TruEra Alternatives

Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more. Google Cloud offers two computer vision products that use machine learning to help you understand your images with industry-leading prediction accuracy. Automate the training of your own custom machine learning models. Simply upload images and train custom image models with AutoML Vision’s easy-to-use graphical interface; optimize your models for accuracy, latency, and size; and export them to your application in the cloud, or to an array of devices at the edge. Google Cloud’s Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. Assign labels to images and quickly classify them into millions of predefined categories. Detect objects and faces, read printed and handwritten text, and build valuable metadata into your image catalog.

(Bank CFO) “I don’t have confidence and trust in our data. We keep discovering hidden risks”. Since 70% of data initiatives fail due to unreliable data (Gartner research), are you risking your reputation by trusting the accuracy of your data that you share with your business stakeholders and partners? Data Trust Scores must be measured in Data Lakes, warehouses, and throughout the pipeline, to ensure the data is trustworthy and fit for use. It typically takes 4-6 weeks of manual effort just to set a file or table for validation. Then, the rules have to be constantly updated as the data evolves. The only scalable option is to automate data validation rules discovery and rules maintenance. DataBuck is an autonomous, self-learning, Data Observability, Quality, Trustability and Data Matching tool. It reduces effort by 90% and errors by 70%. "What took my team of 10 Engineers 2 years to do, DataBuck could complete it in less than 8 hours." (VP, Enterprise Data Office, a US bank)

Immuta is the market leader in secure Data Access, providing data teams one universal platform to control access to analytical data sets in the cloud. Only Immuta can automate access to data by discovering, securing, and monitoring data. Data-driven organizations around the world trust Immuta to speed time to data, safely share more data with more users, and mitigate the risk of data leaks and breaches. Founded in 2015, Immuta is headquartered in Boston, MA. Immuta is the fastest way for algorithm-driven enterprises to accelerate the development and control of machine learning and advanced analytics. The company's hyperscale data management platform provides data scientists with rapid, personalized data access to dramatically improve the creation, deployment and auditability of machine learning and AI.

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.

Giskard provides interfaces for AI & Business teams to evaluate and test ML models through automated tests and collaborative feedback from all stakeholders. Giskard speeds up teamwork to validate ML models and gives you peace of mind to eliminate risks of regression, drift, and bias before deploying ML models to production.

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.

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.

AI-powered data integration tool that helps streamline the process of customer data onboarding. It allows for easy and fast automated data integration from various sources without coding, reducing preparation time to just a few minutes. With Datuum, organizations can efficiently extract, ingest, transform, migrate, and establish a single source of truth for their data, while integrating it into their existing data storage. Datuum is a no-code product and can reduce up to 80% of the time spent on data-related tasks, freeing up time for organizations to focus on generating insights and improving the customer experience. With over 40 years of experience in data management and operations, we at Datuum have incorporated our expertise into the core of our product, addressing the key challenges faced by data engineers and managers and ensuring that the platform is user-friendly, even for non-technical specialists.

NetOwl NameMatcher, the winner of the MITRE Multicultural Name Matching Challenge, offers the most accurate, fast, and scalable name matching available. Using a revolutionary machine learning-based approach, NetOwl addresses complex fuzzy name matching challenges. Traditional name matching approaches, such as Soundex, edit distance, and rule-based methods, suffer from both precision (false positives) and recall (false negative) problems in addressing the variety of fuzzy name matching challenges discussed above. NetOwl applies an empirically driven, machine learning-based probabilistic approach to name matching challenges. It derives intelligent, probabilistic name matching rules automatically from large-scale, real-world, multi-ethnicity name variant data. NetOwl utilizes different matching models optimized for each of the entity types (e.g., person, organization, place) In addition, NetOwl performs automatic name ethnicity detection as well.

Deeploy helps you to stay in control of your ML models. Easily deploy your models on our responsible AI platform, without compromising on transparency, control, and compliance. Nowadays, transparency, explainability, and security of AI models is more important than ever. Having a safe and secure environment to deploy your models enables you to continuously monitor your model performance with confidence and responsibility. Over the years, we experienced the importance of human involvement with machine learning. Only when machine learning systems are explainable and accountable, experts and consumers can provide feedback to these systems, overrule decisions when necessary and grow their trust. That’s why we created Deeploy.

