Ensemble Dark Matter Alternatives

The training data platform for AI teams. A machine learning model is only as good as its training data. Labelbox is an end-to-end platform to create and manage high-quality training data all in one place, while supporting your production pipeline with powerful APIs. Powerful image labeling tool for image classification, object detection and segmentation. When every pixel matters, you need accurate and intuitive image segmentation tools. Customize the tools to support your specific use case, including instances, custom attributes and much more. Performant video labeling editor for cutting-edge computer vision. Label directly on the video up to 30 FPS with frame level. Additionally, Labelbox provides per frame label feature analytics enabling you to create better models faster. Creating training data for natural language intelligence has never been easier. Label text strings, conversations, paragraphs, and documents with fast & customizable classification.

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.

A machine learning monitoring solution that helps you easily oversee and troubleshoot high model volumes. With explainability accuracy that’s unparalleled and unique analyses that are not available anywhere else, data scientists avoid false alarms and dead ends, addressing critical problems quickly and effectively. Your machine learning models stay optimized, so that your business is optimized. TruEra’s solution is based on an explainability engine that, due to years of dedicated research and development, is significantly more accurate than current tools. TruEra’s enterprise-class AI explainability technology is without peer. The core diagnostic engine is based on six years of research at Carnegie Mellon University and dramatically outperforms competitors. The platform quickly performs sophisticated sensitivity analysis that enables data scientists, business users, and risk and compliance teams to understand exactly how and why a model makes predictions.

Nucleus helps ML teams build better datasets. Bring together your data, ground truth, and model predictions to effortlessly fix model failures and data quality issues. Optimize your labeling spend by identifying class imbalance, errors, and edge cases in your data with Scale Nucleus. Significantly improve model performance by uncovering and fixing model failures. Find and label high-value data by curating unlabeled data with active learning and edge case mining. Curate the best datasets by collaborating with ML engineers, labelers, and data ops on the same platform. Easily visualize and explore your data to quickly find edge cases that need labeling. Check how well your models are performing and always ship the best one. Easily view your data, metadata, and aggregate statistics with rich overlays, using our powerful UI. Nucleus supports visualization of images, videos, and lidar scenes, overlaid with all associated labels, predictions, and metadata.

The leading platform for entire computer vision lifecycle. Iterate from image annotation to accurate neural networks 10x faster. With our best-in-class data labeling tools transform your images / videos / 3d point cloud into high-quality training data. Train your models, track experiments, visualize and continuously improve model predictions, build custom solution within the single environment. Our self-hosted solution guaranties data privacy, powerful customization capabilities, and easy integration into your technology stack. A turnkey solution for Computer Vision: multi-format data annotation & management, quality control at scale and neural networks training in end-to-end platform. Inspired by professional video editing software, created by data scientists for data scientists — the most powerful video labeling tool for machine learning and more.

Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology. Cloud AutoML leverages more than 10 years of proprietary Google Research technology to help your machine learning models achieve faster performance and more accurate predictions. Use Cloud AutoML’s simple graphical user interface to train, evaluate, improve, and deploy models based on your data. You’re only a few minutes away from your own custom machine learning model. Google’s human labeling service can put a team of people to work annotating or cleaning your labels to make sure your models are being trained on high-quality data.

Neural Designer is a powerful software tool for developing and deploying machine learning models. It provides a user-friendly interface that allows users to build, train, and evaluate neural networks without requiring extensive programming knowledge. With a wide range of features and algorithms, Neural Designer simplifies the entire machine learning workflow, from data preprocessing to model optimization. In addition, it supports various data types, including numerical, categorical, and text, making it versatile for domains. Additionally, Neural Designer offers automatic model selection and hyperparameter optimization, enabling users to find the best model for their data with minimal effort. Finally, its intuitive visualizations and comprehensive reports facilitate interpreting and understanding the model's performance.

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.

