UnionML Alternatives

Union.ai is an award-winning, Flyte-based data and ML orchestrator for scalable, reproducible ML pipelines. With Union.ai, you can write your code locally and easily deploy pipelines to remote Kubernetes clusters. “Flyte’s scalability, data lineage, and caching capabilities enable us to train hundreds of models on petabytes of geospatial data, giving us an edge in our business.” — Arno, CTO at Blackshark.ai “With Flyte, we want to give the power back to biologists. We want to stand up something that they can play around with different parameters for their models because not every … parameter is fixed. We want to make sure we are giving them the power to run the analyses.” — Krishna Yeramsetty, Principal Data Scientist at Infinome “Flyte plays a vital role as a key component of Gojek's ML Platform by providing exactly that." — Pradithya Aria Pura, Principal Engineer at Goj

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost. Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps. SageMaker Studio gives you complete access, control, and visibility into each step required.

An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.

Core ML applies a machine learning algorithm to a set of training data to create a model. You use a model to make predictions based on new input data. Models can accomplish a wide variety of tasks that would be difficult or impractical to write in code. For example, you can train a model to categorize photos or detect specific objects within a photo directly from its pixels. After you create the model, integrate it in your app and deploy it on the user’s device. Your app uses Core ML APIs and user data to make predictions and to train or fine-tune the model. You can build and train a model with the Create ML app bundled with Xcode. Models trained using Create ML are in the Core ML model format and are ready to use in your app. Alternatively, you can use a wide variety of other machine learning libraries and then use Core ML Tools to convert the model into the Core ML format. Once a model is on a user’s device, you can use Core ML to retrain or fine-tune it on-device.

Apache Mahout is a powerful, scalable, and versatile machine learning library designed for distributed data processing. It offers a comprehensive set of algorithms for various tasks, including classification, clustering, recommendation, and pattern mining. Built on top of the Apache Hadoop ecosystem, Mahout leverages MapReduce and Spark to enable data processing on large-scale datasets. Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end or can be extended to other distributed backends. Matrix computations are a fundamental part of many scientific and engineering applications, including machine learning, computer vision, and data analysis. Apache Mahout is designed to handle large-scale data processing by leveraging the power of Hadoop and Spark.

Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve.

Helidon is a cloud-native, open‑source set of Java libraries for writing microservices that run on a fast web core powered by Netty. Helidon Níma is the first Java microservices framework based on virtual threads. Helidon is designed to be simple to use, with tooling and examples to get you going quickly. Since Helidon is simply a collection of Java libraries running on a fast Netty core, there is no extra overhead or bloat. Helidon supports MicroProfile and provides familiar APIs like JAX-RS, CDI, and JSON-P/B. Our implementation runs on our fast Helidon Reactive WebServer. Helidon Reactive WebServer provides a modern functional programming model and runs on top of Netty. Lightweight, flexible, and reactive, the Helidon WebServer provides a simple-to-use and fast foundation for your microservices. With support for health checks, metrics, tracing, and fault tolerance, Helidon has what you need to write cloud-ready applications that integrate with Prometheus, Jaeger/Zipkin, etc.

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

A full-stack PHP framework delivered as a C-extension. Its innovative architecture makes Phalcon the fastest PHP framework ever built. Developers do not need to know C to use Phalcon. Its functionality is exposed as PHP classes and interfaces under the Phalcon namespace, ready to be used. Zephir/C extensions are loaded together with PHP one time on the web server's daemon start process. Classes and functions provided by the extension are ready to use for any application. The code is compiled and isn't interpreted because it's already compiled to a specific platform and processor. Thanks to its low-level architecture and optimizations Phalcon provides the lowest overhead for MVC-based applications. Build single and multi-module applications with ease and pleasure. Using the file structure, scheme, and patterns you already know. Writing REST servers and applications has never been easier, with no boilerplate, and simple services that fit in one file.

