Amazon SageMaker Course is a comprehensive guide for AWS Certified Machine Learning Specialty (MLS-C01) that covers training, optimizing, deploying, and integrating machine learning models in the AWS cloud. The course includes hands-on experience with AWS built-in algorithms, Bring Your Own models, and ready-to-use AI capabilities. It also provides a complete guide to AWS Certified Machine Learning – Specialty certification, along with a high-quality timed practice test. Participants will learn how to integrate trained models into their applications and receive prompt support through the course Q&A forum and private messaging.

The AWS Healthcare and Life Sciences AI/ML Immersion Day workshops provide hands-on experience for customers to learn about AI/ML services, gain a deep understanding of AWS AI/ML services, and understand best practices for using AI/ML in the context of HCLS applications. The workshops cater to individuals at all levels, from machine learning experts to developers and managers, and cover topics such as training, testing, MLOps, deployment practices, and software development life cycle in the context of AI/ML. The repository contains notebooks that can be used in AWS Instructure-Led Labs or self-paced labs, offering a comprehensive learning experience for integrating AI/ML into applications.

Start Machine Learning in 2024 is a comprehensive guide for beginners to advance in machine learning and artificial intelligence without any prior background. The guide covers various resources such as free online courses, articles, books, and practical tips to become an expert in the field. It emphasizes self-paced learning and provides recommendations for learning paths, including videos, podcasts, and online communities. The guide also includes information on building language models and applications, practicing through Kaggle competitions, and staying updated with the latest news and developments in AI. The goal is to empower individuals with the knowledge and resources to excel in machine learning and AI.

This repository introduces and helps organizations get started with building AI Apps and incorporating Large Language Models (LLMs) into them. The workshop covers topics such as prompt engineering, AI orchestration, and deploying AI apps. Participants will learn how to use Azure OpenAI, Langchain/ Semantic Kernel, Qdrant, and Azure AI Search to build intelligent applications.

This repository is for the AI Tour workshop: Interacting with Multimodal models in Azure AI Foundry. The workshop provides a hands-on introduction to core concepts and best practices for interacting with OpenAI models in Azure AI Foundry portal. Participants can innovate with Azure OpenAI's GPT-4o multimodal model to generate text, sound, and images using GPT-4o-mini, DALL-E, and GPT-4o-realtime. The workshop also covers creating AI Agents to enhance user experiences and drive innovation. It includes instructions, resources for continued learning, and information on responsible AI practices.

Metaflow is a user-friendly library designed to assist scientists and engineers in developing and managing real-world data science projects. Initially created at Netflix, Metaflow aimed to enhance the productivity of data scientists working on diverse projects ranging from traditional statistics to cutting-edge deep learning. For further information, refer to Metaflow's website and documentation.

AI Engineer Headquarters is a comprehensive learning resource designed to help individuals master scientific methods, processes, algorithms, and systems to build stories and models in the field of Data and AI. The repository provides in-depth content through video sessions and text materials, catering to individuals aspiring to be in the top 1% of Data and AI experts. It covers various topics such as AI engineering foundations, large language models, retrieval-augmented generation, fine-tuning LLMs, reinforcement learning, ethical AI, agentic workflows, and career acceleration. The learning approach emphasizes action-oriented drills and routines, encouraging consistent effort and dedication to excel in the AI field.

VectorHub is a free and open-sourced learning hub for people interested in adding vector retrieval to their ML stack. On VectorHub you will find practical resources to help you create MVPs with easy-to-follow learning materials, solve use case specific challenges in vector retrieval, get confident in taking their MVPs to production and making them actually useful, and learn about vendors in the space and select the ones that fit their use-case.

