Alternatives to UnionML

Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility.

Union.ai Benefits: - Accelerated Data Processing & ML: Union.ai significantly speeds up data processing and machine learning. - Built on Trusted Open-Source: Leverages the robust open-source project Flyte™, ensuring a reliable and tested foundation for your ML projects. - Kubernetes Efficiency: Harnesses the power and efficiency of Kubernetes along with enhanced observability and enterprise features. - Optimized Infrastructure: Facilitates easier collaboration among Data and ML teams on optimized infrastructures, boosting project velocity. - Breaks Down Silos: Tackles the challenges of distributed tooling and infrastructure by simplifying work-sharing across teams and environments with reusable tasks, versioned workflows, and an extensible plugin system. - Seamless Multi-Cloud Operations: Navigate the complexities of on-prem, hybrid, or multi-cloud setups with ease, ensuring consistent data handling, secure networking, and smooth service integrations. - Cost Optimization: Keeps a tight rein on your compute costs, tracks usage, and optimizes resource allocation even across distributed providers and instances, ensuring cost-effectiveness.

Core ML creates a model by applying a machine-learning algorithm to a collection of training data. A model is used to make predictions using new input data. Models can perform a variety of tasks which would be difficult to code or impractical. You can train a model, for example, to categorize images or detect specific objects in a photo based on its pixels. After creating the model, you can integrate it into your app and deploy on the device of the user. Your app uses Core ML and user data to make forecasts and train or fine-tune a model. Create ML, which is bundled with Xcode, allows you to build and train a ML model. Create ML models are Core ML formatted and ready to be used in your app. Core ML Tools can be used to convert models from other machine learning libraries into Core ML format. Core ML can be used to retrain a model on the device of a user.

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

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.

Apache Mahout is an incredibly powerful, scalable and versatile machine-learning library that was designed for distributed data processing. It provides a set of algorithms that can be used for a variety of tasks, such as classification, clustering and recommendation. Mahout is built on top of Apache Hadoop and uses MapReduce and Spark for data processing. Apache Mahout(TM), a distributed linear-algebra framework, is a mathematically expressive Scala DSL that allows mathematicians to quickly implement their algorithms. Apache Spark is recommended as the default distributed back-end, but can be extended to work with other distributed backends. Matrix computations play a key role in many scientific and engineering applications such as machine learning, data analysis, and computer vision. Apache Mahout is designed for large-scale data processing, leveraging Hadoop and Spark.

Scikit-learn offers simple and efficient tools to analyze predictive data. Scikit-learn, an open source machine learning toolkit for Python, is designed to provide efficient and simple tools for data modeling and analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, built on popular scientific libraries such as NumPy SciPy and Matplotlib. It offers a range of supervised learning algorithms and unsupervised learning methods, making it a valuable toolkit for researchers, data scientists and machine learning engineers. The library is organized in a consistent, flexible framework where different components can be combined to meet specific needs. This modularity allows users to easily build complex pipelines, automate tedious tasks, and integrate Scikit-learn in larger machine-learning workflows. The library's focus on interoperability also ensures that it integrates seamlessly with other Python libraries to facilitate smooth data processing.

Helidon is an open-source, cloud-native Java library set for writing microservices. It runs on Netty's fast web core. Helidon Nima is the original Java microservices framework that uses virtual threads. Helidon is easy to use and includes examples and tooling to help you get started quickly. Helidon is a collection Java libraries that runs on a fast Netty Core. There is no additional overhead or bloat. Helidon supports MicroProfile, provides familiar APIs such as JAX-RS and CDI, and JSON/P/B. Our implementation runs on the fast Helidon Reactive WebServer. The Helidon Reactive WebServer is a modern functional programming framework that runs on top Netty. The Helidon WebServer is lightweight, flexible, reactive, and provides a fast and easy foundation for microservices. Helidon supports health checks, metrics and fault tolerance. This allows you to create cloud-ready applications that integrate seamlessly with Prometheus, Jaeger/Zipkin and other systems.

