Best Development Frameworks for Amazon Web Services (AWS)

Gatsby is a modern, open-source website framework that enables you to build performance into your site by leveraging the most recent web technologies like React and GraphQL. You don't need to be a performance expert to create lightning fast apps and websites. Preview is a private playground for content creators, designers, and developers. It allows you to view changes in context and shareable URLs. Automated Lighthouse performance checks are used to deploy previews and fix errors before they are published. Use Gatsby to build and deploy to your favorite CDN.

Atri Framework is a full stack web development framework for building Progressive Web Apps. Our visual editor will increase your productivity. You can also add custom React code. Currently, we only support Python for backend developers. NodeJS will be added soon. Our CLI offers rich support for deployment on your platform of preference such as GitHub pages, AWS, and more. Atri framework includes a set of productivity tools like visual editor, asset-management tools, etc. This reduces development time from months to just a few hours. Atri framework extends the definition of a full-stack app to include non-web developers in the creation and maintenance of the app.

Uber developed Horovod to make distributed deep-learning fast and easy to implement, reducing model training time from days and even weeks to minutes and hours. Horovod allows you to scale up an existing script so that it runs on hundreds of GPUs with just a few lines Python code. Horovod is available on-premises or as a cloud platform, including AWS Azure and Databricks. Horovod is also able to run on Apache Spark, allowing data processing and model-training to be combined into a single pipeline. Horovod can be configured to use the same infrastructure to train models using any framework. This makes it easy to switch from TensorFlow to PyTorch to MXNet and future frameworks, as machine learning tech stacks evolve.

This static site generator comes with batteries and is suitable for most uses. You can use it straight out of the box or add custom pipelines, data sources, layouts, and configurations. Statiq Web now supports generating.NET API documentation. This allows you to extend the functionality of a general-purpose static site generator. Statiq Docs and Statiq Web are the framework that allows you to easily create a custom static generator application for your specific needs. Statiq supports Markdown and Razor, along with plain HTML. More languages like Handlebars/Mustache or Liquid will be added soon. Statiq can read and interpret a variety data formats, including YAML, JSON and XML. It is designed to be able to integrate any data format into any use. You can choose the data format that you are most comfortable with, from data files to front matter.

Your application startup time is not limited by the size of your codebase. This results in a massive leap in startup times, lightning fast throughput, and minimal memory footprint. The framework caches reflection data and loads it for each bean in an application context when you build applications using reflection-based IoC frameworks. Cloud support is included, including cloud runtimes, distributed tracing, discovery services, and distributed tracing. You can quickly configure your favorite data-access layer, and use the APIs to create your own. You can quickly reap the benefits of familiar annotations. You can quickly spin up servers or clients in your unit testing and run them instantly. This API provides a simple, compile time, aspect-oriented programming API, which does not use reflection.

Creating ML applications should be easy and frictionless. UnionML is a Python framework that is built on Flyte™ and unifies the ecosystem of ML software into a single interface. Combine the tools you love with a simple, standard API. This allows you to stop writing boilerplate code and focus on the important things: the data and models that learn from it. Fit the rich ecosystems of tools and frameworks to a common protocol for Machine Learning. Implement endpoints using industry-standard machine-learning methods for fetching data and training models. Serve predictions (and more) in order to create a complete ML stack. UnionML apps can be used by data scientists, ML engineers, and MLOps professionals to define a single source for truth about the behavior of your ML system.