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.

Adopting data-centric AI has never been easier with automated data quality profiling and synthetic data generation. We help data scientists to unlock data's full potential. YData Fabric empowers users to easily understand and manage data assets, synthetic data for fast data access, and pipelines for iterative and scalable flows. Better data, and more reliable models delivered at scale. Automate data profiling for simple and fast exploratory data analysis. Upload and connect to your datasets through an easily configurable interface. Generate synthetic data that mimics the statistical properties and behavior of the real data. Protect your sensitive data, augment your datasets, and improve the efficiency of your models by replacing real data or enriching it with synthetic data. Refine and improve processes with pipelines, consume the data, clean it, transform your data, and work its quality to boost machine learning models' performance.

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.

The ultimate platform for expanding your business horizons with data-driven decisions. Improve every step of your supply chain journey. Our no-code platform enhances the quality of master data to offer you a set of optimization solutions, from inventory planning to distribution network. Relying on trusted data-driven analytics, you can proactively prevent critical issues from arising, making crucial real-time adjustments. Build trust in your data and manage them with confidence. Our user-friendly platform empowers financial institutions with transparent data-driven insights to improve key financial processes. We put eXplainable AI in the hands of business experts, so they can develop advanced financial models and improve decision-making. Rulex Academy will teach you all you need to know to analyse your data, build your first workflows, get to grips with algorithms, and quickly optimize complex processes with our self-paced, interactive online training courses.

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.

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

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.

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.

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.

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.

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.

We are a leader in Data Quality & ML Model for enterprises adopting a multi-cloud, ML and modern data management ecosystem. Algorithms to track Data Anomalies in Azure, GCP & AWS databases. Measure and monitor data issues from all your cloud database management tools and data silos, using a single, centralized tool. Quality is in the eye of the beholder. Data issues have different implications depending on where you sit in the enterprise. Qualdo is a pioneer in organizing all data quality management issues through the lens of multiple enterprise stakeholders, presenting a unified view in a consumable format. Deploy powerful auto-resolution algorithms to track and isolate critical data issues. Take advantage of robust reports and alerts to manage your enterprise regulatory compliance.

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.

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.

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.

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.

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.

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.

A new category of AI. Most current AI solutions are engineered through a statistical and purely mathematical lens. We took a different approach. With discoveries in information theory, the team at Intelligent Artifacts has built a new category of AI: a true AGI that eliminates current machine intelligence shortcomings. Our framework keeps the data and application layers separate from the intelligence layer allowing it to learn in real-time, and enabling it to explain predictions down to root cause. A true AGI demands a truly integrated platform. With Intelligent Artifacts, you'll model information, not data — predictions and decisions are real-time and transparent, and can be deployed across various domains without the need to rewrite code. And by combining specialized AI consultants with our dynamic platform, you'll get a customized solution that rapidly offers deep insights and greater outcomes from your data.

Train accurate ML models on limited, sparse, and high-dimensional data without extensive feature engineering by creating statistically optimized representations of your data. By learning how to extract and represent complex relationships in your existing data, Dark Matter improves model performance and speeds up training without extensive feature engineering or resource-intensive deep learning, enabling data scientists to spend less time on data and more time-solving hard problems. Dark Matter significantly improved model precision and f1 scores in predicting customer conversion in the online retail space. Model performance metrics improved across the board when trained on an optimized embedding learned from a sparse, high-dimensional data set. Training XGBoost on a better representation of the data improved predictions of customer churn in the banking industry. Enhance your pipeline, no matter your model or domain.

Wallaroo facilitates the last-mile of your machine learning journey, getting ML into your production environment to impact the bottom line, with incredible speed and efficiency. Wallaroo is purpose-built from the ground up to be the easy way to deploy and manage ML in production, unlike Apache Spark, or heavy-weight containers. ML with up to 80% lower cost and easily scale to more data, more models, more complex models. Wallaroo is designed to enable data scientists to quickly and easily deploy their ML models against live data, whether to testing environments, staging, or prod. Wallaroo supports the largest set of machine learning training frameworks possible. You’re free to focus on developing and iterating on your models while letting the platform take care of deployment and inference at speed and scale.

