Search Results for "lifecycle"
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40 projects for "lifecycle" with 2 filters applied:
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Stop vibe-debugging.AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
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CyberStrikeAI
CyberStrikeAI is an AI-native security testing platform built in Go
...It supports role-based testing, letting teams define security roles with tailored tool access and prompts, and includes a skills system that encapsulates specialized testing strategies that the AI can incorporate into its planning. Through comprehensive lifecycle management, results are tracked, aggregated, and visualized, with support for versioned persistence, search, and risk severity scoring. -
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MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
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Wanwu AI Agent Platform
Enterprise AI agent platform for workflows, models, and RAG apps
...Wanwu integrates large language models with business process automation, allowing developers to design complex, production-ready AI solutions tailored to enterprise needs. It includes comprehensive model lifecycle management capabilities, enabling users to configure, monitor, and manage different models efficiently. Wanwu also supports knowledge base construction, allowing organizations to incorporate structured and unstructured data into their AI applications. With a focus on openness and extensibility, it encourages developers to build on top of its ecosystem while maintaining a secure and compliant architecture for business use cases. -
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BMad Method
Breakthrough Method for Agile Ai Driven Development
BMad Method is a comprehensive AI-driven software development framework that structures the entire lifecycle of building applications through coordinated agent workflows and agile methodologies. It transforms AI from a reactive assistant into a structured team of specialized roles such as product manager, architect, developer, and QA, each operating within predefined workflows. The system guides users through phases including analysis, planning, solution design, and implementation, ensuring that projects are approached systematically rather than through ad hoc prompting. ... -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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MLOps Zoomcamp
Free MLOps course from DataTalks.Club
...The course is designed to teach data scientists and engineers how to move machine learning models from experimentation environments into scalable production services. The repository provides lessons, code examples, and assignments that cover the entire MLOps lifecycle, including model training, experiment tracking, deployment, monitoring, and infrastructure management. Students learn to use widely adopted tools such as MLflow, orchestration frameworks, and cloud platforms to manage machine learning pipelines. The curriculum emphasizes hands-on projects so learners gain practical experience building automated ML pipelines and maintaining deployed models. -
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Polyaxon
MLOps tools for managing & orchestrating the ML LifeCycle
Polyaxon is an open-source machine learning operations (MLOps) platform built to help individuals, teams, and organizations develop, train, orchestrate, and monitor machine learning and deep learning workflows at scale with reproducibility and automation as core principles. It provides a unified solution for tracking experiments, managing datasets, scheduling jobs, and comparing results across runs, which greatly improves productivity and collaboration in data science teams. Polyaxon... -
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SimpleMem
SimpleMem: Efficient Lifelong Memory for LLM Agents
...It provides easy-to-use APIs for storing structured memory entries, querying those memories using semantic search, and retrieving context to augment prompt inputs for downstream processing. Unlike monolithic systems where memory management is ad-hoc, SimpleMem formalizes a memory lifecycle—write, index, retrieve, refine—so applications can handle user history, document collections, or dynamic contextual state systematically. It supports customizable embedding models, efficient vector indexes, and relevance weighting, making it practical for building assistants, personal agents, or domain-specific retrieval systems that need persistent knowledge. -
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MiMo Code
Where Models and Agents Co-Evolve
MiMo-Code is a terminal-native AI coding assistant built for real software projects. It can read and edit code, run commands, manage Git, and preserve project knowledge across sessions. The tool includes multiple agent modes, including a build mode for development, a plan mode for read-only analysis, and a compose mode for structured workflows. Its persistent memory system stores project notes, checkpoints, scratch notes, and task progress so the assistant can resume work with context. It... -
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Coze Loop
Next-generation AI Agent Optimization Platform
