Best AI tools for< Monitor Deployments >

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.

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.

Mystic.ai is an AI tool designed to deploy and scale Machine Learning models with ease. It offers a fully managed Kubernetes platform that runs in your own cloud, allowing users to deploy ML models in their own Azure/AWS/GCP account or in a shared GPU cluster. Mystic.ai provides cost optimizations, fast inference, simpler developer experience, and performance optimizations to ensure high-performance AI model serving. With features like pay-as-you-go API, cloud integration with AWS/Azure/GCP, and a beautiful dashboard, Mystic.ai simplifies the deployment and management of ML models for data scientists and AI engineers.

The website is a platform for managing software deployments. It allows users to control the deployment process, ensuring smooth and efficient delivery of software updates and changes. Users can monitor the status of deployments, pause or resume them as needed, and troubleshoot any issues that may arise during the deployment process.

The website is a platform for managing software deployments. It allows users to automate the deployment process, ensuring smooth and efficient delivery of software updates and changes to servers and applications. With features like version control, rollback options, and monitoring capabilities, users can easily track and manage their deployments. The platform simplifies the deployment process, reducing errors and downtime, and improving overall productivity.

The website is a platform that allows users to deploy applications. It provides a service for managing and controlling the deployment process of software applications. Users can pause and resume deployments as needed, ensuring smooth and efficient deployment procedures. The platform offers a user-friendly interface for monitoring and managing deployment tasks.

PluginLab is a platform offering a no-code User Management and Monetization solution for GPT plugins. It allows users to connect with Stripe and various OAuth Providers in just 5 minutes. The platform aims to help developers monetize their GPT plugins efficiently while providing a seamless user experience. PluginLab also offers features like authentication, monetization, real-time events monitoring, deployments, and insights generation to help users grow their business.

Arthur is an industry-leading MLOps platform that simplifies deployment, monitoring, and management of traditional and generative AI models. It ensures scalability, security, compliance, and efficient enterprise use. Arthur's turnkey solutions enable companies to integrate the latest generative AI technologies into their operations, making informed, data-driven decisions. The platform offers open-source evaluation products, model-agnostic monitoring, deployment with leading data science tools, and model risk management capabilities. It emphasizes collaboration, security, and compliance with industry standards.

Radicalbit is an MLOps and AI Observability platform that helps businesses deploy, serve, observe, and explain their AI models. It provides a range of features to help data teams maintain full control over the entire data lifecycle, including real-time data exploration, outlier and drift detection, and model monitoring in production. Radicalbit can be seamlessly integrated into any ML stack, whether SaaS or on-prem, and can be used to run AI applications in minutes.

Seldon is an MLOps platform that helps enterprises deploy, monitor, and manage machine learning models at scale. It provides a range of features to help organizations accelerate model deployment, optimize infrastructure resource allocation, and manage models and risk. Seldon is trusted by the world's leading MLOps teams and has been used to install and manage over 10 million ML models. With Seldon, organizations can reduce deployment time from months to minutes, increase efficiency, and reduce infrastructure and cloud costs.

Striveworks is an AI application that offers a Machine Learning Operations Platform designed to help organizations build, deploy, maintain, monitor, and audit machine learning models efficiently. It provides features such as rapid model deployment, data and model auditability, low-code interface, flexible deployment options, and operationalizing AI data science with real returns. Striveworks aims to accelerate the ML lifecycle, save time and money in model creation, and enable non-experts to leverage AI for data-driven decisions.

Narrow AI is an AI application that autonomously writes, monitors, and optimizes prompts for any model, enabling users to ship AI features 10x faster at a fraction of the cost. It streamlines the workflow by allowing users to test new models in minutes, compare prompt performance, and deploy on the optimal model for their use case. Narrow AI helps users maximize efficiency by generating expert-level prompts, adapting prompts to new models, and optimizing prompts for quality, cost, and speed.