IBM Watson Machine Learning is a full-service IBM Cloud offering that makes it easy for developers and data scientists to work together to integrate predictive capabilities with their applications. The Machine Learning service is a set of REST APIs that you can call from any programming language to develop applications that make smarter decisions, solve tough problems, and improve user outcomes. Take advantage of machine learning models management (continuous learning system) and deployment (online, batch, streaming). Select any of widely supported machine learning frameworks: TensorFlow, Keras, Caffe, PyTorch, Spark MLlib, scikit learn, xgboost and SPSS. Use the command-line interface and Python client to manage your artifacts. Extend your application with artificial intelligence through the Watson Machine Learning REST API.

We ​help our clients ​leverage visual AI to solve real-world business problems​. Our team of skilled data scientists and machine learning engineers ​will quickly train and deliver highly precise and accurate machine learning models for your visual data. IceCream Labs is the leading enterprise AI solution company. IceCream Labs provides solutions for retail, digital media and higher education. The company’s expertise is developing machine learning and deep learning models to solve real world business problems using text, image and numerical data. Try IceCream Labs if your business ​handles visual data like images, video and documents. If you need to identify what’s in an image or a document, we can help you. ​If you need to quickly train and deploy a machine learning model, IceCream Labs is the answer. Talk to our AI experts and get sales performance improvements across your product line.

JADBio is a state-of-the-art automated Machine Learning Platform without the need for coding. With its breakthrough algorithms it can solve open problems in machine learning. Anybody can use it and perform a sophisticated and correct machine learning analysis even if they do not know any math, statistics, or coding. It is purpose-built for life science data and particularly molecular data. This means that it can deal with the idiosyncrasies of molecular data such as very low sample size and very high number of measured quantities that could reach to millions. Life scientists need it to understand what are the features and biomarkers that are predictive and important, what is their role, and get intuition about the molecular mechanisms involved. Knowledge discovery is often more important than a predictive model. So, JADBio focuses on feature selection and its interpretation.

Every step of the machine learning and computer vision workflow is made simple and fast within one no-code platform. Sense allows anyone to build and deploy AI IoT solutions to any cloud, the edge or on-premise. Learn how Sense provides simplicity, consistency and transparency to AI/ML teams with enough power and depth for ML engineers yet easy enough to use for subject matter experts. Sense Data Annotation optimizes the success of your machine learning models with the fastest, easiest way to label video and image data for high-quality training dataset creation. The Sense platform offers one-touch labeling integration for continuous machine learning at the edge for simplified management of all your AI solutions.

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.

It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP).

With Automaton AI’s ADVIT, create, manage and develop high-quality training data and DNN models all in one place. Optimize the data automatically and prepare it for each phase of the computer vision pipeline. Automate the data labeling processes and streamline data pipelines in-house. Manage the structured and unstructured video/image/text datasets in runtime and perform automatic functions that refine your data in preparation for each step of the deep learning pipeline. Upon accurate data labeling and QA, you can train your own model. DNN training needs hyperparameter tuning like batch size, learning, rate, etc. Optimize and transfer learning on trained models to increase accuracy. Post-training, take the model to production. ADVIT also does model versioning. Model development and accuracy parameters can be tracked in run-time. Increase the model accuracy with a pre-trained DNN model for auto-labeling.

Sagify complements AWS Sagemaker by hiding all its low-level details so that you can focus 100% on Machine Learning. Sagemaker is the ML engine and Sagify is the data science-friendly interface. You just need to implement 2 functions, a train and a predict in order to train, tune and deploy hundreds of ML models. Manage your ML models from one place without dealing with low level engineering tasks. No more flaky ML pipelines. Sagify offers 100% reliable training and deployment on AWS. Train, tune and deploy hundreds of ML models by implementing just 2 functions.

Quickly and easily generate diverse and realistic synthetic data and high-fidelity 3D worlds to enhance model performance. Bifrost's platform is the fastest way to generate the high-quality synthetic images that you need to improve ML performance and overcome real-world data limitations. Prototype and test up to 30x faster by circumventing costly and time-consuming real-world data collection and annotation. Generate data to account for rare scenarios underrepresented in real data, resulting in more balanced datasets. Manual annotation and labeling is an error-prone, resource-intensive process. Easily and quickly generate data that is pre-labeled and pixel-perfect. Real-world data can inherit the biases of conditions under which the data was collected, and generate data to solve for these instances.