Simplify your MLOps pipelines. ZenML allows you to manage, deploy and scale any infrastructure. ZenML is open-source and free. Two simple commands will show you the magic. ZenML can be set up in minutes and you can use all your existing tools. ZenML interfaces ensure your tools work seamlessly together. Scale up your MLOps stack gradually by changing components when your training or deployment needs change. Keep up to date with the latest developments in the MLOps industry and integrate them easily. Define simple, clear ML workflows and save time by avoiding boilerplate code or infrastructure tooling. Write portable ML codes and switch from experiments to production in seconds. ZenML's plug and play integrations allow you to manage all your favorite MLOps software in one place. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code.

Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported.

Built on top of standard HTML, CSS, and JavaScript with intuitive APIs and world-class documentation. Reactive, compiler-optimized rendering system that rarely needs manual optimization. An ecosystem rich in features that can be easily adopted incrementally. It can scale between a library or a fully-featured framework. Vue is a JavaScript framework that allows you to build user interfaces. It is built on top of JavaScript, CSS, and standard HTML and offers a declarative and component-based programming structure that allows you to efficiently create user interfaces, no matter how simple or complex. Vue extends the standard HTML with a template syntax, which allows us to describe HTML output using JavaScript state. Vue tracks JavaScript state changes automatically and updates the DOM as soon as they occur. Vue is a framework that provides most of the features required for frontend development.

A full-stack PHP Framework delivered as a C extension. Phalcon is the fastest PHP framework built. Phalcon does not require developers to be C-literate. Its functionality can be accessed as PHP classes and PHP interfaces in the Phalcon namespace. Zephir/C is loaded along with PHP once on the daemon startup process of the web server. The extension provides classes and functions that are ready for use in any application. The code is not interpreted, but compiled because it is already compiled for a specific processor and platform. Phalcon's low-level architecture and optimizations provide the lowest overhead possible for MVC-based apps. Create single- and multimodule applications with ease. You can use the file structure, scheme and patterns that you are already familiar with. It's never been easier to write REST servers and apps, with no boilerplate and simple services that can fit in one single file.

Growler is a web-framework built on top of asyncio. This asynchronous library was described in PEP 3156 and added to python's standard library with version 3.4. It takes its cues from the Connect & express frameworks within the nodejs eco-system, which uses a single application object with a series of middleware for processing HTTP requests. The custom middleware chain is a simple way to implement complex apps. The pip utility lets packages specify optional requirements so that features can be installed only on request. This fits well with the minimal approach of the Growler project. Don't install anything that the user doesn’t need. There are (will) be community packages that have been approved by the Growler developers. (After ensuring that they work as expected and have been well tested with each growler version) These will be available directly from the Growler package.

Bottle is a lightweight, fast and simple WSGI micro-web framework for Python. It is distributed in a single module file and does not have any dependencies except for the Python Standard Library. Support for clean and dynamic URLs. Requests to function call mapping. Built-in template engine that is fast and pythonic, and supports mako2, jinja2 or cheetah. Access to form data, file uploads cookies, headers, and other HTTP metadata. Built-in HTTP Development Server and support for paste bjoern gae cherrypy or other WSGI capable HTTP servers.

CodeIgniter is an Application Development Framework, a toolkit for those who use PHP to build websites. It aims to make it easier to create projects faster than if you were to write code from scratch. CodeIgniter allows you to focus creatively on your project by minimizing the code required for a given task. CodeIgniter is as flexible as possible. This allows you to work as you wish and not be forced to. You can easily extend or replace core components of the framework to make it work the way that you want. CodeIgniter, in short, is a flexible framework that provides the tools you need and keeps you out of the way.

Getcss is an intuitive CSS framework. Create responsive web applications with getcss. It's easy to use, open source, and free. Features: * Accelerate your development - Write less, get more * Zero dependencies * Latest technologies - Supports latest browsers, HTML5, CSS3 * Easy to learn, easy to use - as easy as 1-2-3 * Free and Open Source - Develop for yourself or your client * Responsive User Interface - Developed with mobile first approach * Media queries and Flexbox based.

