Superwise Alternatives

Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection.

Go beyond compliance and build trust through transparency, choice, and control. People demand greater control of their data, unlocking an opportunity for organizations to use these moments to build trust and deliver more valuable experiences. We provide privacy and data governance automation to help organizations better understand their data across the business, meet regulatory requirements, and operationalize risk mitigation to provide transparency and choice to individuals. Achieve data privacy compliance faster and build trust in your organization. Our platform helps break down silos across processes, workflows, and teams to operationalize regulatory compliance and enable trusted data use. Build proactive privacy programs rooted in global best practices, not reactive to individual regulations. Gain visibility into unknown risks to drive mitigation and risk-based decision making. Respect individual choice and embed privacy and security by default into the data lifecycle.

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.

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.

The biggest challenge to scaling AI-powered decision-making is unused data. IBM Cloud Pak® for Data is a unified platform that delivers a data fabric to connect and access siloed data on-premises or across multiple clouds without moving it. Simplify access to data by automatically discovering and curating it to deliver actionable knowledge assets to your users, while automating policy enforcement to safeguard use. Further accelerate insights with an integrated modern cloud data warehouse. Universally safeguard data usage with privacy and usage policy enforcement across all data. Use a modern, high-performance cloud data warehouse to achieve faster insights. Empower data scientists, developers and analysts with an integrated experience to build, deploy and manage trustworthy AI models on any cloud. Supercharge analytics with Netezza, a high-performance data warehouse.

Enable observability to detect data and ML issues faster, deliver continuous improvements, and avoid costly incidents. Start with reliable data. Continuously monitor any data-in-motion for data quality issues. Pinpoint data and model drift. Identify training-serving skew and proactively retrain. Detect model accuracy degradation by continuously monitoring key performance metrics. Identify risky behavior in generative AI applications and prevent data leakage. Protect your generative AI applications are safe from malicious actions. Improve AI applications through user feedback, monitoring, and cross-team collaboration. Integrate in minutes with purpose-built agents that analyze raw data without moving or duplicating it, ensuring privacy and security. Onboard the WhyLabs SaaS Platform for any use cases using the proprietary privacy-preserving integration. Security approved for healthcare and banks.

Fiddler is a pioneer in Model Performance Management for responsible AI. The Fiddler platform’s unified environment provides a common language, centralized controls, and actionable insights to operationalize ML/AI with trust. Model monitoring, explainable AI, analytics, and fairness capabilities address the unique challenges of building in-house stable and secure MLOps systems at scale. Unlike observability solutions, Fiddler integrates deep XAI and analytics to help you grow into advanced capabilities over time and build a framework for responsible AI practices. Fortune 500 organizations use Fiddler across training and production models to accelerate AI time-to-value and scale, build trusted AI solutions, and increase revenue.

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.

While not all models are created equal, every model needs governance to drive responsible and ethical decision-making throughout the business. IBM® watsonx.governance™ toolkit for AI governance allows you to direct, manage and monitor your organization’s AI activities. It employs software automation to strengthen your ability to mitigate risks, manage regulatory requirements and address ethical concerns for both generative AI and machine learning (ML) models. Access automated and scalable governance, risk and compliance tools that cover operational risk, policy management, compliance, financial management, IT governance and internal or external audits. Proactively detect and mitigate model risks while translating AI regulations into enforceable policies for automatic enforcement.

An AI governance platform designed by lawyers with regulatory expertise, tailored to your use cases and policies. Businesses must learn to navigate and comply with new legislation and guidelines. Organizations risk losing customer trust and a breakdown in product engagement if AI malfunctions. Teams must deal with increasingly complex AI systems, with more use cases than ever. Monitor compliance of your AI systems through our assessments and live model controls. Alert users to mitigate potential issues or risks. Implementing good AI governance practices can be time-consuming. Leverage built-in automation to import model data and artifacts, and review and update documentation. Understand AI compliance across your organization. Provide senior stakeholders with the full picture of their AI compliance to make strategic decisions and share reports for curated audiences. We offer a complete set of policies that ensure legal and regulatory compliance through pre-configured assessments.