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

The 'jobs' repository by comma.ai focuses on solving self-driving cars by building a robotics stack that includes state-of-the-art machine learning models, operating system design, hardware development, and manufacturing. The company aims to deliver constant incremental progress in self-driving technology to users, with a focus on practical solutions rather than hype. Job opportunities at comma.ai include technical challenges, phone screenings, and paid micro-internships, with perks such as chef-prepared meals, on-site gym access, and health insurance. The teams at comma.ai are organized into web, systems, infrastructure, product, design, and electrical engineering, with specific challenges for each team. The repository also offers opportunities for non-job seekers to participate in challenges and win prizes.

A drill of scientific methods, processes, algorithms, and systems to build stories & models. An in-depth learning resource for humans. This is a drill for people who aim to be in the top 1% of Data and AI experts. The repository provides a routine for deep and shallow work sessions, covering topics from Python to AI/ML System Design and Personal Branding & Portfolio. It emphasizes the importance of continuous effort and action in the tech field.

AI Summer is a repository focused on providing workshops and resources for developing foundational skills in generative AI models and transformer models. The repository offers practical applications for inferencing and training, with a specific emphasis on understanding and utilizing advanced AI chat models like BingGPT. Participants are encouraged to engage in interactive programming environments, decide on projects to work on, and actively participate in discussions and breakout rooms. The workshops cover topics such as generative AI models, retrieval-augmented generation, building AI solutions, and fine-tuning models. The goal is to equip individuals with the necessary skills to work with AI technologies effectively and securely, both locally and in the cloud.

This code repository is for the book 'Mastering NLP from Foundations to LLMs', which provides an in-depth introduction to Natural Language Processing (NLP) techniques. It covers mathematical foundations of machine learning, advanced NLP applications such as large language models (LLMs) and AI applications, as well as practical skills for working on real-world NLP business problems. The book includes Python code samples and expert insights into current and future trends in NLP.

LangGraph-learn is a community-driven project focused on mastering LangGraph and other AI-related topics. It provides hands-on examples and resources to help users learn how to create and manage language model workflows using LangGraph and related tools. The project aims to foster a collaborative learning environment for individuals interested in AI and machine learning by offering practical examples and tutorials on building efficient and reusable workflows involving language models.

The End-to-End LLM Bootcamp is a comprehensive training program that covers the entire process of developing and deploying large language models. Participants learn to preprocess datasets, train models, optimize performance using NVIDIA technologies, understand guardrail prompts, and deploy AI pipelines using Triton Inference Server. The bootcamp includes labs, challenges, and practical applications, with a total duration of approximately 7.5 hours. It is designed for individuals interested in working with advanced language models and AI technologies.

The Machine Learning for Public Policy Lab is a project-based course focused on solving real-world problems using machine learning in the context of public policy and social good. Students will gain hands-on experience building end-to-end machine learning systems, developing skills in problem formulation, working with messy data, communicating with non-technical stakeholders, model interpretability, and understanding algorithmic bias & disparities. The course covers topics such as project scoping, data acquisition, feature engineering, model evaluation, bias and fairness, and model interpretability. Students will work in small groups on policy projects, with graded components including project proposals, presentations, and final reports.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

Ray is a unified framework for scaling AI and Python applications. It consists of a core distributed runtime and a set of AI libraries for simplifying ML compute, including Data, Train, Tune, RLlib, and Serve. Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations. With Ray, you can seamlessly scale the same code from a laptop to a cluster, making it easy to meet the compute-intensive demands of modern ML workloads.

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. It is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and allows users to integrate machine learning and deep learning models with their Java applications. The framework is deep learning engine agnostic, enabling users to switch engines at any point for optimal performance. DJL's ergonomic API interface guides users with best practices to accomplish deep learning tasks, such as running inference and training neural networks.

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). MLflow's current components are: * `MLflow Tracking `_: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI. * `MLflow Projects `_: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others. * `MLflow Models `_: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker. * `MLflow Model Registry `_: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.