Coze Loop is a developer-oriented platform that provides full lifecycle management for AI agents, covering everything from prompt engineering to production monitoring. The project aims to simplify the increasingly complex workflow of building reliable AI agents by offering integrated tools for debugging, evaluation, observability, and optimization. Through its visual playground, developers can test prompts interactively and compare outputs across different language models. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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Scene Framework
Android Single Activity Framework compatible with Fragment
...As such, Scene could serve as a backbone for media apps, games, or modular user interface projects, offering abstractions for scene management, resource loading, event dispatching, and lifecycle handling. Because of its open-source status, developers may adapt it to their own use-cases — customizing rendering, UI transitions, or content management — though the maturity and documentation may vary. -
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OpenShorts
Free & open source AI video platform
...Its architecture uses modern technologies such as FastAPI, FFmpeg, and AI models for transcription, analysis, and rendering. Designed for creators and businesses, it automates the entire lifecycle of short-form video production. -
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VIPER
AI-powered red team platform for adversary simulation toolkit
Viper is a comprehensive red teaming and adversary simulation platform designed to support cybersecurity professionals in conducting advanced security assessments. It integrates a wide range of tools and capabilities required for penetration testing, post-exploitation, and attack simulation workflows into a unified environment. Viper emphasizes ease of use through a graphical interface, allowing users to manage complex operations without relying solely on command-line tools. It includes a... -
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Machine Learning Zoomcamp
Learn ML engineering for free in 4 months
Machine Learning Zoomcamp is an open-source repository containing the materials for a comprehensive course that teaches machine learning engineering from fundamentals to deployment. The project is designed to guide learners through the complete lifecycle of developing machine learning systems, starting with data preparation and model training and ending with production deployment. Participants learn how to build regression and classification models using Python libraries such as NumPy, Pandas, and Scikit-learn. The course also introduces more advanced topics including decision trees, ensemble methods, and neural networks. ... -
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AI Marketing Skills
Open-source AI marketing skills for Claude Code
...The system is organized into multiple domains such as growth experimentation, sales pipeline generation, content production, outbound marketing, SEO optimization, and financial analysis, effectively covering the entire revenue lifecycle of a business. Each skill functions as an executable capability that can be invoked on demand, enabling users to perform tasks like running A/B tests, generating high-quality content, or analyzing conversion funnels with minimal manual effort. -
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Khazix Skills
Digital Life Kazik Open Source AI Skills Collection
...It acts as a pipeline that analyzes a repository’s metadata, extracts relevant information such as README content and commit hashes, and converts it into a standardized skill format that can be integrated into agent ecosystems. The system emphasizes lifecycle management by embedding versioning, traceability, and metadata directly into generated skill files, allowing future updates and synchronization with the original repository. It also generates wrapper scripts that enable AI agents to interact with the underlying repository functionality without requiring deep manual integration. ... -
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AG-UI
The Agent-User Interaction Protocol
...It is designed to make agent behavior visible, interactive, and controllable inside real-time front-end experiences. Instead of treating an AI agent as a black-box chat endpoint, AG-UI defines structured events for messages, tool calls, state changes, lifecycle updates, and user interactions. This makes it easier for developers to build agent-powered apps that stream progress, request human input, update UI state, and coordinate complex workflows. The project is especially useful for teams building copilots, workflow assistants, multi-agent products, or custom AI interfaces. Overall, AG-UI provides a shared communication layer between autonomous systems and the interfaces where people actually use them. -
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FIT Framework
An enterprise-level AI development framework
FIT Framework is an open-source infrastructure designed to support the development, training, and evaluation of machine learning and AI models through a modular and scalable architecture. It aims to streamline the lifecycle of AI systems by providing standardized components for data processing, model training, evaluation, and deployment. The framework is particularly useful for research and production environments where reproducibility and consistency are critical, as it enforces structured workflows and configurable pipelines. It supports experimentation with different models and datasets, allowing developers to iterate quickly while maintaining clear organization of results and configurations. ... -