Baidu AI Cloud Machine Learning (BML), an end-to-end machine learning platform designed for enterprises and AI developers, can accomplish one-stop data pre-processing, model training, and evaluation, and service deployments, among others. The Baidu AI Cloud AI development platform BML is an end-to-end AI development and deployment platform. Based on the BML, users can accomplish the one-stop data pre-processing, model training and evaluation, service deployment, and other works. The platform provides a high-performance cluster training environment, massive algorithm frameworks and model cases, as well as easy-to-operate prediction service tools. Thus, it allows users to focus on the model and algorithm and obtain excellent model and prediction results. The fully hosted interactive programming environment realizes the data processing and code debugging. The CPU instance supports users to install a third-party software library and customize the environment, ensuring flexibility.

An end-to-end platform that provides various machine learning algorithms to meet your data mining and analysis requirements. Machine Learning Platform for AI provides end-to-end machine learning services, including data processing, feature engineering, model training, model prediction, and model evaluation. Machine learning platform for AI combines all of these services to make AI more accessible than ever. Machine Learning Platform for AI provides a visualized web interface allowing you to create experiments by dragging and dropping different components to the canvas. Machine learning modeling is a simple, step-by-step procedure, improving efficiencies and reducing costs when creating an experiment. Machine Learning Platform for AI provides more than one hundred algorithm components, covering such scenarios as regression, classification, clustering, text analysis, finance, and time series.

Manage and optimize models across the entire ML lifecycle, from experiment tracking to monitoring models in production. Achieve your goals faster with the platform built to meet the intense demands of enterprise teams deploying ML at scale. Supports your deployment strategy whether it’s private cloud, on-premise servers, or hybrid. Add two lines of code to your notebook or script and start tracking your experiments. Works wherever you run your code, with any machine learning library, and for any machine learning task. Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. Monitor your models during every step from training to production. Get alerts when something is amiss, and debug your models to address the issue. Increase productivity, collaboration, and visibility across all teams and stakeholders.

Automatically discover issues, diagnose problems, and improve models with Arize’s machine learning observability platform. Machine learning systems address mission critical needs for businesses and their customers every day, yet often fail to perform in the real world. Arize is an end-to-end observability platform to accelerate detecting and resolving issues for your AI models at large. Seamlessly enable observability for any model, from any platform, in any environment. Lightweight SDKs to send training, validation, and production datasets. Link real-time or delayed ground truth to predictions. Gain foresight and confidence that your models will perform as expected once deployed. Proactively catch any performance degradation, data/prediction drift, and quality issues before they spiral. Reduce the time to resolution (MTTR) for even the most complex models with flexible, easy-to-use tools for root cause analysis.

Creating ML apps should be simple and frictionless. UnionML is an open-source Python framework built on top of Flyte™, unifying the complex ecosystem of ML tools into a single interface. Combine the tools that you love using a simple, standardized API so you can stop writing so much boilerplate and focus on what matters: the data and the models that learn from them. Fit the rich ecosystem of tools and frameworks into a common protocol for machine learning. Using industry-standard machine learning methods, implement endpoints for fetching data, training models, serving predictions (and much more) to write a complete ML stack in one place. ‍ Data science, ML engineering, and MLOps practitioners can all gather around UnionML apps as a way of defining a single source of truth about your ML system’s behavior.