Koin simplifies DI for developers and empowers them in the Kotlin environment. Koin allows you to build any type of Kotlin and Kotlin Multiplatform application from Android mobile applications to backend Ktor Server applications. Koin is designed for the most demanding applications and is trusted by developers around the world. Koin is a dependency injection framework that was designed specifically for Kotlin developers. Koin's lightweight container and intuitive DSL simplify dependency injection in any Kotlin SDK or application. Koin framework allows you to build dependency injection with ease, using simple APIs and Kotlin DSL. Koin helps you build complex Android mobile apps so you can concentrate on your business and not your tools. Koin connects to the Kotlin eco-system. Koin injects dependencies across multiple platforms using the power of Kotlin. Koin, which is already used by thousands of companies around the world, is ready for production.

Streamlit. The fastest way to create and share data apps. In minutes, turn data scripts into sharable Web apps All in Python. All this for free. No need for front-end experience. Streamlit combines three simple concepts. Use Python scripting. Our API is simple and allows you to create an app in just a few lines of code. You can then see the app update automatically as you save your source file. You can also use interaction. Declaring a variable is the same thing as adding a widget. You don't need to create a backend, define routes or handle HTTP requests. You can deploy your app instantly. Streamlit's platform for sharing allows you to easily share, manage and collaborate on your apps. A framework that allows you to create powerful apps. Face-GAN explorer. App that generates faces matching selected attributes using Shaobo Guan’s TL-GAN project, TensorFlow and NVIDIA’s PG-GAN. Real time object detection. A browser that displays images from the Udacity self driving-car dataset.

Dropwizard, a Java framework, is for creating ops-friendly, high performance, RESTful web service. Dropwizard combines stable, mature Java libraries into a lightweight package that allows you to focus on what's important. Dropwizard supports complex configuration, application metrics, logging, operational tools, and many other features. This allows you and your team the ability to deliver a high-quality web service in a short time. Dropwizard is both a library and framework. Dropwizard's goal is to provide reliable, performant implementations of all the necessary components for a production-ready web app. This functionality is embedded into a reusable library to keep your application focused and lean, which reduces both time-to market and maintenance burdens. Dropwizard embeds a highly tuned HTTP server directly in your project because you can't have a web app without HTTP.

Aurelia's unobtrusive, standards-based style makes it the only framework that allows you to create components using vanilla JavaScript and TypeScript. There is little to no additional information required to create complex apps. Aurelia's core is a reactive, high-performance system that can batch DOM updates. This makes it stand out from other frameworks and their virtual DOMs. No matter how complex your interface is, you will experience consistent and scalable performance. Aurelia allows you to easily react to any object. Aurelia uses adaptive techniques to determine the most efficient way for you to observe each property of your model. It automatically syncs your state and your UI with best-in class performance. All official plugins of the core team for state management, internationalization, and validation. CLI, VS Code plugin and Chrome debugger – optional tools to improve development.

Open-Source AI layer for databases. Machine Learning capabilities can be integrated directly into your data domain to increase efficiency and productivity. MindsDB makes it easy to create, train, and then test ML models. Then publish them as virtual AI tables into databases. Integrate seamlessly with all major databases. SQL queries can be used to manipulate ML models. You can increase model training speed using GPU without affecting the performance of your database. Learn how the ML model arrived at its conclusions and what factors affect prediction confidence. Visual tools that allow you to analyze model performance. SQL and Python queries that return explanation insights in a single code. You can use What-if analysis to determine confidence based upon different inputs. Automate the process for applying machine learning using the state-of the-art Lightwood AutoML library. Machine Learning can be used to create custom solutions in your preferred programming language.

The workflow automation platform that automates complex, mission-critical data processing and ML processes at large scale. Flyte makes it simple to create machine learning and data processing workflows that are concurrent, scalable, and manageable. Flyte is used for production at Lyft and Spotify, as well as Freenome. Flyte is used at Lyft for production model training and data processing. It has become the de facto platform for pricing, locations, ETA and mapping, as well as autonomous teams. Flyte manages more than 10,000 workflows at Lyft. This includes over 1,000,000 executions per month, 20,000,000 tasks, and 40,000,000 containers. Flyte has been battle-tested by Lyft and Spotify, as well as Freenome. It is completely open-source and has an Apache 2.0 license under Linux Foundation. There is also a cross-industry oversight committee. YAML is a useful tool for configuring machine learning and data workflows. However, it can be complicated and potentially error-prone.