Dynamiq is an operating platform for GenAI applications that enables users to build compliant GenAI applications in their own infrastructure. It offers a comprehensive suite of features including rapid prototyping, testing, deployment, observability, and model fine-tuning. The platform helps streamline the development cycle of AI applications and provides tools for workflow automations, knowledge base management, and collaboration. Dynamiq is designed to optimize productivity, reduce AI adoption costs, and empower organizations to establish AI ahead of schedule.

This is a unified DevOps platform to build AI applications. It provides a comprehensive set of tools and services to help developers build, deploy, and manage AI applications. The platform includes a variety of features such as a code editor, a debugger, a profiler, and a deployment manager. It also provides access to a variety of AI services, such as natural language processing, machine learning, and computer vision.

JFrog ML is an AI platform designed to streamline AI development from prototype to production. It offers a unified MLOps platform to build, train, deploy, and manage AI workflows at scale. With features like Feature Store, LLMOps, and model monitoring, JFrog ML empowers AI teams to collaborate efficiently and optimize AI & ML models in production.

Fathom5 is a company that specializes in the intersection of AI and industrial systems. They offer a range of products and services to help customers build more resilient, flexible, and efficient industrial systems. Fathom5's approach is unique in that they take a security-first approach to cyber-physical system design. This means that security is built into every stage of the development process, from ideation to engineering to testing to deployment. This approach has been proven to achieve higher system resiliency and faster regulatory compliance at a reduced cost.

Heroku is a cloud platform that enables developers to build, deliver, monitor, and scale applications quickly and easily. It supports multiple programming languages and provides a seamless deployment process. With Heroku, developers can focus on coding without worrying about infrastructure management.

PoplarML is a platform that enables the deployment of production-ready, scalable ML systems with minimal engineering effort. It offers one-click deploys, real-time inference, and framework agnostic support. With PoplarML, users can seamlessly deploy ML models using a CLI tool to a fleet of GPUs and invoke their models through a REST API endpoint. The platform supports Tensorflow, Pytorch, and JAX models.

HostAI is a platform that allows users to host their artificial intelligence models and applications with ease. It provides a user-friendly interface for managing and deploying AI projects, eliminating the need for complex server setups. With HostAI, users can seamlessly run their AI algorithms and applications in a secure and efficient environment. The platform supports various AI frameworks and libraries, making it versatile for different AI projects. HostAI simplifies the process of AI deployment, enabling users to focus on developing and improving their AI models.

Meysey is an AI fraud protection application designed for accountants and IT managed service providers. It offers automated fraud scanning using financial and operational data to help organizations prosper against the evolving fraud landscape. Meysey provides scalable solutions with seamless deployment and tailored pricing, enabling users to gain visibility of bribery and corruption risk, identify conflicts of interest, baseline financial patterns, and comply with fraud legislation. The application integrates with finance and business operations tools to analyze data across the commercial landscape, providing actionable insights to enhance business resilience and reduce fraud risk.

Sample notebooks for IBM Watsonx.ai for IBM Cloud and IBM Watsonx.ai software product. The notebooks demonstrate capabilities such as running experiments on model building using AutoAI or Deep Learning, deploying third-party models as web services or batch jobs, monitoring deployments with OpenScale, managing model lifecycles, inferencing Watsonx.ai foundation models, and integrating LangChain with Watsonx.ai. Notebooks with Python code and the Python SDK can be found in the `python_sdk` folder. The REST API examples are organized in the `rest_api` folder.

AI-Assisted DevOps is a 10-day course focusing on integrating artificial intelligence (AI) technologies into DevOps practices. The course covers various topics such as AI for observability, incident response, CI/CD pipeline optimization, security, and FinOps. Participants will learn about running large language models (LLMs) locally, making API calls, AI-powered shell scripting, and using AI agents for self-healing infrastructure. Hands-on activities include creating GitHub repositories with bash scripts, generating Docker manifests, predicting server failures using AI, and building AI agents to monitor deployments. The course culminates in a capstone project where learners implement AI-assisted DevOps automation and receive peer feedback on their projects.

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