Search Results for "platform-tools"
Sort By:
125 projects for "platform-tools" with 2 filters applied:
-
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
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.
-
1
LibrePhotos
A self-hosted open source photo management service
...The application includes AI-powered tools that automatically analyze images to detect faces, objects, and locations, allowing photos to be grouped and searched more efficiently. LibrePhotos supports a wide variety of media formats and provides a web interface that can be accessed from different devices and operating systems. The platform is built using a Django backend and a React frontend, forming a full-stack web application architecture. -
2
MediaPipe Solutions
Cross-platform, customizable ML solutions
MediaPipe is an open-source framework developed by Google for building cross-platform machine learning pipelines that process audio, video, and other streaming data in real time. The system provides developers with tools and reusable components that allow them to combine multiple machine learning models with preprocessing and postprocessing logic into efficient perception pipelines. These pipelines can run on a wide variety of platforms including mobile devices, desktop systems, web browsers, and embedded edge devices. ... -
3
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). -
4
Quantitative Trading System
A comprehensive quantitative trading system with AI-powered analysis
Quantitative Trading System is a comprehensive quantitative trading platform that integrates artificial intelligence, financial data analysis, and automated strategy execution within a unified software system. The project is designed to provide an end-to-end infrastructure for building and operating algorithmic trading strategies in financial markets. It includes tools for collecting and processing market data from multiple sources, performing statistical and machine learning analysis, and generating trading signals based on quantitative models. ... -
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.
-
5
ML Intern
ML engineer that reads papers, trains models, and ships ML models
...The project includes tutorials, datasets, and example implementations that guide users through different aspects of ML development. It emphasizes hands-on learning, encouraging users to build and experiment rather than passively consume information. The repository also introduces tools and libraries commonly used in the Hugging Face ecosystem. It is structured to help users progressively build skills and confidence in AI development. Overall, ML Intern is a practical learning platform for aspiring machine learning engineers. -
6
ggml
Tensor library for machine learning
...The project emphasizes portability and performance, enabling machine learning inference across a wide range of hardware environments including CPUs and specialized accelerators. It is widely used as a foundational component in projects that run large language models locally, including tools that perform inference for transformer-based models. The library also implements optimization algorithms and computation graph functionality so developers can build training and inference workflows directly on top of its tensor operations. -
7
FISSURE
The RF and reverse engineering framework for everyone
FISSURE is an open-source radio frequency analysis and signal intelligence framework built to support software-defined radio research, wireless security experimentation, and protocol reverse engineering. The project brings together tools for capturing, inspecting, decoding, replaying, and analyzing RF signals across a wide range of wireless technologies. It is designed as a practical environment for researchers and operators who need to move from raw spectrum observation to structured investigation without stitching together too many separate utilities by hand. The platform supports workflows related to signal discovery, demodulation, packet inspection, fuzzing, and attack simulation, making it useful for both defensive research and controlled lab testing. ... -
8
Data Science Articles from CodeCut
Collection of useful data science topics along with articles
The Data-science repository from CodeCutTech is a curated collection of educational content focused on practical tools and workflows used in modern data science projects. Instead of providing a single software package, the repository aggregates articles, tutorials, and examples covering many topics within the data science ecosystem. The materials address areas such as MLOps, data management, project organization, testing practices, visualization techniques, and productivity tools used by data scientists. ... -
9
Karpathy
An agentic Machine Learning Engineer
...Its startup script automatically prepares the environment by creating a sandbox directory, installing key ML libraries, and launching the agent interface. The system is tightly integrated with the Claude Scientific Skills ecosystem, enabling the agent to leverage specialized scientific and machine learning tools. It is intended primarily for research and experimentation with autonomous ML workflows rather than as a polished production platform. Overall, karpathy represents an early step toward fully automated machine learning engineering driven by agentic AI systems. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
10
skfolio
Python library for portfolio optimization built on top of scikit-learn
...It supports a wide range of allocation methods, from classical mean-variance optimization to modern techniques that rely on clustering, factor models, and risk-based allocations. The framework also includes tools for evaluating portfolio performance under different market conditions, enabling users to test robustness and reduce the risk of overfitting. -
11
Deepnote
Deepnote is a drop-in replacement for Jupyter
Deepnote is an open-source collaborative data science notebook platform designed as a modern alternative to traditional Jupyter notebooks. The project provides an AI-first computational environment where users can write, analyze, and share code, data, and visualizations in a single integrated workspace. Built on top of the Jupyter kernel ecosystem, it maintains compatibility with existing notebook workflows while introducing additional features focused on collaboration and automation. ... -
12
AI Engineer Headquarters
A collection of scientific methods, processes, algorithms
AI-Engineer-Headquarters is a comprehensive educational repository designed to help developers become advanced AI engineers through a structured learning path and practical system-building exercises. The project serves as a curated collection of resources, methodologies, and tools covering topics across the entire artificial intelligence development lifecycle. Rather than focusing only on theoretical knowledge, the repository emphasizes applied learning and encourages engineers to build real systems that incorporate machine learning, large language models, data pipelines, and AI infrastructure. The curriculum includes a progression of topics such as foundational AI engineering skills, machine learning systems design, large language model usage, retrieval-augmented generation systems, model fine-tuning, and autonomous AI agents. ... -
