegetables, and flowers, combining real-time data and AI to boost yield, cut costs, and foster sustainable practices in a user-friendly interface.

Your SRE, DevOps and Observability buddy

EUR/ZAR analysis with real-time rate and current news focus

stunspot's advisor for all things Financial Cybersec

Expert in Broad Financial Compliance

Specializes in Anti-Money Laundering/Counter-Financing of Terrorism compliance and analysis.

Advanced DevOps Guru with Linux distro and cloud-native tech expertise.

Facilitates Socratic dialogues to refine your network state.

Expert in Maritime Conservation & Drone Technology.

A bot that provides detailed cybersecurity threat intelligence.

Ensures product quality through systematic process monitoring and evaluation.

Global Conflict Analyst that will provide a 'wartime update' on the worst global conflict atm.