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.

The Descartes Labs Platform is designed to answer some of the world’s most complex and pressing geospatial analytics questions. Our customers use the platform to build algorithms and models that transform their businesses quickly, efficiently, and cost-effectively. By giving data scientists and their line-of-business colleagues the best geospatial data and modeling tools in one package, we help turn AI into a core competency. Data science teams can use our scaling infrastructure to design models faster than ever, using our massive data archive or their own. Customers rely on our cloud-based platform to quickly and securely scale computer vision, statistical, and machine learning models to inform business decisions with powerful raster-based analytics. Our extensive API documentation, tutorials, guides and demos provide a deep knowledge base for users allowing them to quickly deploy high-value applications across diverse industries.

Access, manipulate, analyze and present information in visual formats using a powerful combination of SAS technologies. With SAS Visual Machine Learning, you can broaden your analytics with machine learning and deep learning capabilities that are accessible across your organization for better visualization and reporting. Visualize and discover relevant relationships in your data. Create and share interactive reports and dashboards, and use self-service analytics to quickly assess probable outcomes for smarter, more data-driven decisions. Explore data and build or adjust predictive analytical models with this solution running in SAS® Viya®. Data scientists, statisticians, and analysts can collaborate and iteratively refine models for each segment or group to make decisions based on accurate insights. Solve complex analytical problems with a comprehensive visual interface that handles all tasks in the analytics life cycle.

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.

Scale your machine learning development from research to production with an end-to-end solution that gives your data science team all the tools they need in one place. As the leading data science platform for MLOps and model management, cnvrg.io is a pioneer in building cutting-edge machine learning development solutions so you can build high-impact machine learning models in half the time. Bridge science and engineering teams in a clear and collaborative machine learning management environment. Communicate and reproduce results with interactive workspaces, dashboards, dataset organization, experiment tracking and visualization, a model repository and more. Focus less on technical complexity and more on building high impact ML models. Cnvrg.io container-based infrastructure helps simplify engineering heavy tasks like tracking, monitoring, configuration, compute resource management, serving infrastructure, feature extraction, and model deployment.

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.

Skan is a cognitive technology startup redefining business process discovery to empower large enterprises to uncover, untangle & unleash their business processes. Skan’s offering accelerates and optimizes intelligent automation, digital transformation, and helps define the future of work. Skan’s unique approach leverages computer vision, deep learning & machine intelligence to observe, learn, assemble, and optimize business processes without intrusion or integration. The output in the form of a process metamodel & digital process twins makes it a breeze to model, simulate, and measure the future state in a sandbox. Skan’s founding team is a blend of entrepreneurs, technologists, data scientists & AI experts – all well versed in the challenges and opportunities at large enterprises with complex business and IT landscapes. The genesis of Skan is rooted in the practical experience of our founders while working on automation and transformation projects for Fortune 500 companies

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.

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.

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.

Amazon SageMaker Clarify provides machine learning (ML) developers with purpose-built tools to gain greater insights into their ML training data and models. SageMaker Clarify detects and measures potential bias using a variety of metrics so that ML developers can address potential bias and explain model predictions. SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model. For instance, you can check for bias related to age in your dataset or in your trained model and receive a detailed report that quantifies different types of potential bias. SageMaker Clarify also includes feature importance scores that help you explain how your model makes predictions and produces explainability reports in bulk or real time through online explainability. You can use these reports to support customer or internal presentations or to identify potential issues with your model.

DataProphet specializes in optimizing the complex manufacturing processes of key industrial verticals with state-of-the-art machine learning. Our AI-driven solutions leverage the existing data streams from your plant’s production line equipment to identify process efficiencies. High-impact adjustments are then prescribed, which guarantee ROI in the first year of deployment. In 21st-century manufacturing plants, excellence is achieved when production issues are pre-empted. Before latent problems manifest on the production line, operators need immediate access to actionable insights about part quality, production performance, and machine availability. For today’s manufacturers, real-time troubleshooting is too late. Explore how DataProphet’s AI-driven prescriptions can help you improve quality targets, reduce scrap and defects, and achieve manufacturing process optimization—ahead of real-time.