Qwak simplifies the productionization of machine learning models at scale. Qwak’s [ML Engineering Platform] empowers data science and ML engineering teams to enable the continuous productionization of models at scale. By abstracting the complexities of model deployment, integration and optimization, Qwak brings agility and high-velocity to all ML initiatives designed to transform business, innovate, and create competitive advantage. Qwak build system allows data scientists to create an immutable, tested production-grade artifact by adding "traditional" build processes. Qwak build system standardizes a ML project structure that automatically versions code, data, and parameters for each model build. Different configurations can be used to build different builds. It is possible to compare builds and query build data. You can create a model version using remote elastic resources. Each build can be run with different parameters, different data sources, and different resources. Builds c

SANCARE is a start-up specializing in Machine Learning applied to hospital data. We collaborate with some of the best scientists in the field. SANCARE provides Medical Information Departments with an ergonomic and intuitive interface, promoting rapid adoption. The user has access to all the documents that constitute the computerized patient record. A true production tool, each step of the coding process is traced for external checks. Machine learning makes it possible to develop powerful predictive models from large volumes of data, and to take into account the notion of context, which is not possible for rule engines or semantic analysis engines. It is therefore possible to automate complex decision-making processes or to detect weak signals ignored by humans. The SANCARE software machine learning engine is based on a probabilistic approach. It learns over a large amount of examples to predict the right codes, without any indication.

Easy-to-Use Build advanced predictive models without math & coding - in just a few clicks. Complete Experience. From cleaning data to building models to making predictions, we provide you all. Trustworthy. Know the 'why' behind AI decisions and drive business with actionable insights. Data Analytics in minutes, without writing code. The total process of building ML algorithms, explaining results, and predicting outcomes in one single click. Machine Learning in 3 Steps. Go from raw data to actionable analytics without writing a single line of code. Upload your data. Ask questions in plain english. Get the best performing model for your data and Share your results. Increase Customer Productivity. We help Companies to leverage no code Machine learning to improve their Customer Experience.

Bring data analysts, engineers, and scientists together. Enable self-service analytics and operationalize machine learning. Get results today and build for tomorrow. Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) or a customizable drag-and-drop visual interface at any step of the predictive dataflow prototyping process – from wrangling to analysis to modeling. Profile the data visually at every step of the analysis. Interactively explore and chart your data using 25+ built-in charts. Prepare, enrich, blend, and clean data using 80+ built-in functions. Leverage Machine Learning technologies (Scikit-Learn, MLlib, TensorFlow, Keras, etc.) in a visual UI. Build & optimize models in Python or R and integrate any external ML library through code APIs.

The open-source ML observability platform. Evaluate, test, and monitor ML models from validation to production. From tabular data to NLP and LLM. Built for data scientists and ML engineers. All you need to reliably run ML systems in production. Start with simple ad hoc checks. Scale to the complete monitoring platform. All within one tool, with consistent API and metrics. Useful, beautiful, and shareable. Get a comprehensive view of data and ML model quality to explore and debug. Takes a minute to start. Test before you ship, validate in production and run checks at every model update. Skip the manual setup by generating test conditions from a reference dataset. Monitor every aspect of your data, models, and test results. Proactively catch and resolve production model issues, ensure optimal performance, and continuously improve it.

Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio. Perform all ML development steps, from preparing raw data to deploying and monitoring ML models, with access to the most comprehensive set of tools in a single web-based visual interface. Quickly move between steps of the ML lifecycle to fine-tune your models. Replay training experiments, tune model features and other inputs, and compare results, without leaving SageMaker Studio.

CentML accelerates Machine Learning workloads by optimizing models to utilize hardware accelerators, like GPUs or TPUs, more efficiently and without affecting model accuracy. Our technology boosts training and inference speed, lowers compute costs, increases your AI-powered product margins, and boosts your engineering team's productivity. Software is no better than the team who built it. Our team is stacked with world-class machine learning and system researchers and engineers. Focus on your AI products and let our technology take care of optimum performance and lower cost for you.

Powerful data engineering workflows, without the infrastructure headaches. Complex streaming, scheduling, and data backfill pipelines, are all defined in simple, composable Python. Make ETL a thing of the past, fetch all of your data in real-time, no matter how complex. Incorporate deep learning and LLMs into decisions alongside structured business data. Make better predictions with fresher data, don’t pay vendors to pre-fetch data you don’t use, and query data just in time for online predictions. Experiment in Jupyter, then deploy to production. Prevent train-serve skew and create new data workflows in milliseconds. Instantly monitor all of your data workflows in real-time; track usage, and data quality effortlessly. Know everything you computed and data replay anything. Integrate with the tools you already use and deploy to your own infrastructure. Decide and enforce withdrawal limits with custom hold times.