Amazon SageMaker Canvas provides business analysts with a visual interface to help them generate accurate ML predictions. They don't need any ML experience nor to write a single line code. A visual interface that allows users to connect, prepare, analyze and explore data in order to build ML models and generate accurate predictions. Automate the creation of ML models in just a few clicks. By sharing, reviewing, updating, and revising ML models across tools, you can increase collaboration between data scientists and business analysts. Import ML models anywhere and instantly generate predictions in Amazon SageMaker Canvas. Amazon SageMaker Canvas allows you to import data from different sources, select the values you wish to predict, prepare and explore data, then quickly and easily build ML models. The model can then be analyzed and used to make accurate predictions.

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

Machine learning models can be deployed at scale with greater accuracy. With more models in production, R&D can be turned into ROI. Seldon reduces time to value so models can get to work quicker. Scale with confidence and minimize risks through transparent model performance and interpretable results. Seldon Deploy cuts down on time to production by providing production-grade inference servers that are optimized for the popular ML framework and custom language wrappers to suit your use cases. Seldon Core Enterprise offers enterprise-level support and access to trusted, global-tested MLOps software. Seldon Core Enterprise is designed for organizations that require: - Coverage for any number of ML models, plus unlimited users Additional assurances for models involved in staging and production - You can be confident that their ML model deployments will be supported and protected.

SvelteKit allows you to quickly develop robust and performant web applications by using Svelte. It provides solutions for routing, data fetching and service workers. TypeScript integration is also supported. SvelteKit applications are server-rendered as a default, providing excellent performance on first load and SEO benefits. However, they can be switched to client-side navigation for a better user experience. The framework is designed for developers to grow along with them, allowing them start simple and then add new features when needed. SvelteKit uses Vite to provide a feature-rich and fast development experience. This includes hot module replacement. Svelte is an easy way to create user interface components like a navigational bar, comment section or contact form that users can see and interact with. The Svelte compiler will convert your components into JavaScript, which can be used to render HTML and CSS for the page.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

Flask is a lightweight WSGI framework for web applications. It's easy to use and can scale up to complex applications. It started as a wrapper for Jinja and Werkzeug, and has since grown to be one of the most popular Python web app frameworks. Flask is a suggestion tool that doesn't require any dependencies. The developer can choose the tools and libraries that they prefer. The community has many extensions that make it easy to add new functionality.

CherryPy lets developers build web applications the same way as they would any other object-oriented Python programme. This allows for smaller source code to be developed in less time. CherryPy has been around for more than ten year and has proven to be reliable and fast. Many sites, from the most basic to the most demanding, use it in production. To get the most out of CherryPy you should begin with the tutorials which will guide you through the most important aspects of the framework. After you have completed the tutorials, you may want to look at the advanced and basic sections to see how certain operations are implemented. You will also want to read the configuration and extension sections, which go into detail about the powerful features of the framework.

Django is a high level Python web framework that encourages quick development and pragmatic design. It was developed by experienced web developers and allows you to focus on writing your application without having to reinvent the wheel. Django was created to make it easy for developers to take their applications from conception to completion as quickly and efficiently as possible. Django is committed to security and helps developers avoid common security errors. Django's ability scale quickly and flexiblely is a key feature of some of the most popular web sites. Django comes with a variety of additional tools that can be used to perform common web development tasks. Django handles user authentication, content administration and site maps. It also supports RSS feeds and many other tasks.

Laminas, the enterprise-ready PHP Framework and Components, is a framework and components that are ready for business. Components include dependency injection, input validation, input filtering, pagination, navigation, feed generation, and more. Build PHP middleware apps using the PSR-7/PSR-15 specifications. Use a variety routing and template options and the dependency container of your choice. Build RESTful APIs with standards, and get validation, authentication and authorization right out of box. PHP 7 ready, with a proven track record of running high-usage and business-critical applications. Laminas was built using security best practices. Store passwords, and encrypt them with AES-256. Laminas is up to four times faster than previous versions. Laminas API Tools provide JSON representations which can be parsed in any mobile framework.