Simplify your MLOps pipelines. Manage, deploy, and scale on any infrastructure with ZenML. ZenML is completely free and open-source. See the magic with just two simple commands. Set up ZenML in a matter of minutes, and start with all the tools you already use. ZenML standard interfaces ensure that your tools work together seamlessly. Gradually scale up your MLOps stack by switching out components whenever your training or deployment requirements change. Keep up with the latest changes in the MLOps world and easily integrate any new developments. Define simple and clear ML workflows without wasting time on boilerplate tooling or infrastructure code. Write portable ML code and switch from experimentation to production in seconds. Manage all your favorite MLOps tools in one place with ZenML's plug-and-play integrations. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code.

Accelerate the end-to-end machine learning lifecycle. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R.

Builds on top of standard HTML, CSS and JavaScript with intuitive API and world-class documentation. Truly reactive, compiler-optimized rendering system that rarely requires manual optimization. A rich, incrementally adoptable ecosystem that scales between a library and a full-featured framework. Vue is a JavaScript framework for building user interfaces. It builds on top of standard HTML, CSS and JavaScript, and provides a declarative and component-based programming model that helps you efficiently develop user interfaces, be it simple or complex. Vue extends standard HTML with a template syntax that allows us to declaratively describe HTML output based on JavaScript state. Vue automatically tracks JavaScript state changes and efficiently updates the DOM when changes happen. Vue is a framework and ecosystem that covers most of the common features needed in frontend development.

Bottle is a fast, simple and lightweight WSGI micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the Python Standard Library. Requests to function-call mapping with support for clean and dynamic URLs. Fast and pythonic built-in template engine and support for mako, jinja2 and cheetah templates. Convenient access to form data, file uploads, cookies, headers and other HTTP-related metadata. Built-in HTTP development server and support for paste, bjoern, gae, cherrypy or any other WSGI capable HTTP server.

Growler is a web framework built atop asyncio, the asynchronous library described in PEP 3156 and added to the standard library in python 3.4. It takes a cue from the Connect & Express frameworks in the nodejs ecosystem, using a single application object and series of middleware to process HTTP requests. The custom chain of middleware provides an easy way to implement complex applications. The pip utility allows packages to provide optional requirements, so features may be installed only upon request. This meshes well with the minimal nature of the Growler project: don't install anything the user doesn't need. That being said, there are (will be) community packages that are blessed by the growler developers (after ensuring they work as expected and are well tested with each version of growler) that will be available as extras directly from the growler package.

getcss - An intuitive CSS framework. Create responsive web apps with getcss. It's simple, easy to use, free, and open source. 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.

CodeIgniter is an Application Development Framework - a toolkit - for people who build web sites using PHP. Its goal is to enable you to develop projects much faster than you could if you were writing code from scratch, by providing a rich set of libraries for commonly needed tasks, as well as a simple interface and logical structure to access these libraries. CodeIgniter lets you creatively focus on your project by minimizing the amount of code needed for a given task. Where possible, CodeIgniter has been kept as flexible as possible, allowing you to work in the way you want, not being forced into working any certain way. The framework can have core parts easily extended or completely replaced to make the system work the way you need it to. In short, CodeIgniter is the malleable framework that tries to provide the tools you need while staying out of the way.

Streamlit. The fastest way to build and share data apps. Turn data scripts into sharable web apps in minutes. All in Python. All for free. No front-end experience required. Streamlit combines three simple ideas. Embrace Python scripting. Build an app in a few lines of code with our magically simple API. Then see it automatically update as you save the source file. Weave in interaction. Adding a widget is the same as declaring a variable. No need to write a backend, define routes, handle HTTP requests, etc. Deploy instantly. Use Streamlit’s sharing platform to effortlessly share, manage, and collaborate on your apps. A minimal framework for powerful apps. Face-GAN explorer. App that uses Shaobo Guan’s TL-GAN project from Insight Data Science, TensorFlow, and NVIDIA's PG-GAN to generate faces that match selected attributes. Real time object detection. An image browser for the Udacity self-driving-car dataset with real-time object detection.