13
Olmo-core
PyTorch building blocks for the OLMo ecosystem
OLMo-core is a Python library from AI2 that provides reusable PyTorch building blocks for training large language models at scale. It underpins the broader OLMo ecosystem and includes components for models, data loading, distributed execution, optimization, checkpointing, evaluation, and generation. Official scripts reproduce training configurations for released OLMo models. The library supports advanced attention backends, mixture-of-experts architectures, float8 training, and fused... -
14
TensorFlow Quantum
Open-source Python framework for hybrid quantum-classical ml learning
...The framework enables researchers and developers to represent quantum circuits as data and integrate them directly into machine learning workflows. By combining classical deep learning techniques with quantum algorithms, the platform allows experimentation with quantum machine learning methods that may offer advantages for certain computational tasks. TensorFlow Quantum integrates with the Cirq quantum computing framework to define and manipulate quantum circuits, while leveraging TensorFlow’s infrastructure for optimization, automatic differentiation, and large-scale computation. ... -
15
PySINDy
A package for the sparse identification of nonlinear dynamical systems
...This approach is particularly valuable in scientific fields such as physics, engineering, and biology where researchers seek both predictive accuracy and theoretical insight. The library provides tools for constructing libraries of candidate functions, performing sparse regression, and validating discovered models against observed data. It integrates with standard Python scientific computing libraries, making it easy to apply to experimental datasets or simulated systems. -
16
pyAudioAnalysis
Python Audio Analysis Library: Feature Extraction, Classification
...Because the library integrates machine learning algorithms with signal processing tools, it enables researchers to develop complete audio analysis pipelines using a single framework. -
17
Netflix Maestro
Netflix’s Workflow Orchestrator
...The platform enables engineers and data scientists to define workflows using structured configuration files and execute tasks across diverse compute environments, including scripts, containers, and notebook environments. Maestro provides built-in mechanisms for retry logic, task scheduling, dependency management, and error handling, which are essential when orchestrating production-scale pipelines. -
18
TorchCode
Practice implementing softmax, attention, GPT-2 and more
TorchCode is an interactive learning and practice platform designed to help developers master PyTorch by implementing core machine learning operations and architectures from scratch. It is structured similarly to competitive programming platforms like LeetCode but focuses specifically on tensor operations and deep learning concepts. The platform provides a collection of curated problems that cover fundamental topics such as activation functions, normalization layers, attention mechanisms, and full transformer architectures. ... -
19
Advanced AI explainability for PyTorch
Advanced AI Explainability for computer vision
...The library supports a wide variety of tasks including image classification, object detection, semantic segmentation, and similarity analysis. It also provides metrics and evaluation tools that help measure the reliability and quality of the generated explanations. By integrating easily with PyTorch models, the library allows developers to diagnose model errors, detect biases in datasets, and improve model transparency. -
20
SimpleTuner
A general fine-tuning kit geared toward image/video/audio diffusion
SimpleTuner is an open-source toolkit designed to simplify the fine-tuning of modern diffusion models for generating images, video, and audio. The project focuses on providing a clear and understandable training environment for researchers, developers, and artists who want to customize generative AI models without navigating complex machine learning pipelines. It supports fine-tuning workflows for models such as Stable Diffusion variants and other diffusion architectures, enabling users to... -
21
Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
...The repository includes datasets, Jupyter notebooks, documentation, and example code that walk learners through the entire machine learning workflow from problem definition to model deployment. The course introduces essential tools such as NumPy, pandas, Matplotlib, and scikit-learn before moving on to deep learning with frameworks like TensorFlow and Keras. It also includes milestone projects that demonstrate how to build end-to-end machine learning systems using real datasets, including classification and regression tasks. -
22
MLOps Zoomcamp
Free MLOps course from DataTalks.Club
...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. -
23
TNT
A lightweight library for PyTorch training tools and utilities
TNT is a lightweight training framework developed by Meta that simplifies the process of building and managing machine learning training loops using PyTorch. The project focuses on providing a flexible yet structured environment for implementing training pipelines without the complexity of large deep learning frameworks. It introduces modular abstractions that allow developers to organize training logic into reusable components such as trainers, evaluators, and callbacks. This design helps... -
24
Apache Hamilton
Helps data scientists define testable self-documenting dataflows
Apache Hamilton is an open-source Python framework designed to simplify the creation and management of dataflows used in analytics, machine learning pipelines, and data engineering workflows. The framework enables developers to define data transformations as simple Python functions, where each function represents a node in a dataflow graph and its parameters define dependencies on other nodes. Hamilton automatically analyzes these functions and constructs a directed acyclic graph... -
25
CUDA Containers for Edge AI & Robotics
Machine Learning Containers for NVIDIA Jetson and JetPack-L4T
...By using containerized environments, developers can ensure that their applications run consistently across different Jetson platforms and JetPack versions. The repository also includes build tools and package management utilities that help automate the process of assembling machine learning environments.