FairNow equips organizations with all the AI governance tools they need to ensure global compliance and manage AI risk. Loved by CPOs, CAIOs, risk management, and legal professionals, FairNow's features are simplified, centralized, and empowering for the entire team. FairNow’s platform continuously monitors AI models to ensure that every model is fair, compliant, and audit-ready. Top features include: - Intelligent AI Risk Assessments: Conduct real-time assessments of AI models, using their deployment locations to highlight possible reputational, financial, and operational risks. - Hallucination Detection: Proactively detect errors and unexpected answers. - Automated Bias Evaluations: Automate bias evaluations and mitigate algorithmic bias as it happens. Plus: - AI Inventory - Centralized Policy Center - Roles and Controls FairNow’s AI governance platform helps organizations build, buy, and deploy AI with complete confidence.

Standardize your AI governance efforts across diverse stakeholders, ensure regulatory readiness of your governance processes, and measure and manage your AI risks and compliance. Go from fragmented teams and processes to a centralized repository of trusted governance that makes it easy to ensure all of your AI/ML projects are being governed effectively. Stay up-to-date with the latest regulations and standards with AI Policy Packs that meet current and emerging regulations. Credo AI is an intelligence layer that sits on top of your AI infrastructure and translates technical artifacts into actionable risk & compliance insights for product leaders, data scientists, and governance teams. Credo AI is an intelligence layer that sits on top of your technical and business infrastructure and translates technical artifacts into risk and compliance scores.

Creating responsible AI is a business problem, not just a tech problem. We solve for the whole problem by bringing teams together onto one platform to mitigate risk, leverage your full potential, and turn intention into action. Uniting every stage of your AI/ML journey with cloud-based governance applications. GovernML is the kickstarter you need to bring good AI/ML systems into the world. We bring user-friendly workflows that document the lifecycle of your AI journey on one platform. That’s good news for your risk mitigation and your bottom line. Monitaur provides cloud-based governance applications that track your AI/ML models from policy to proof. We are SOC 2 Type II-certified to enhance your AI governance and deliver bespoke solutions on a single unifying platform. GovernML brings responsible AI/ML systems into the world. Get scalable, user-friendly workflows that document the lifecycle of your AI journey on one platform.

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.

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.

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.

AI and non-AI models need risk management and oversight. Fairly provides a continuous monitoring system for advanced model governance and oversight. With Fairly, risk and compliance teams can collaborate with data science and cyber security teams easily to ensure models are reliable and secure. Fairly makes it easy to stay up-to-date with policies and regulations for procurement, validation and audit of non-AI, predictive AI and generative AI models. Fairly simplifies the model validation and auditing process with direct access to the ground truth in a controlled environment for in-house and third-party models, without adding overhead to development and IT teams. Fairly's platform ensures compliant, secure, and ethical models. Fairly helps teams identify, assess, monitor, report and mitigate compliance, operational and model risks according to internal policies and external regulations.

Take your data-driven enterprise to the next level with augmented intelligence. From algorithms to architecture, BasisAI manages the end to end development of real-time, scalable and responsible AI systems. Complete lifecycle AI solution: from bespoke AI algorithms, to production-grade AI application, and ongoing multi-year management. Rapid time to market: from your data to scalable, containerised deployment of real-time AI engine in weeks. No black boxes. Built-in AI governance, fairness and compliance. Retain control over your enterprise data, whether it is on AWS, GCP or another cloud infrastructure. We provide strategic guidance in building the right structures, frameworks and technologies, giving you a path to long-term scalability. Through exploratory use case sessions and system design workshops, we help you to move beyond experiments and algorithms and have control over your own capability development.

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.

Mind Foundry is an artificial intelligence company operating at the intersection of research, innovation, and usability to empower teams with AI that is built for humans. Founded by world-leading academics, Mind Foundry develops AI solutions that help organisations in the public and private sectors tackle high-stakes problems, focusing on human outcomes and the long-term impact of AI interventions. Our intrinsically collaborative platform powers AI design, testing and deployment and enables stakeholders to manage their AI investment responsibly with key focus on performance, efficiency and ethical impact. Built on a cornerstone of scientific principles and an understanding that you can’t add things like ethics and transparency after the fact. The fusion of experience design and quantitative methods that makes collaboration between humans and AI more intuitive, efficient and powerful.

Quality assurance for machine learning simplified. Snitch removes the noise to surface only the most useful information to improve your models. Track your model’s performance beyond just accuracy with powerful dashboards and analysis. Identify problems in your data pipeline and distribution shifts before they affect your predictions. Stay in production once you’ve deployed and gain visibility on your models & data throughout its cycle. Keep your data secure, cloud, on-prem, private cloud, hybrid, and you decide how to install Snitch. Work within the tools you love and integrate Snitch into your MLops pipeline! Get up and running quickly, we keep installation, learning, and running the product easy as pie. Accuracy can often be misleading. Look into robustness and feature importance to evaluate your models before deploying. Gain actionable insights to improve your models. Compare against historical metrics and your models’ baseline.