Aquarium's embedding technology surfaces the biggest problems in your model performance and finds the right data to solve them. Unlock the power of neural network embeddings without worrying about maintaining infrastructure or debugging embedding models. Automatically find the most critical patterns of model failures in your dataset. Understand the long tail of edge cases and triage which issues to solve first. Trawl through massive unlabeled datasets to find edge-case scenarios. Bootstrap new classes with a handful of examples using few-shot learning technology. The more data you have, the more value we offer. Aquarium reliably scales to datasets containing hundreds of millions of data points. Aquarium offers solutions engineering resources, customer success syncs, and user training to help customers get value. We also offer an anonymous mode for organizations who want to use Aquarium without exposing any sensitive data.

Onboard your data and models to Openlayer and collaborate with the whole team to align expectations surrounding quality and performance. Breeze through the whys behind failed goals to solve them efficiently. The information to diagnose the root cause of issues is at your fingertips. Generate more data that looks like the subpopulation and retrain the model. Test new commits against your goals to ensure systematic progress without regressions. Compare versions side-by-side to make informed decisions and ship with confidence. Save engineering time by rapidly figuring out exactly what’s driving model performance. Find the most direct paths to improving your model. Know the exact data needed to boost model performance and focus on cultivating high-quality and representative datasets.

Our platforms provide a trusted foundation upon which to design, build, and deploy custom machine learning and artificial intelligence solutions. Build next-best-action applications that learn, adapt, and optimize using reinforcement-learning based algorithms. Custom, continuously-improving deep learning vision models to solve your unique challenges. Predict the future using state-of-the-art forecasts. Enable smarter decisions throughout your organization with cloud based tools to monitor and analyze. The process of taking a modern machine learning application from research and ad-hoc code to a robust, scalable platform remains a key challenge for experienced data science and engineering teams. Strong ML simplifies this process with a complete suite of tools to manage, deploy, and monitor your machine learning applications.

The entire process of building machine learning algorithms and predicting outcomes, packed in one single click. Not all data is built to be ready for ML, use the Data Dialog to seamlessly shape your dataset without wrangling your files. Share your prediction reports with your team or make them public. Allow anyone to start making predictions on your model. Bring dynamic ML predictions into your own app using our low-code API. Predict willingness to pay, score leads and much more in real-time. Obviously AI puts the world’s most cutting-edge algorithms in your hands, without compromising on performance. Forecast revenue, optimize supply chain, personalize marketing. You can now know what happens next. Add a CSV file OR integrate with your favorite data sources in minutes. Pick your prediction column from a dropdown, we'll auto build the AI. Beautifully visualize predicted results, top drivers and simulate "what-if" scenarios.

Unique and efficient turn-key algorithms for data scientists. Powerful circuit building blocks for quantum engineers. Turn-key algorithm implementations for data scientists, financial analysts, and engineers. Explore problems in binary optimization, machine learning, linear algebra, and monte carlo sampling on simulators and real quantum hardware. No prior experience with quantum computing is required. Use NISQ data loader circuits to load classical data into quantum states to use with your algorithms. Use circuit building blocks for linear algebra with distance estimation and matrix multiplication circuits. Use our circuit building blocks to create your own algorithms. Get a significant performance boost for D-Wave hardware and use the latest improvements for gate-based approaches. Try out quantum data loaders and algorithms with guaranteed speed-ups on clustering, classification, and regression.

Segmind provides simplified access to large computing. You can use it to run your high-performance workloads such as Deep learning training or other complex processing jobs. Segmind offers zero-setup environments within minutes and lets your share access with your team members. Segmind's MLOps platform can also be used to manage deep learning projects end-to-end with integrated data storage and experiment tracking. ML engineers are not cloud engineers and cloud infrastructure management is a pain. So, we abstracted away all of it so that your ML team can focus on what they do best, and build models better and faster. Training ML/DL models take time and can get expensive quickly. But with Segmind, you can scale up your compute seamlessly while also reducing your costs by up to 70%, with our managed spot instances. ML managers today don't have a bird's eye view of ML development activities and cost.