Pop PHP is a PHP object-oriented framework that was first released in 2009 as a small component library. Pop's main goal is to provide lightweight tools that can help developers develop applications quickly. Pop is designed to be easy to learn and help developers quickly implement the features that they need in their applications. The Pop PHP Framework is a collection of core components, plus an additional 30+ components. It's a toolkit that helps developers create applications quickly. The framework has a set of core components and a number of additional components that facilitate many of the features common to a PHP application. The Pop PHP Framework is designed to be easy to install and use by anyone, from a novice programmer to an experienced developer.

It is not easy to develop RESTful web service that support exposing data in a variety representation media types, and abstract away the low level details of client-server communications without a good toolkit. A standard and portable JAX RS API was designed to simplify the development and deployment of RESTful web service clients and services in Java. Jersey RESTful Web Services 3. x Framework is an open source, production-quality framework that supports Jakarta RESTful Web Services 3.0. Jersey framework is much more than JAX-RS's reference implementation. Jersey's API extends JAXRS with new features. GlassFish will continue to track the JAX-RS API, and release regular releases of production-quality reference implementations. APIs are provided to extend Jersey and build a community. It makes it easy to create RESTful Web Services using Java and the Java Virtual Machine.

Svelte is a revolutionary new way to build user interfaces. Traditional frameworks like Vue and React do most of their work in browsers. Svelte moves that work to a compile step when you build your app. Svelte uses techniques such as virtual DOM diffing to update the DOM whenever your app's state changes. Svelte was recently voted as the most popular web framework by the most satisfied developers in two industry surveys. We are sure you will love it. For more information, read the blog post introducing Svelte. Svelte is a tool to build fast web applications. It is similar to JavaScript frameworks like Vue and React, which share the goal of making it easy for users to create intuitive user interfaces. Svelte converts your application into JavaScript at build time. It does not interpret your application code at runtime.

Yii is an efficient, fast, and secure PHP framework. Flexible and pragmatic, Yii works right out of box, with reasonable defaults. Yii can virtually eliminate repetitive coding tasks. However, the real creative work is yours. Often, this begins with the design of the entire system in terms a database schema. Migrations are the best way to achieve this. Yii provides maximum functionality with the least amount of overhead. Sane defaults, built-in tools and a solid base of features help you create secure and reliable code. Simple APIs and code generators help you write more code in less than half the time. Yii is a PHP-based web programming framework that can be used to develop all types of web applications. It is particularly suitable for large-scale applications, such as portals and forums, content-management systems (CMS), RESTful services and ecommerce. This is because of its component architecture and sophisticated caching.

jQuery Mobile, an HTML5-based user interface, is designed to create responsive websites and apps that can be accessed on all smartphones, tablets, as well as desktop devices. The jQuery Mobile framework takes the concept of "write less, make more" to the next level. Instead of creating unique apps for each mobile device or operating system, the jQuery framework allows you to create a single, highly-branded responsive website that will work on every smartphone, tablet, or desktop platform. Your website or app should reflect your brand, not an OS. ThemeRoller mobile is a tool that makes it easy to create highly customized themes. It allows you to drag and drop colors, and even download a custom theme. CSS3 properties are a great way to create polished visuals that don't include bloat. Our tool can be used to create a custom bundle with only the components that you require.

Keepsake, an open-source Python tool, is designed to provide versioning for machine learning models and experiments. It allows users to track code, hyperparameters and training data. It also tracks metrics and Python dependencies. Keepsake integrates seamlessly into existing workflows. It requires minimal code additions and allows users to continue training while Keepsake stores code and weights in Amazon S3 or Google Cloud Storage. This allows for the retrieval and deployment of code or weights at any checkpoint. Keepsake is compatible with a variety of machine learning frameworks including TensorFlow and PyTorch. It also supports scikit-learn and XGBoost. It also has features like experiment comparison that allow users to compare parameters, metrics and dependencies between experiments.

Free. Cross-platform. Open source. Open source platform for developing all your apps. You can create native apps for Android and iOS from one code base. Your.NET apps can be written in C# or F#, as well as Visual Basic. You can use your skills, code, favorite libraries, and code wherever you use.NET. These videos will show you more about.NET. .NET is open-source and we are grateful for all the contributions from the community.