Train the most demanding AI, ML, and Deep Learning models. Scale from a single machine to an entire fleet of VMs with a few clicks. Start or scale up your Deep Learning project with Lambda Cloud. Get started quickly, save on compute costs, and easily scale to hundreds of GPUs. Every VM comes preinstalled with the latest version of Lambda Stack, which includes major deep learning frameworks and CUDA® drivers. In seconds, access a dedicated Jupyter Notebook development environment for each machine directly from the cloud dashboard. For direct access, connect via the Web Terminal in the dashboard or use SSH directly with one of your provided SSH keys. By building compute infrastructure at scale for the unique requirements of deep learning researchers, Lambda can pass on significant savings. Benefit from the flexibility of using cloud computing without paying a fortune in on-demand pricing when workloads rapidly increase.

Censius is an innovative startup in the machine learning and AI space. We bring AI observability to enterprise ML teams. Ensuring that ML models' performance is in check is imperative with the extensive use of machine learning models. Censius is an AI Observability Platform that helps organizations of all scales confidently make their machine-learning models work in production. The company launched its flagship AI observability platform that helps bring accountability and explainability to data science projects. A comprehensive ML monitoring solution helps proactively monitor entire ML pipelines to detect and fix ML issues such as drift, skew, data integrity, and data quality issues. Upon integrating Censius, you can: 1. Monitor and log the necessary model vitals 2. Reduce time-to-recover by detecting issues precisely 3. Explain issues and recovery strategies to stakeholders 4. Explain model decisions 5. Reduce downtime for end-users 6. Build customer trust

ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers. Develop in your preferred framework without worrying about downstream inferencing implications. ONNX enables you to use your preferred framework with your chosen inference engine. ONNX makes it easier to access hardware optimizations. Use ONNX-compatible runtimes and libraries designed to maximize performance across hardware. Our active community thrives under our open governance structure, which provides transparency and inclusion. We encourage you to engage and contribute.

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.

Extract, normalize, and standardize your data across multiple inputs and formats. Normalize all your information – including business and individual data, structured and unstructured. Precisely applies supervised machine learning neural network-based techniques to understand the structure and variations of different types of information and parses data automatically. Spectrum Quality is ideally suited for global client bases that require multi-level data standardization and transliteration for multiple languages and culturally specific terms, including those in Arabic, Chinese, Japanese and Korean. Our advanced text-processing enables information extraction from any natural language input text and assigns categories to unstructured text. Using pre-trained models and machine learning based algorithms, you can extract entities and further train and customize your models to define specific entities of any domain or type.

Comprehensive high-quality IP data for patent analytics. 136 million patents from 100+ countries. Trusted by world-class brands and organizations of all sizes. XLSCOUT engineered data with best-in-class artificial intelligence technologies to develop the most accurate, comprehensive, and intelligent patent & research publication database. Using Natural Language Processing (NLP), Machine Learning (ML), and innovation/scientific principles, XLSCOUT gives you more time and reliable insights to confidently make data-driven strategic decisions. Drafting LLM is a cutting-edge patent application drafting platform that utilizes Large Language Models (LLMs) & Generative AI for drafting top-tier preliminary patent drafts. Novelty Checker LLM swifts through patent and non-patent literature, delivering a comprehensive list of ranked prior art references along with a key feature analysis report.

Cleanlab Studio handles the entire data quality and data-centric AI pipeline in a single framework for analytics and machine learning tasks. Automated pipeline does all ML for you: data preprocessing, foundation model fine-tuning, hyperparameter tuning, and model selection. ML models are used to diagnose data issues, and then can be re-trained on your corrected dataset with one click. Explore the entire heatmap of suggested corrections for all classes in your dataset. Cleanlab Studio provides all of this information and more for free as soon as you upload your dataset. Cleanlab Studio comes pre-loaded with several demo datasets and projects, so you can check those out in your account after signing in.