Aurelia's standards-based, unobtrusive style makes it the only framework that empowers you to build components using vanilla JavaScript or TypeScript. If you know modern JS and HTML, there's little more to learn to build even the most complex apps. At the core of Aurelia is a high-performance, reactive system, capable of batching DOM updates in a way that leaves other frameworks, and their virtual DOMs, in the dust. Experience consistent, scalable performance, no matter how complex your UI. Aurelia enables powerful reactive binding to any object. By using adaptive techniques Aurelia selects the most efficient way to observe each property in your model and automatically syncs your UI and your state with best-in-class performance. State management, internationalization and validation - all official plugins from the core team. CLI, VS Code plugin, and Chrome debugger - optional tools to enhance development.

Dropwizard is a Java framework for developing ops-friendly, high-performance, RESTful web services. Dropwizard pulls together stable, mature libraries from the Java ecosystem into a simple, lightweight package that lets you focus on getting things done. Dropwizard has out-of-the-box support for sophisticated configuration, application metrics, logging, operational tools, and much more, allowing you and your team to ship a production-quality web service in the shortest time possible. Dropwizard straddles the line between being a library and a framework. Its goal is to provide performant, reliable implementations of everything a production-ready web application needs. Because this functionality is extracted into a reusable library, your application remains lean and focused, reducing both time-to-market and maintenance burdens. Because you can’t be a web application without HTTP, Dropwizard uses the Jetty HTTP library to embed an incredibly tuned HTTP server directly into your project.

The workflow automation platform for complex, mission-critical data and ML processes at scale. Flyte makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing. Flyte is used in production at Lyft, Spotify, Freenome, and others. At Lyft, Flyte has been serving production model training and data processing for over four years, becoming the de-facto platform for teams like pricing, locations, ETA, mapping, autonomous, and more. In fact, Flyte manages over 10,000 unique workflows at Lyft, totaling over 1,000,000 executions every month, 20 million tasks, and 40 million containers. Flyte has been battle-tested at Lyft, Spotify, Freenome, and others. It is entirely open-source with an Apache 2.0 license under the Linux Foundation with a cross-industry overseeing committee. Configuring machine learning and data workflows can get complex and error-prone with YAML.

Spark Framework is a simple and expressive Java/Kotlin web framework DSL built for rapid development. Sparks intention is to provide an alternative for Kotlin/Java developers that want to develop their web applications as expressive as possible and with minimal boilerplate. With a clear philosophy Spark is designed not only to make you more productive, but also to make your code better under the influence of Spark’s sleek, declarative and expressive syntax. The JVM offers one of the biggest programming ecosystems in the world. It has a lot of Java web frameworks, but pure Java web development has traditionally been very cumbersome. If you love the JVM, but hate verbose code and frameworks, Spark is the web framework for you.

Open-Source AI layer for databases. Boost efficiency of your projects by bringing Machine Learning capabilities directly to the data domain. MindsDB provides a simple way to create, train and test ML models and then publish them as virtual AI-Tables into databases. Integrate seamlessly with most of databases on the market. Use SQL queries for all manipulation with ML models. Improve model training speed with GPU without affecting your database performance. Get insights on why the ML model reached its conclusions and what affects prediction confidence. Visual tools that allows you to investigate model performance. SQL and Python queries that return explainability insights in a code. What-if analysis to evaluate confidence based on different inputs. Automate the process of applying machine learning with the state-of-the-art Lightwood AutoML library. Build custom solutions with Machine Learning in your favorite programming language.

NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.

Amazon SageMaker Canvas expands access to machine learning (ML) by providing business analysts with a visual interface that allows them to generate accurate ML predictions on their own, without requiring any ML experience or having to write a single line of code. Visual point-and-click interface to connect, prepare, analyze, and explore data for building ML models and generating accurate predictions. Automatically build ML models to run what-if analysis and generate single or bulk predictions with a few clicks. Boost collaboration between business analysts and data scientists by sharing, reviewing, and updating ML models across tools. Import ML models from anywhere and generate predictions directly in Amazon SageMaker Canvas. With Amazon SageMaker Canvas, you can import data from disparate sources, select values you want to predict, automatically prepare and explore data, and quickly and more easily build ML models. You can then analyze models and generate accurate predictions.

Flask is a lightweight WSGI web application framework. It is designed to make getting started quick and easy, with the ability to scale up to complex applications. It began as a simple wrapper around Werkzeug and Jinja and has become one of the most popular Python web application frameworks. Flask offers suggestions, but doesn't enforce any dependencies or project layout. It is up to the developer to choose the tools and libraries they want to use. There are many extensions provided by the community that make adding new functionality easy.