With Amazon SageMaker Model Monitor, you can select the data you would like to monitor and analyze without the need to write any code. SageMaker Model Monitor lets you select data from a menu of options such as prediction output, and captures metadata such as timestamp, model name, and endpoint so you can analyze model predictions based on the metadata. You can specify the sampling rate of data capture as a percentage of overall traffic in the case of high volume real-time predictions, and the data is stored in your own Amazon S3 bucket. You can also encrypt this data, configure fine-grained security, define data retention policies, and implement access control mechanisms for secure access. Amazon SageMaker Model Monitor offers built-in analysis in the form of statistical rules, to detect drifts in data and model quality. You can also write custom rules and specify thresholds for each rule.

Track model performance to detect and react to data drift, improving model accuracy for better business outcomes. Build trust, ensure compliance, and drive more actionable ML outcomes with Arthur’s explainability and transparency APIs. Proactively monitor for bias, track model outcomes against custom bias metrics, and improve the fairness of your models. See how each model treats different population groups, proactively 
identify bias, and use Arthur's proprietary bias mitigation techniques. Arthur scales up and down to ingest up to 1MM transactions 
per second and deliver insights quickly. Actions can only be performed by authorized users. Individual teams/departments can have isolated environments with specific access control policies. Data is immutable once ingested, which prevents manipulation of metrics/insights.

ModelOp is the leading AI governance software that helps enterprises safeguard all AI initiatives, including generative AI, Large Language Models (LLMs), in-house, third-party vendors, embedded systems, etc., without stifling innovation. Corporate boards and C‑suites are demanding the rapid adoption of generative AI but face financial, regulatory, security, privacy, ethical, and brand risks. Global, federal, state, and local-level governments are moving quickly to implement AI regulations and oversight, forcing enterprises to urgently prepare for and comply with rules designed to prevent AI from going wrong. Connect with AI Governance experts to stay informed about market trends, regulations, news, research, opinions, and insights to help you balance the risks and rewards of enterprise AI. ModelOp Center keeps organizations safe and gives peace of mind to all stakeholders. Streamline reporting, monitoring, and compliance adherence across the enterprise.

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.

Deploy all major ML frameworks such as Pytorch, Onnx, XGBoost etc with 1 line of code. Don't have your own models? Deploy our prebuilt models that have been optimised to run with sub-second latency. Fine-tune smaller models on particular tasks in order to decrease costs and latency while increasing performance. It takes just a few lines of code and don't worry about infrastructure, we got it. Integrate with top ML observability platforms in order to be alerted about feature or prediction drift, compare model versions and resolve issues quickly. Discover the root causes for prediction and feature drift to resolve degraded model performance. Understand which features are contributing most to the performance of your model.

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

NuEnergy.ai’s Machine Trust Platform™ (MTP) is a unique cloud-based software platform to measure, monitor, and build trust in your artificial intelligence (AI) technology, whether you’re developing it, procuring it, or deploying it. MTP measures essential trust parameters including privacy, ethics, transparency, and bias metrics and protects against the risks of AI drift. The software lets you assess your AI technologies against key risk-mitigating metrics, third-party frameworks, and standards to help ensure governance, ethical and regulatory guidelines are met. It offers flexibility and scope for configurations to ensure you have compatibility to enhance trust in your AI technologies. Global standards, including the Government of Canada Algorithmic Impact Assessment (AIA), are integrated into the platform, which can be customized to include other relevant governance standards. You can choose from an ever-growing list of NuEnergy.ai’s library of qualified AI trust tools.

IBM Watson OpenScale is an enterprise-scale environment for AI-powered applications that provides businesses with visibility into how AI is created and used, and how ROI is delivered. IBM Watson OpenScale is an enterprise-scale environment for AI-powered applications that provides companies with visibility into how AI is created and used, and how ROI is delivered at the business level. Create and develop trusted AI using the IDE of your choice and power your business and support teams with data insights into how AI affects business results. Capture payload data and deployment output to monitor the ongoing health of business applications through operations dashboards, alerts, and access to open data warehouse for custom reporting. Automatically detects when artificial intelligence systems deliver the wrong results at run time, based on business-determined fairness attributes. Mitigate bias through smart recommendations of new data for new model training.

