#AWS

Discover Jupyter notebooks demonstrating processes to build, train, and deploy models with Amazon SageMaker. These cover tasks like data preparation, model construction, and deployment, plus advanced features such as MLOps and generative AI. Suitable for ML experts aiming to utilize SageMaker's services, these resources include detailed documentation and code samples for easy integration into diverse workflows.

Raster Vision is an open-source Python framework that facilitates creating computer vision models for satellite and aerial imagery. It supports functions like chip classification, object detection, and semantic segmentation and provides cloud execution using AWS Batch and Sagemaker. This tool offers a low-code solution for comprehensive geospatial deep learning workflows, including data processing, model training, and result generation. It fits both novices and experts, offering installation via pip or Docker for flexible application.

Explore Sensei Search, an AI-driven answer engine that employs open source large language models. Built with technologies like FastAPI, OpenAI client, and Next.js, it delivers strong frontend and backend systems. The platform enables both local and cloud deployment on AWS for scalable solutions, offering in-depth insights into LLM usage through community contributions. An optimal choice for developers aiming to seamlessly integrate AI-based search capabilities.

Discover a curated list of HashiCorp's Terraform resources, helping users effectively manage production infrastructure. This platform offers official resources, community modules, guides, and supports cloud services like AWS, Azure, and Google Cloud, catering to both beginners and experts in dynamic environments.

Pulumi provides a comprehensive Infrastructure as Code (IaC) solution that supports efficient cloud infrastructure deployment across AWS, Azure, Google Cloud, and Kubernetes, with compatibility for over 120 providers. It enhances developer productivity by allowing the use of loops, functions, and classes without complex YAML setups, while its Automation API facilitates easy integration of IaC practices. As an open-source platform, Pulumi offers extensive language support, making it adaptable to various cloud infrastructure requirements.

Explore comprehensive AI/ML techniques with hands-on exercises, including real-time analytics and anomaly detection using Amazon SageMaker. This guide focuses on natural language processing with real-world examples, providing detailed instructions on creating a BERT-based text classifier to predict product review ratings. Users will gain practical experience with model training, deployment, and advanced techniques like hyper-parameter tuning, A/B testing, and auto-scaling. The book also covers crucial topics like data ingestion, bias detection, feature engineering, and setting up streaming analytics applications, positioning it as an invaluable resource for data scientists seeking practical insights.

Terraformer is a CLI tool that reverse engineers existing infrastructures into Terraform and JSON files. It supports providers like AWS, Google Cloud, and Azure, allowing users to manage and upgrade infrastructure effectively using provider updates. Key features include resource importing, service and ID filtering, and customizable file structures.

This repository offers resources and configuration scripts to build custom Amazon EKS AMIs using HashiCorp Packer, as used by Amazon EKS for official AMIs. It includes a Makefile for easy integration with specific Kubernetes versions and OS distributions. New users are guided through AWS documentation to efficiently begin with Amazon EKS and launch node groups. The repository emphasizes security by directing issue reports to AWS Security and is available under the MIT-0 license with specific provisions for NVIDIA and Neuron accelerated AMIs.

Explore the profile of a Technology Architect with expertise in cloud services like AWS, GCP, and Azure, as well as Big Data systems. Vaquar Khan brings experience in cloud architecture and polyglot programming, including Java, Python, and Scala. The site provides insights on distributed systems and microservices, and highlights his contributions on platforms like GitHub and StackOverflow for technical knowledge and collaboration.

Explore how the Data Solutions Framework on AWS facilitates swift data platform deployment with optimized defaults and adherence to AWS best practices. Available in TypeScript and Python, it simplifies the development process through Infrastructure as Code, enabling rapid and tailored data solutions without the need for starting from zero. Comprehensive documentation and examples ensure a smooth learning curve.

Webiny is a powerful open-source serverless CMS tailored for enterprise environments. It features a Page Builder, a Headless CMS with GraphQL API, a File Manager, and a Form Builder. With AWS-based deployment, it focuses on scalability, cost-efficiency, and security. It integrates seamlessly with providers such as OKTA and Cognito, and supports multi-tenancy. Its flexibility makes it suitable for various applications from marketing sites to serverless applications. Webiny's API capabilities and modular design ensure it keeps pace with technological advancements like future multi-cloud support.

SageMaker Python SDK allows for the training and deployment of machine learning models on Amazon SageMaker, with support for popular frameworks such as Apache MXNet and TensorFlow, in addition to Amazon's scalable, GPU-optimized algorithms. It also supports the use of custom algorithms in Docker containers compatible with SageMaker. The SDK includes detailed documentation and examples for various machine learning applications, including model tuning, batch transformations, and training security with VPC. Suitable for Unix/Linux and Mac operating systems and equipped with comprehensive testing and telemetry, it is licensed under Apache 2.0, aiming to simplify machine learning implementations.