Deploy machine learning models at scale with more accuracy. Turn R&D into ROI with more models into production at scale, faster, with increased accuracy. Seldon reduces time-to-value so models can get to work faster. Scale with confidence and minimize risk through interpretable results and transparent model performance. Seldon Deploy reduces the time to production by providing production grade inference servers optimized for popular ML framework or custom language wrappers to fit your use cases. Seldon Core Enterprise provides access to cutting-edge, globally tested and trusted open source MLOps software with the reassurance of enterprise-level support. Seldon Core Enterprise is for organizations requiring: - Coverage across any number of ML models deployed plus unlimited users - Additional assurances for models in staging and production - Confidence that their ML model deployments are supported and protected.

Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of web development, so you can focus on writing your app without needing to reinvent the wheel. Django was designed to help developers take applications from concept to completion as quickly as possible. Django takes security seriously and helps developers avoid many common security mistakes. Some of the busiest sites on the web leverage Django’s ability to quickly and flexibly scale. Django includes dozens of extras you can use to handle common web development tasks. Django takes care of user authentication, content administration, site maps, RSS feeds, and many more tasks — right out of the box.

CherryPy allows developers to build web applications in much the same way they would build any other object-oriented Python program. This results in smaller source code developed in less time. CherryPy is now more than ten years old, and it has proven to be fast and reliable. It is being used in production by many sites, from the simplest to the most demanding. In order to make the most of CherryPy, you should start with the tutorials that will lead you through the most common aspects of the framework. Once done, you will probably want to browse through the basics and advanced sections that will demonstrate how to implement certain operations. Finally, you will want to carefully read the configuration and extend sections that go in-depth regarding the powerful features provided by the framework.

ClearML is the leading open source MLOps and AI platform that helps data science, ML engineering, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. Our frictionless, unified, end-to-end MLOps suite enables users and customers to focus on developing their ML code and automation. ClearML is used by more than 1,300 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle, from product feature exploration to model deployment and monitoring in production. Use all of our modules for a complete ecosystem or plug in and play with the tools you have. ClearML is trusted by more than 150,000 forward-thinking Data Scientists, Data Engineers, ML Engineers, DevOps, Product Managers and business unit decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide within industries such as gaming, biotech , defense, healthcare, CPG, retail, financial services, among others.

Pop PHP is an open source, object-oriented PHP framework with humble beginnings dating back to 2009, where it started as a small library of components. The focus of Pop is to provide a robust set of lightweight tools to assist developers with rapid application development. With a relatively small learning curve, Pop aims to help developers in getting the features they need into their applications so they can be up and running quickly. The Pop PHP Framework contains a group of core components and an additional 30+ components that serve as a toolkit for rapid application development. The framework itself has a base set of core components as well as numerous other additional components to facilitate many of the common features needed for a PHP application. The goal is so that anyone from a novice developer to an advanced programmer can install and start using the Pop PHP Framework quickly and effectively.

Laminas is the enterprise-ready PHP framework and components. Components for any PHP application, include dependency injection, event dispatchers, input validation and filtering, pagination and navigation, feed generation and parsing, and much, much more. Build PHP middleware applications, using the PSR-7 and PSR-15 specifications, using a variety of routing and templating options, and the dependency injection container you prefer. Build RESTful APIs using standards and provide validation, authentication, authorization, and versioning right out of the box. A proven history of success running business critical and high-usage applications, ready for PHP 7. Laminas is built with security best practices. Store passwords and encrypt with AES-256. Engineered with performance tuning in mind, Laminas runs up to 4x faster than version 2 releases. Laminas API tools provide JSON representations that can be parsed and used in any mobile framework.