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ComfyUI-Copilot
AI assistant for ComfyUI workflow generation, debugging, and tuning
...ComfyUI-Copilot focuses on reducing the complexity of building node-based pipelines for generative AI tasks such as image generation, making it more accessible to both beginners and experienced users. It supports the entire workflow lifecycle, including generation, debugging, rewriting, and parameter optimization, helping users iterate more efficiently. ComfyUI-Copilot leverages large language model capabilities to analyze user intent, recommend nodes, and suggest models that match specific requirements. It also provides automated error detection and repair suggestions, improving reliability during development. -
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LMOps
General technology for enabling AI capabilities w/ LLMs and MLLMs
...The project explores the technologies and methodologies required to move foundation models from research environments into production-grade AI products. It includes experimental tools and frameworks that help developers optimize prompts, design workflows for generative models, and manage the lifecycle of LLM-based systems. The initiative also investigates techniques for improving the reliability, scalability, and maintainability of applications powered by large models. By addressing challenges such as prompt engineering, evaluation strategies, and deployment infrastructure, LMOps aims to establish best practices for operating large language model systems in real-world environments. -
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OllamaSharp
The easiest way to use Ollama in .NET
OllamaSharp is an open-source .NET library that provides strongly typed bindings for interacting with the Ollama API, making it easier for developers to integrate local large language models into C# and .NET applications. The project acts as a wrapper around the Ollama API, exposing all endpoints through asynchronous methods that allow developers to perform tasks such as generating text, creating embeddings, and managing models. It supports both local and remote Ollama instances, enabling... -
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EverMemOS
Long-term memory OS for AI with structured recall and context awarenes
...EverMemOS goes beyond simple retrieval by actively applying stored knowledge to current tasks, improving personalization and consistency. EverMemOS uses a multi-stage memory lifecycle to convert raw dialogue into structured semantic data, supporting long-horizon reasoning and adaptive behavior across sessions. -
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nanochat
The best ChatGPT that $100 can buy
nanochat is a from-scratch, end-to-end “mini ChatGPT” that shows the entire path from raw text to a chatty web app in one small, dependency-lean codebase. The repository stitches together every stage of the lifecycle: tokenizer training, pretraining a Transformer on a large web corpus, mid-training on dialogue and multiple-choice tasks, supervised fine-tuning, optional reinforcement learning for alignment, and finally efficient inference with caching. Its north star is approachability and speed: you can boot a fresh GPU box and drive the whole pipeline via a single script, producing a usable chat model in hours and a clear markdown report of what happened. ... -
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Swarms
Enterprise multi-agent orchestration framework for scalable AI apps
...It supports integration with multiple model providers and existing ecosystems, allowing developers to combine different AI tools and frameworks within a unified system. Swarms also includes mechanisms for agent lifecycle management, memory handling, and dynamic composition, making it adaptable to evolving workloads. Additionally, it focuses on developer productivity through APIs, CLI tools, and templates that simplify building and deploying agent-based applications. -
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Machine Learning Systems
Introduction to Machine Learning Systems
...Rather than concentrating only on model training, the material emphasizes the broader discipline of AI engineering, covering efficiency, reliability, deployment, and evaluation across the full lifecycle of intelligent systems. The repository includes textbook content, supporting labs, and companion tools such as TinyTorch to help learners move from theory to hands-on experimentation. Its mission is to establish AI systems engineering as a foundational discipline alongside traditional software and computer engineering. The project is structured to guide users through reading, building, and deploying workflows, including running labs on edge devices like Arduino and Raspberry Pi. -
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Cube Studio
Cube Studio open source cloud native one-stop machine learning
Cube Studio is an open-source, cloud-native end-to-end machine learning and AI platform designed to support the full lifecycle of AI development — from data preparation and interactive notebook coding to distributed training, model tuning, and deployment in production-ready environments. It provides a unified interface where teams can manage data sources, track datasets, and build pipelines using drag-and-drop workflow orchestration, making it accessible for both engineers and data scientists working at scale. ...