Delineate offers an easy-to-use platform for generating machine learning-driven predictive models for a range of purposes. Enrich your CRM data with churn predictions, sales forecasts, and even build data products for your customers and team, just to name a few. With Delineate you can access data-driven insights to improve decision-making with ease. The platform caters to founders, revenue teams, product managers, executives, and data enthusiasts. Try Delineate and unleash your data's full potential.

Data scientists and business analysts use Altair to generate actionable insight from their data. Knowledge Studio is a market-leading easy to use machine learning and predictive analytics solution that rapidly visualizes data as it quickly generates explainable results - without requiring a single line of code. A recognized analytics leader, Knowledge Studio brings transparency and automation to machine learning with features such as AutoML and explainable AI without restricting how models are configured and tuned, giving you control over model building. Knowledge Studio is designed to enable collaboration across the business. Data scientists and business analysts can complete complex projects in minutes or hours, not weeks or months. Results are easily understood and explained. The ease of use and automation of steps of the modeling process enable data scientists to efficiently develop more machine learning models faster than coding or using other tools.

Deploy best-in-class predictive analytics models TADA by MyDataModels helps professionals use their Small Data to enhance their business with a light, easy-to-set-up tool. TADA provides a predictive modeling solution leading to fast and usable results. Shift from days to a few hours into building ad hoc effective models with our 40% reduced time automated data preparation. Get outcomes from your data without programming or machine learning skills. Optimize your time with explainable and understandable models made of easy-to-read formulas. Turn your data into insights in a snap on any platform and create effective automated models. TADA removes the complexity of building predictive models by automating the generative machine learning process – data in, model out. Build and run machine learning models on any devices and platforms through our powerful web-based pre-processing features.

TrueFoundry is a Cloud-native Machine Learning Training and Deployment PaaS on top of Kubernetes that enables Machine learning teams to train and Deploy models at the speed of Big Tech with 100% reliability and scalability - allowing them to save cost and release Models to production faster. We abstract out the Kubernetes for Data Scientists and enable them to operate in a way they are comfortable. It also allows teams to deploy and fine-tune large language models seamlessly with full security and cost optimization. TrueFoundry is open-ended, API Driven and integrates with the internal systems, deploys on a company's internal infrastructure and ensures complete Data Privacy and DevSecOps practices.

Experience an entirely new way of training machine learning models on your Mac. Create ML takes the complexity out of model training while producing powerful Core ML models. Train multiple models using different datasets, all in a single project. Preview your model performance using Continuity with your iPhone camera and microphone on your Mac, or drop in sample data. Pause, save, resume, and extend your training process. Interactively learn how your model performs on test data from your evaluation set. Explore key metrics and their connections to specific examples to help identify challenging use cases, further investments in data collection, and opportunities to help improve model quality. Use an external graphics processing unit with your Mac for even better model training performance. Train models blazingly fast right on your Mac while taking advantage of CPU and GPU. Create ML has a variety of model types to choose from.

At Vaex.io we aim to democratize big data and make it available to anyone, on any machine, at any scale. Cut development time by 80%, your prototype is your solution. Create automatic pipelines for any model. Empower your data scientists. Turn any laptop into a big data powerhouse, no clusters, no engineers. We provide reliable and fast data driven solutions. With our state-of-the-art technology we build and deploy machine learning models faster than anyone on the market. Turn your data scientist into big data engineers. We provide comprehensive training of your employees, enabling you to take full advantage of our technology. Combines memory mapping, a sophisticated expression system, and fast out-of-core algorithms. Efficiently visualize and explore big datasets, and build machine learning models on a single machine.

RapidMiner is reinventing enterprise AI so that anyone has the power to positively shape the future. We’re doing this by enabling ‘data loving’ people of all skill levels, across the enterprise, to rapidly create and operate AI solutions to drive immediate business impact. We offer an end-to-end platform that unifies data prep, machine learning, and model operations with a user experience that provides depth for data scientists and simplifies complex tasks for everyone else. Our Center of Excellence methodology and the RapidMiner Academy ensures customers are successful, no matter their experience or resource levels. Simplify operations, no matter how complex models are, or how they were created. Deploy, evaluate, compare, monitor, manage and swap any model. Solve your business issues faster with sharper insights and predictive models, no one understands the business problem like you do.