Svelte is a radical new approach to building user interfaces. Whereas traditional frameworks like React and Vue do the bulk of their work in the browser, Svelte shifts that work into a compile step that happens when you build your app. Instead of using techniques like virtual DOM diffing, Svelte writes code that surgically updates the DOM when the state of your app changes. We're proud that Svelte was recently voted the most loved web framework with the most satisfied developers in a pair of industry surveys. We think you'll love it too. Read the introductory blog post to learn more. Svelte is a tool for building fast web applications. It is similar to JavaScript frameworks such as React and Vue, which share a goal of making it easy to build slick interactive user interfaces. But there's a crucial difference: Svelte converts your app into ideal JavaScript at build time, rather than interpreting your application code at run time.

Developing RESTful web services that seamlessly support exposing your data in a variety of representation media types and abstracting away the low-level details of client-server communication is not an easy task without a good toolkit. In order to simplify the development of RESTful web services and their clients in Java, a standard and portable JAX-RS API has been designed. Jersey RESTful web services 3. x framework is an open-source, production-quality, framework that provides support for Jakarta RESTful web services 3.0. Jersey framework is more than the JAX-RS reference implementation. Jersey provides its own API that extends the JAX-RS toolkit with additional features. Track the JAX-RS API and provide regular releases of production quality reference implementations that ship with GlassFish. Provides APIs to extend Jersey & build a community of users and developers. Makes it easy to build RESTful Web services utilizing Java and the Java Virtual Machine.

jQuery Mobile is an HTML5-based user interface system designed to make responsive websites and apps that are accessible on all smartphones, tablets, and desktop devices. jQuery Mobile framework takes the "write less, do more" mantra to the next level: Instead of writing unique applications for each mobile device or OS, the jQuery mobile framework allows you to design a single highly-branded responsive website or application that will work on all popular smartphone, tablet, and desktop platforms. We believe that your website or app should feel like your brand, not any particular OS. To make building highly customized themes easy, we've created ThemeRoller for mobile to make it easy to drag and drop colors and download a custom theme. For polished visuals without the bloat, we leverage CSS3 properties. We recommend using our tool to build a custom bundle that contains only the components you need.

Yii is a fast, secure, and efficient PHP framework. Flexible yet pragmatic, works right out of the box, and has reasonable defaults. While Yii can virtually eliminate most repetitive coding tasks, you are responsible for the real creative work. This often starts with designing the whole system to be built, in terms of some database schema. The best way to do this is by using migrations. Yii gives you the maximum functionality by adding the least possible overhead. Sane defaults and built-in tools help you write solid and secure code. Write more code in less time with simple, yet powerful APIs and code generation. Yii is a generic web programming framework, meaning that it can be used for developing all kinds of web applications using PHP. Because of its component-based architecture and sophisticated caching support, it is especially suitable for developing large-scale applications such as portals, forums, content management systems (CMS), ecommerce projects, RESTful services, etc.

Free. Cross-platform. Open source. A developer platform for building all your apps. Build native apps for Android, iOS, macOS and Windows from a single codebase. You can write your .NET apps in C#, F#, or Visual Basic. Your skills, code, and favorite libraries apply anywhere you use .NET. You can learn more about what .NET can do with these free videos. .NET is open source and we are very thankful for the many contributions it receives from the community.

Speed up your development process. F3 is lightweight, easy-to-use, and fast. Most of all, it doesn't get in your way. It has a gentle learning curve so you save money on training expenses. Whether you're a novice or an expert PHP programmer, F3 will get you up and running in no time. No unnecessary and painstaking installation procedures and no complex configuration are required. A powerful yet easy-to-use PHP micro-framework designed to help you build dynamic and robust web applications. Full-featured toolkit, super lightweight code base with just ~90kb, and easy to learn. Take advantage of the built-in features. Develop apps that really rock. F3 gives you a solid foundation, a mature code base, and a no-nonsense approach to writing web applications. Under the hood is an easy-to-use web development tool kit, high-performance URL routing, a multi-protocol cache engine, built-in code highlighting, and support for multilingual i18n applications.

