Search Results for "ml"
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91 projects for "ml" with 2 filters applied:
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Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
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applied-ml
Papers & tech blogs by companies sharing their work on data science
The applied-ml repository is a rich, curated collection of papers, technical articles, and case-study blog posts about how machine learning (ML) and data-driven systems are applied in real production environments by major companies. Instead of focusing solely on theoretical ML research, this repo highlights industry-scale challenges: data collection, quality, infrastructure, feature stores, model serving, monitoring, scalability, and how ML is embedded in product workflows. ... -
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Machine Learning Tutorials Repository
Dive deep into the realms of Machine Learning and other topics
The Machine Learning Tutorials Repository is a comprehensive collection of resources, examples, and implementations designed to help users understand and apply machine learning concepts. It covers a wide range of topics, including supervised learning, unsupervised learning, neural networks, and data preprocessing techniques. The project is structured to provide both theoretical explanations and practical code examples, making it suitable for learners at different levels. It includes... -
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Flama
Fire up your models with the flame
Flama is a python library which establishes a standard framework for development and deployment of APIs with special focus on machine learning (ML). The main aim of the framework is to make ridiculously simple the deployment of ML APIs, simplifying (when possible) the entire process to a single line of code. The library builds on Starlette, and provides an easy-to-learn philosophy to speed up the building of highly performant GraphQL, REST and ML APIs. Besides, it comprises an ideal solution for the development of asynchronous and production-ready services, offering automatic deployment for ML models. -
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Visual Blocks
Visual Blocks for ML is a Google visual programming framework
...Because everything lives in the browser, sharing is as simple as exporting a project or link, and collaborators can experiment without installing toolchains. For educators and product teams alike, Visual Blocks reduces the distance from idea to interactive proof-of-concept by turning ML diagrams. -
Forever Free Full-Stack Observability | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Bacalhau
Community-driven, simple, yet powerful framework
...Bacalhau supports various runtime environments and is designed to make decentralized data processing as accessible as traditional cloud computing. It’s especially useful for large-scale AI/ML jobs, scientific research, and content indexing in Web3 ecosystems. -
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StructuralEquationModels.jl
A fast and flexible Structural Equation Modelling Framework
...For those, you can decide to provide analytical gradients or use finite difference approximation / automatic differentiation. You can choose to mix loss functions natively found in this package and those you provide. In such cases, you optimize over a sum of different objectives (e.g. ML + Ridge). This strategy also applies to gradients, where you may supply analytic gradients or opt for automatic differentiation or mixed analytical and automatic differentiation. You may consider using this package if you need extensibility and/or speed, and if you want to extend SEM. -
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Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. -
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Groq Python
The official Python Library for the Groq API
...This makes it easy to integrate Groq-powered AI capabilities into backend services, data pipelines, research notebooks, or applications written in Python. For those building AI-based tooling, automation scripts, or ML-backed backends, groq-python abstracts away HTTP request plumbing and exposes a clean API, accelerating development and reducing boilerplate. -
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CoreNet
CoreNet: A library for training deep neural networks
...Its distributed runtime manages synchronization, load balancing, and mixed-precision computation to maximize throughput while minimizing communication bottlenecks. CoreNet integrates tightly with Apple’s proprietary ML stack and hardware, serving as the foundation for research in computer vision, language models, and multimodal systems within Apple AI. The framework includes monitoring tools, fault tolerance mechanisms, and efficient checkpointing for massive training runs. -
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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TensorStore
Library for reading and writing large multi-dimensional arrays
...Rich indexing, slicing, and broadcasting operations make it feel like a familiar array API, while asynchronous I/O pipelines stream chunks efficiently in parallel. Transactional semantics allow atomic updates and consistent snapshots, which is essential for large, shared datasets used by ML and scientific workflows. The library is engineered for scalability—background caching, chunk sharding, and retryable operations keep throughput high even over unreliable networks. With language bindings, it fits into Python-heavy analysis pipelines while retaining a fast C++ core. -
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core.match
An optimized pattern matching library for Clojure
core.match is a high-performance pattern-matching library for Clojure and ClojureScript. It provides an optimized macro-based DSL for structurally matching data—such as sequences, maps, regexes—offering a clearer alternative to nested conditionals or destructuring. A symbol pattern can represent one of three behaviours. Match the value of an existing local binding. Create a "named" wildcard pattern that creates a binding of the given name to the right of the pattern row. -
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The Algorithms Python
All Algorithms implemented in Python
The Algorithms-Python project is a comprehensive collection of Python implementations for a wide range of algorithms and data structures. It serves primarily as an educational resource for learners and developers who want to understand how algorithms work under the hood. Each implementation is designed with clarity in mind, favoring readability and comprehension over performance optimization. The project covers various domains including mathematics, cryptography, machine learning, sorting,... -
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Earth Engine API
Python and JavaScript bindings for calling the Earth Engine API
The Earth Engine API provides Python and JavaScript client libraries for Google Earth Engine, a planetary-scale geospatial analysis platform. With it, users compose lazy, server-side computations over massive catalogs of satellite imagery and vector datasets without handling raw files locally. The API exposes functional operators for map algebra, reducers, joins, and machine learning that scale transparently on Earth Engine’s backend. Developers authenticate once, work interactively in... -