You can handle more requests with less resources than traditional stacks or frameworks that rely on blocking I/O. Vert.x works well in all types of execution environments, even constrained ones like containers and virtual machines. People have told you that asynchronous programming is too difficult. Vert.x programming is easy to use, and we don't believe in sacrificing accuracy or performance. You can save money and reduce waste by not wasting resources. Pick the model that best suits your task, including callbacks, promises and futures, reactive extensions, and (Kotlin), coroutines. Vert.x is not a framework but a toolkit. It is easily reusable and embeddable. We don't have a strong opinion about how your application structure should look. As you create your application, choose the modules and clients that you require and then compose them.

Speed up your development process. F3 is fast, lightweight, and easy to use. It doesn't get you in the way. It has a gradual learning curve, so you can save money on training costs. F3 is a great tool for beginners and experts alike. F3 does not require complex configuration or unnecessary installation procedures. A powerful, yet easy-to use PHP micro-framework that helps you build dynamic and robust websites. Easy to learn, full-featured toolkit with a code base of just 90kb. Take advantage of built-in features. Create apps that rock. F3 provides a solid base, mature code, and a no nonsense approach to developing web applications. The web development toolkit is easy to use, has a high-performance URL router, a multiprotocol caching engine, code highlighting and supports multilingual i18n apps.

Machine Learning made simple for everyone The leading Machine Learning platform will take your business to the next level. Get data-driven decisions now! No more cumbersome or expensive solutions. Machine Learning that works. BigML offers a variety of Machine Learning algorithms that are robustly engineered and can be applied across your company to solve real-world problems. You can avoid dependencies on multiple libraries that will increase complexity, maintenance costs, or technical debt in your projects. BigML allows unlimited predictive applications in all industries, including aerospace, automotive and energy, entertainment, financial, financial services, food and healthcare, IoT pharmaceutical, transportation, telecommunications and many more. Supervised Learning: Classification and regression (trees and ensembles, logistic regressions and deepnets), as well as time series forecasting.

Free. Cross-platform. Open source. A platform to build Android and iOS apps using.NET or C#. You can create native apps for billions of Android, iPhones, iPads, Macs, and Windows devices using.NET or C#. You can also share code with any.NET application, enabling you to be more productive and create apps faster across all platforms. Are you ready to take code sharing to the next step? Xamarin.Forms, an open-source mobile UI framework by Microsoft, allows you to build iOS, Android, and Windows apps with.NET using a single codebase. Xamarin.Forms can be used to create a consistent look across all platforms. .NET is a platform for developers that includes tools, programming languages and libraries to build many types of applications. Xamarin is an extension to the.NET platform that provides tools and libraries for building apps for Android and iOS, tvOS and watchOS, macOS and Windows.

A simple-to-use interactive tool that allows data exploration, analysis, visualization and machine learning. Cloud Datalab is an interactive tool that allows you to analyze, transform, visualize, and create machine learning models on Google Cloud Platform. It runs on Compute Engine. It connects to multiple cloud services quickly so you can concentrate on data science tasks. Cloud Datalab is built using Jupyter (formerly IPython), a platform that boasts a rich ecosystem of modules and a solid knowledge base. Cloud Datalab allows you to analyze your data on BigQuery and AI Platform, Compute Engine and Cloud Storage using Python and SQL. JavaScript is also available (for BigQuery user defined functions). Cloud Datalab can handle megabytes and terabytes of data. Cloud Datalab allows you to query terabytes and run local analysis on samples of data, as well as run training jobs on terabytes in AI Platform.

HPE Ezmeral ML Ops offers pre-packaged tools that enable you to operate machine learning workflows at any stage of the ML lifecycle. This will give you DevOps-like speed, agility, and speed. You can quickly set up environments using your preferred data science tools. This allows you to explore multiple enterprise data sources, and simultaneously experiment with multiple deep learning frameworks or machine learning models to find the best model for the business problems. On-demand, self-service environments that can be used for testing and development as well as production workloads. Highly performant training environments with separation of compute/storage that securely access shared enterprise data sources in cloud-based or on-premises storage.