Han­dle more re­quests with fewer re­sources com­pared to tra­di­tional stacks and frame­works based on block­ing I/O. Vert.x is a great fit for all kinds of ex­e­cu­tion en­vi­ron­ments, in­clud­ing con­strained en­vi­ron­ments like vir­tual ma­chines and con­tain­ers. Peo­ple told you asyn­chro­nous pro­gram­ming is too hard for you? We strive to make pro­gram­ming with Vert.x an ap­proach­able ex­pe­ri­ence, with­out sac­ri­fy­ing cor­rect­ness and per­for­mance. Don’t waste re­sources, in­crease de­ploy­ment den­sity and save money. You pick the model that works best for the task at hand, call­backs, promises, fu­tures, re­ac­tive ex­ten­sions, and (Kotlin) corou­tines. Vert.x is a toolkit, not a frame­work, so it is nat­u­rally very com­pos­able and em­bed­d­a­ble. We have no strong opin­ion on what your ap­pli­ca­tion struc­ture should be like. Se­lect the mod­ules and clients you need and com­pose them as you craft your ap­pli­ca­tion.

Free. Cross-platform. Open source. An app platform for building Android and iOS apps with .NET and C#. Use .NET and C# to create native apps for the billions of Android, iPhone, iPad, Mac, and Windows devices around the world. Add to that sharing code with any .NET app, empowering you to be more productive and build apps faster than ever across all platforms. Ready to take code sharing to the next level? Xamarin.Forms is an open source mobile UI framework from Microsoft for building iOS, Android, & Windows apps with .NET from a single shared codebase. Whether you want a consistent look across platforms or prefer native look and feel, Xamarin.Forms will get you up and running in no time. .NET is a developer platform made up of tools, programming languages, and libraries for building many different types of applications. Xamarin extends the .NET developer platform with tools and libraries specifically for building apps for Android, iOS, tvOS, watchOS, macOS, and Windows.

HPE Ezmeral ML Ops provides pre-packaged tools to operationalize machine learning workflows at every stage of the ML lifecycle, from pilot to production, giving you DevOps-like speed and agility. Quickly spin-up environments with your preferred data science tools to explore a variety of enterprise data sources and simultaneously experiment with multiple machine learning or deep learning frameworks to pick the best fit model for the business problems you need to address. Self-service, on-demand environments for development and test or production workloads. Highly performant training environments—with separation of compute and storage—that securely access shared enterprise data sources in on-premises or cloud-based storage. HPE Ezmeral ML Ops enables source control with out of the box integration tools such as GitHub. Store multiple models (multiple versions with metadata) for various runtime engines in the model registry.

Machine Learning made beautifully simple for everyone. Take your business to the next level with the leading Machine Learning platform. Start making data-driven decisions today! No more wildly expensive or cumbersome solutions. Machine Learning that simply works. BigML provides a selection of robustly-engineered Machine Learning algorithms proven to solve real world problems by applying a single, standardized framework across your company. Avoid dependencies on many disparate libraries that increase complexity, maintenance costs, and technical debt in your projects. BigML facilitates unlimited predictive applications across industries including aerospace, automotive, energy, entertainment, financial services, food, healthcare, IoT, pharmaceutical, transportation, telecommunications, and more. Supervised Learning: classification and regression (trees, ensembles, linear regressions, logistic regressions, deepnets), and time series forecasting.

An easy-to-use interactive tool for data exploration, analysis, visualization, and machine learning. Cloud Datalab is a powerful interactive tool created to explore, analyze, transform, and visualize data and build machine learning models on Google Cloud Platform. It runs on Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks. Cloud Datalab is built on Jupyter (formerly IPython), which boasts a thriving ecosystem of modules and a robust knowledge base. Cloud Datalab enables analysis of your data on BigQuery, AI Platform, Compute Engine, and Cloud Storage using Python, SQL, and JavaScript (for BigQuery user-defined functions). Whether you're analyzing megabytes or terabytes, Cloud Datalab has you covered. Query terabytes of data in BigQuery, run local analysis on sampled data, and run training jobs on terabytes of data in AI Platform seamlessly.

Seamless machine learning model management, deployment, and monitoring for supercharging MLOps for any organization on the best AI platform. Use navio to perform various machine learning operations across an organization's entire artificial intelligence landscape. Take your experiments out of the lab and into production, and integrate machine learning into your workflow for a real, measurable business impact. navio provides various Machine Learning operations (MLOps) to support you during the model development process all the way to running your model in production. Automatically create REST endpoints and keep track of the machines or clients that are interacting with your model. Focus on exploration and training your models to obtain the best possible result and stop wasting time and resources on setting up infrastructure and other peripheral features. Let navio handle all aspects of the product ionization process to go live quickly with your machine learning models.