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Superduper
Superduper: Integrate AI models and machine learning workflows
Superduper is a Python-based framework for building end-2-end AI-data workflows and applications on your own data, integrating with major databases. It supports the latest technologies and techniques, including LLMs, vector-search, RAG, and multimodality as well as classical AI and ML paradigms. Developers may leverage Superduper by building compositional and declarative objects that out-source the details of deployment, orchestration versioning, and more to the Superduper engine. This allows developers to completely avoid implementing MLOps, ETL pipelines, model deployment, data migration, and synchronization. ... -
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Professional Services
Common solutions and tools developed by Google Cloud
Professional Services repository is a collection of real-world solutions, tools, and reference implementations developed by Google Cloud’s Professional Services team to address common enterprise challenges. Unlike simple sample repositories, it focuses on production-oriented use cases such as data pipelines, machine learning workflows, infrastructure automation, and security management. The repository contains a wide variety of projects, including tools for validating data migrations,... -
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all AI news
A list of online news & info sources in the AI/ML/Data Science space
all AI news is a curated repository that aggregates and organizes sources for AI-related news and information. It serves as a centralized collection of feeds, links, and resources that can be used to build news aggregation systems or stay updated on developments in artificial intelligence. The project is designed to be easily extendable, allowing users to add new sources or customize the dataset for their specific needs. It is particularly useful for developers building AI news platforms,... -
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Computer Science courses video lectures
List of Computer Science courses with video lectures
This repository is a curated list of full-length computer science video lecture series across many universities and MOOC platforms, helping learners assemble their own curriculum. The list spans foundational topics like algorithms, data structures, operating systems, computer networks, machine learning, and more, all delivered via lectures rather than just textual tutorials. The contributor guidelines encourage adding high-quality courses (not just casual tutorials) so the list remains... -
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Jupyter Docker Stacks
Ready-to-run Docker images containing Jupyter applications
Jupyter Docker Stacks provides a curated set of ready-to-run Docker container images that bundle Jupyter applications with popular data science and computing tools, enabling users to quickly start working in a reproducible environment. These stacks support a range of use cases, from lightweight base notebook images to full featured environments that include scientific computing libraries, machine learning tools, and IDE-like notebook interfaces, all within Docker containers that run... -
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Deep Learning Essay Reading
Read classic and new deep learning papers paragraph by paragraph
Deep Learning Essay Reading repository is a comprehensive collection of machine learning and deep learning research summaries designed to make cutting-edge academic work more accessible. Instead of reading entire dense academic papers, contributors provide structured breakdowns and insights into the most influential research from the past decade, often including explanation highlights and key takeaways. The content spans foundational models, architectures, and training methodologies across... -
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X For You Feed Algorithm
Algorithm powering the For You feed on X
X For You Feed Algorithm is the open-sourced core recommendation system that powers the For You feed on X (the social network formerly known as Twitter), and it represents one of the first times a major social platform has published production-level ranking code for public review and experimentation. The repository contains the full pipeline that ingests user engagement and content candidate data, processes it through retrieval, hydration, filtering, scoring, and selection layers, and... -
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Deequ
Deequ is a library built on top of Apache Spark
...It also includes a little domain-specific language called DQDL (Data Quality Definition Language) which allows declarative specification of quality rules. Users typically run Deequ before feeding data downstream (to ML pipelines, analytics, or production systems), enabling early detection and isolation of data errors. There is also a Python wrapper, PyDeequ, for users who prefer working from Python environments. -
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EKS Best Practices
A best practices guide for day 2 operations
The Amazon EKS Best Practices Guide is a public repository containing comprehensive documentation and guidance for operating production-grade Kubernetes clusters on AWS’s managed service, Amazon EKS. Rather than a code library, it serves as a reference catalogue of patterns, anti-patterns, checklists and architectures across domains such as security, reliability, scalability, networking, cost optimization and hybrid cloud deployments. The repository is maintained by AWS but open to... -
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PLT (Programming Languages Theory)
Programming Language Theory
Curated roadmap to Programming Language Theory, collecting seminal papers, books, and resources into a navigable structure for self-study. It spans foundational topics like lambda calculus, type systems, interpreters, compilers, and formal semantics, while also pointing to contemporary areas such as effect systems, dependent types, and verification. Each section clusters materials by theme so learners can build understanding step by step instead of grazing at random. The list emphasizes... -
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Open LLMs
A list of open LLMs available for commercial use
Open LLMs, by the same author behind applied-ml — serves as a curated directory of open large language models (LLMs) that are available for commercial or open-source use. Rather than proprietary or closed-source LLMs, this repo focuses on freely available or permissively licensed models that practitioners can download, run, fine-tune or integrate without restrictive licensing. For teams or developers interested in experimenting with LLMs but wanting to avoid vendor lock-in or licensing constraints, open-llms offers a practical starting point. ... -
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Python-Spider
Python3 web crawler practice
Python-Spider is a repository intended to teach or provide examples for writing web spiders / crawlers in Python — part of a broader learning and resource collection by its author. The code and documentation are oriented toward beginners or intermediate learners who want to learn how to fetch, parse, and extract data from websites programmatically. As part of the author’s public learning-path repositories, python-spider likely includes examples of HTTP requests, HTML parsing, maybe...