Experiment tracking, hyperparameter optimization, model and dataset versioning. Track, compare, and visualize ML experiments with 5 lines of code. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Optimize models with our massively scalable hyperparameter search tool. Sweeps are lightweight, fast to set up, and plug in to your existing infrastructure for running models. Save every detail of your end-to-end machine learning pipeline — data preparation, data versioning, training, and evaluation. It's never been easier to share project updates. Explain how your model works, show graphs of how model versions improved, discuss bugs, and demonstrate progress towards milestones. Use this central platform to reliably track all your organization's machine learning models, from experimentation to 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.

Kraken is for everyone from analysts to data scientists. Built to be the easiest-to-use, no-code automated machine learning platform. The Kraken no-code automated machine learning (AutoML) platform simplifies and automates data science tasks like data prep, data cleaning, algorithm selection, model training, and model deployment. Kraken was built with analysts and engineers in mind. If you've done data analysis before, you're ready! Kraken's no-code, easy-to-use interface and integrated SONAR© training make it easy to become a citizen data scientist. Advanced features allow data scientists to work faster and more efficiently. Whether you use Excel or flat files for day-to-day reporting or just ad-hoc analysis and exports, drag-and-drop CSV upload and the Amazon S3 connector in Kraken make it easy to start building models with a few clicks. Data Connectors in Kraken allow you to connect to your favorite data warehouse, business intelligence tools, and cloud storage.

A data science platform that improves productivity with unparalleled abilities. Build and evaluate higher-quality machine learning (ML) models. Increase business flexibility by putting enterprise-trusted data to work quickly and support data-driven business objectives with easier deployment of ML models. Using cloud-based platforms to discover new business insights. Building a machine learning model is an iterative process. In this ebook, we break down the process and describe how machine learning models are built. Explore notebooks and build or test machine learning algorithms. Try AutoML and see data science results. Build high-quality models faster and easier. Automated machine learning capabilities rapidly examine the data and recommend the optimal data features and best algorithms. Additionally, automated machine learning tunes the model and explains the model’s results.

Accelerate your deep learning workload. Speed your time to value with AI model training and inference. With advancements in compute, algorithm and data access, enterprises are adopting deep learning more widely to extract and scale insight through speech recognition, natural language processing and image classification. Deep learning can interpret text, images, audio and video at scale, generating patterns for recommendation engines, sentiment analysis, financial risk modeling and anomaly detection. High computational power has been required to process neural networks due to the number of layers and the volumes of data to train the networks. Furthermore, businesses are struggling to show results from deep learning experiments implemented in silos.

Empower data scientists and application developers to collaborate on ML projects. Build, load, optimize and test models and integrations before publishing to end-users for integration into live applications. Reduce data science workload and improve decision-making by storing and sharing ML models across the organization. Publish updates to automatically push changes to models in production. Drive efficiency by instantly providing ML-powered insights within any web-based business application. Our self-service, drag-and-drop browser extension enables analysts and business users to integrate models into any web-based application with zero code.

Apache PredictionIO® is an open-source machine learning server built on top of a state-of-the-art open-source stack for developers and data scientists to create predictive engines for any machine learning task. It lets you quickly build and deploy an engine as a web service on production with customizable templates. Respond to dynamic queries in real-time once deployed as a web service, evaluate and tune multiple engine variants systematically, and unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics. Speed up machine learning modeling with systematic processes and pre-built evaluation measures, support machine learning and data processing libraries such as Spark MLLib and OpenNLP. Implement your own machine learning models and seamlessly incorporate them into your engine. Simplify data infrastructure management. Apache PredictionIO® can be installed as a full machine learning stack, bundled with Apache Spark, MLlib, HBase, Akka HTTP, etc.