The Xilinx’s AI development platform for AI inference on Xilinx hardware platforms consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP. Supports mainstream frameworks and the latest models capable of diverse deep learning tasks. Provides a comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices. You can find the closest model and start re-training for your applications! Provides a powerful open source quantizer that supports pruned and unpruned model quantization, calibration, and fine tuning. The AI profiler provides layer by layer analysis to help with bottlenecks. The AI library offers open source high-level C++ and Python APIs for maximum portability from edge to cloud. Efficient and scalable IP cores can be customized to meet your needs of many different applications.

MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components. Record and query experiments: code, data, config, and results. Package data science code in a format to reproduce runs on any platform. Deploy machine learning models in diverse serving environments. Store, annotate, discover, and manage models in a central repository. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs. An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects.

Datatron offers tools and features built from scratch, specifically to make machine learning in production work for you. Most teams discover that there’s more to just deploying models, which is already a very manual and time-consuming task. Datatron offers single model governance and management platform for all of your ML, AI, and Data Science models in production. We help you automate, optimize, and accelerate your ML models to ensure that they are running smoothly and efficiently in production. Data Scientists use a variety of frameworks to build the best models. We support anything you’d build a model with ( e.g. TensorFlow, H2O, Scikit-Learn, and SAS ). Explore models built and uploaded by your data science team, all from one centralized repository. Create a scalable model deployment in just a few clicks. Deploy models built using any language or framework. Make better decisions based on your model performance.

Tencent Cloud TI Platform is a one-stop machine learning service platform designed for AI engineers. It empowers AI development throughout the entire process from data preprocessing to model building, model training, model evaluation, and model service. Preconfigured with diverse algorithm components, it supports multiple algorithm frameworks to adapt to different AI use cases. Tencent Cloud TI Platform delivers a one-stop machine learning experience that covers a complete and closed-loop workflow from data preprocessing to model building, model training, and model evaluation. With Tencent Cloud TI Platform, even AI beginners can have their models constructed automatically, making it much easier to complete the entire training process. Tencent Cloud TI Platform's auto-tuning tool can also further enhance the efficiency of parameter tuning. Tencent Cloud TI Platform allows CPU/GPU resources to elastically respond to different computing power needs with flexible billing modes.

Create customized monitors for your machine learning models with our magically-simple monitor builder, and get alerts for issues like concept drift, model performance degradation, bias and more. Aporia integrates seamlessly with any ML infrastructure. Whether it’s a FastAPI server on top of Kubernetes, an open-source deployment tool like MLFlow or a machine learning platform like AWS Sagemaker. Zoom into specific data segments to track model behavior. Identify unexpected bias, underperformance, drifting features and data integrity issues. When there are issues with your ML models in production, you want to have the right tools to get to the root cause as quickly as possible. Go beyond model monitoring with our investigation toolbox to take a deep dive into model performance, data segments, data stats or distribution.

Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. Automate AI lifecycles with ModelOps pipelines. Speed data science development with AutoAI. Prepare and build models visually and programmatically. Deploy and run models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.

The Wekinator is free, open source software. Wekinator 1.0 was originally created in 2009 by Rebecca Fiebrink. In 2015, Rebecca released Wekinator 2.0, an entirely new version with redesigned interactions, new algorithms, and ability to connect easily to dozens of other creative coding tools and sensors. Wekinator 2.0 continues to be gently updated with bug fixes and feature requests. It allows anyone to use machine learning to build new musical instruments, gestural game controllers, computer vision or computer listening systems, and more. The Wekinator allows users to build new interactive systems by demonstrating human actions and computer responses, instead of writing programming code. Create mappings between gesture and computer sounds. Control a drum machine using your webcam! Play Ableton using a Kinect! Control interactive visual environments created in Processing, OpenFrameworks, or Quartz Composer, or game engines like Unity, using gestures sensed from webcam, Kinect, etc.