The Xilinx AI development platform for AI Inference on Xilinx hardware platforms consists optimized IP, tools and libraries, models, examples, and models. It was designed to be efficient and easy-to-use, allowing AI acceleration on Xilinx FPGA or ACAP. Supports mainstream frameworks as well as the most recent models that can perform diverse deep learning tasks. A comprehensive collection of pre-optimized models is available for deployment on Xilinx devices. Find the closest model to your application and begin retraining! This powerful open-source quantizer supports model calibration, quantization, and fine tuning. The AI profiler allows you to analyze layers in order to identify bottlenecks. The AI library provides open-source high-level Python and C++ APIs that allow maximum portability from the edge to the cloud. You can customize the IP cores to meet your specific needs for many different applications.

Easy management, deployment and monitoring of machine learning models for supercharging MLOps. Available for all organizations on the best AI platform. You can use navio for various machine learning operations across your entire artificial intelligence landscape. Machine learning can be integrated into your business workflow to make a tangible, measurable impact on your business. navio offers various Machine Learning Operations (MLOps), which can be used to support you from the initial model development phase to the production run of your model. Automatically create REST endspoints and keep track the clients or machines that interact with your model. To get the best results, you should focus on exploring and training your models. You can also stop wasting time and resources setting up infrastructure. Let navio manage all aspects of product ionization so you can go live quickly with your machine-learning models.

Datatron provides tools and features that are built from scratch to help you make machine learning in production a reality. Many teams realize that there is more to deploying models than just the manual task. Datatron provides a single platform that manages all your ML, AI and Data Science models in production. We can help you automate, optimize and accelerate your ML model production to ensure they run smoothly and efficiently. Data Scientists can use a variety frameworks to create the best models. We support any framework you use to build a model (e.g. TensorFlow and H2O, Scikit-Learn and SAS are supported. Explore models that were created and uploaded by your data scientists, all from one central repository. In just a few clicks, you can create scalable model deployments. You can deploy models using any language or framework. Your model performance will help you make better decisions.

MLflow is an open-source platform that manages the ML lifecycle. It includes experimentation, reproducibility and deployment. There is also a central model registry. MLflow currently has four components. Record and query experiments: data, code, config, results. Data science code can be packaged in a format that can be reproduced on any platform. Machine learning models can be deployed in a variety of environments. A central repository can store, annotate and discover models, as well as manage them. The MLflow Tracking component provides an API and UI to log parameters, code versions and metrics. It can also be used to visualize the results later. MLflow Tracking allows you to log and query experiments using Python REST, R API, Java API APIs, and REST. An MLflow Project is a way to package data science code in a reusable, reproducible manner. It is based primarily upon conventions. The Projects component also includes an API and command line tools to run projects.

Our easy-to-use monitor builder allows you to create customized monitors for your machinelearning models. Get alerts for issues such as concept drift, model performance degradation and bias. Aporia can seamlessly integrate with any ML infrastructure. It doesn't matter if it's a FastAPI server built on top of Kubernetes or an open-source deployment tool such as MLFlow, or a machine-learning platform like AWS Sagemaker. Zoom in on specific data segments to track the model's behavior. Unexpected biases, underperformance, drifting characteristics, and data integrity issues can be identified. You need the right tools to quickly identify the root cause of problems in your ML models. Our investigation toolbox allows you to go deeper than model monitoring and take a deep look at model performance, data segments or distribution.

Tencent Cloud TI Platform, a machine learning platform for AI engineers, is a one stop shop. It supports AI development at every stage, from data preprocessing, to model building, to model training, to model evaluation, as well as model service. It is preconfigured with diverse algorithms components and supports multiple algorithm frameworks for adapting to different AI use-cases. Tencent Cloud TI Platform offers a machine learning experience in a single-stop shop. It covers a closed-loop workflow, from data preprocessing, to model building, training and evaluation. Tencent Cloud TI Platform allows even AI beginners to have their models automatically constructed, making the entire training process much easier. Tencent Cloud TI Platform’s auto-tuning feature can also improve the efficiency of parameter optimization. Tencent Cloud TI Platform enables CPU/GPU resources that can elastically respond with flexible billing methods to different computing power requirements.