TT-NN is a python & C++ Neural Network OP library. It provides a low-level programming model, TT-Metalium, enabling kernel development for Tenstorrent hardware.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

This repository contains reference architectures and test cases for distributed model training with Amazon SageMaker Hyperpod, AWS ParallelCluster, AWS Batch, and Amazon EKS. The test cases cover different types and sizes of models as well as different frameworks and parallel optimizations (Pytorch DDP/FSDP, MegatronLM, NemoMegatron...).

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

This repository provides code samples in Python, C#, REST, and JavaScript for vector support in Azure AI Search. It includes demos for various languages showcasing vectorization of data, creating indexes, and querying vector data. Additionally, it offers tools like Azure AI Search Lab for experimenting with AI-enabled search scenarios in Azure and templates for deploying custom chat-with-your-data solutions. The repository also features documentation on vector search, hybrid search, creating and querying vector indexes, and REST API references for Azure AI Search and Azure OpenAI Service.

The Intel® Geti™ SDK is a python package that enables teams to rapidly develop AI models by easing the complexities of model development and enhancing collaboration between teams. It provides tools to interact with an Intel® Geti™ server via the REST API, allowing for project creation, downloading, uploading, deploying for local inference with OpenVINO, setting project and model configuration, launching and monitoring training jobs, and media upload and prediction. The SDK also includes tutorial-style Jupyter notebooks demonstrating its usage.

Booster is a powerful inference accelerator designed for scaling large language models within production environments or for experimental purposes. It is built with performance and scaling in mind, supporting various CPUs and GPUs, including Nvidia CUDA, Apple Metal, and OpenCL cards. The tool can split large models across multiple GPUs, offering fast inference on machines with beefy GPUs. It supports both regular FP16/FP32 models and quantised versions, along with popular LLM architectures. Additionally, Booster features proprietary Janus Sampling for code generation and non-English languages.

xFasterTransformer is an optimized solution for Large Language Models (LLMs) on the X86 platform, providing high performance and scalability for inference on mainstream LLM models. It offers C++ and Python APIs for easy integration, along with example codes and benchmark scripts. Users can prepare models in a different format, convert them, and use the APIs for tasks like encoding input prompts, generating token ids, and serving inference requests. The tool supports various data types and models, and can run in single or multi-rank modes using MPI. A web demo based on Gradio is available for popular LLM models like ChatGLM and Llama2. Benchmark scripts help evaluate model inference performance quickly, and MLServer enables serving with REST and gRPC interfaces.

The Amazon Transcribe Live Call Analytics (LCA) with Agent Assist Sample Solution is designed to help contact centers assess and optimize caller experiences in real time. It leverages Amazon machine learning services like Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker to transcribe and extract insights from contact center audio. The solution provides real-time supervisor and agent assist features, integrates with existing contact centers, and offers a scalable, cost-effective approach to improve customer interactions. The end-to-end architecture includes features like live call transcription, call summarization, AI-powered agent assistance, and real-time analytics. The solution is event-driven, ensuring low latency and seamless processing flow from ingested speech to live webpage updates.

This repository contains recipes for building and running containerized AI and LLM applications with Podman. It provides model servers that serve machine-learning models via an API, allowing developers to quickly prototype new AI applications locally. The recipes include components like model servers and AI applications for tasks such as chat, summarization, object detection, etc. Images for sample applications and models are available in `quay.io`, and bootable containers for AI training on Linux OS are enabled.

XLearning is a scheduling platform for big data and artificial intelligence, supporting various machine learning and deep learning frameworks. It runs on Hadoop Yarn and integrates frameworks like TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning offers scalability, compatibility, multiple deep learning framework support, unified data management based on HDFS, visualization display, and compatibility with code at native frameworks. It provides functions for data input/output strategies, container management, TensorBoard service, and resource usage metrics display. XLearning requires JDK >= 1.7 and Maven >= 3.3 for compilation, and deployment on CentOS 7.2 with Java >= 1.7 and Hadoop 2.6, 2.7, 2.8.