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.

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.

Get scores for factual accuracy, context retrieval quality, guideline adherence, tonality, and many more. You can’t improve what you can’t measure. UpTrain continuously monitors your application's performance on multiple evaluation criterions and alerts you in case of any regressions with automatic root cause analysis. UpTrain enables fast and robust experimentation across multiple prompts, model providers, and custom configurations, by calculating quantitative scores for direct comparison and optimal prompt selection. Hallucinations have plagued LLMs since their inception. By quantifying degree of hallucination and quality of retrieved context, UpTrain helps to detect responses with low factual accuracy and prevent them before serving to the end-users.

The only all-in-one data pipeline platform. Built ground-up for a cloud-first world, Gathr is the only platform to handle all your data integration and engineering needs - ingestion, ETL, ELT, CDC, streaming analytics, data preparation, machine learning, advanced analytics and more. With Gathr, anyone can build and deploy pipelines in minutes, irrespective of skill levels. Create Ingestion pipelines in minutes, not weeks. Ingest data from any source, deliver to any destination. Build applications quickly with a wizard-based approach. Replicate data in real-time using a templatized CDC app. Native integration for all sources and targets. Best-in-class capabilities with everything you need to succeed today and tomorrow. Choose between free, pay-per-use or customize as per your requirements.

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.

Gain complete visibility into the performance of your data, models, and processes with the most flexible monitoring solution. Automatically surface and resolve performance issues within your AI/ML or intelligent automation processes to avoid negative impacts on both your business and customers. Learning how your data, models, and processes perform in the real world is critical to continuously improving your processes. Monitoring is the ‘eyes and ears' needed to observe your data and workflows to tell you if they’re performing well. Mona exhaustively analyzes your data to provide actionable insights based on advanced anomaly detection mechanisms, to alert you before your business KPIs are hurt. Take stock of any part of your production workflows and business processes, including models, pipelines, and business outcomes. Whatever datatype you work with, whether you have a batch or streaming real-time processes, and for the specific way in which you want to measure your performance.

The Domino platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record allows teams to easily find, reuse, reproduce, and build on any data science work to amplify innovation.

Data Driven Digital Twin toolset to help you Visualize, Monitor, and Optimize your operation in Real-Time using machine learning and AI. Control your Cost of SKU, Resources, Automation, Equipment and more. Real-Time Visibility and Traceability - Digital Twin Shadow Technology. Digital Twin Studio® Open Architecture enables it to interact with a multitude of RTLS and data systems – RFID, BarCode, GPS, PLC, WMS, EMR, ERP, MRP, or RTLS systems Digital Twin with AI and Machine Learning - Predictive Analytics and Dynamic Scheduling Real-time predictive analytics deliver insights via notifications when issues are identified before they occur with state of the art Digital Twin Technology Digital Twin Replay - View Past Events and Setup Active Alerts. Digital Twin Studio can replay and animate all past events through 2D, 3D and VR. Digital Twin Live Real-Time Metrics - Dynamic Dashboards - Simple drag and drop dashboard builder with unlimited layout capabilities.

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.

Detect machine issues before they occur with machine learning (ML), and take action. Start monitoring equipment in minutes with easy installation and automatic, secure analysis through the Amazon Monitron end-to-end system. Improve system accuracy continuously as Amazon Monitron learns from technician feedback entered in the mobile and web apps. Amazon Monitron is an end-to-end system that uses machine learning to detect abnormal conditions in industrial equipment and enable predictive maintenance. Save on costly repairs and prevent factory equipment downtime with easy-to-install hardware and the power of ML. Reduce unplanned equipment downtime with predictive maintenance and machine learning. Amazon Monitron uses machine learning on temperature and vibration data. Amazon Monitron can help you predict equipment downtime before it happens. Compare what it costs to get started with how much you could save.

Select the configuration and resources needed for specific code segments in your ongoing project. It takes seconds to apply changes within a training scenario and save the work result. Choose the right configuration for computing resources to start training models in just a few seconds. Everything will be created automatically with no need to manage infrastructure. Choose an operating mode: serverless or dedicated. Manage project data, save it to datasets, and set up connections to databases, object storage, or other repositories, all in one interface. Collaborate with colleagues around the world to create an ML model, share the project, and set budgets for teams across your organization. Launch your ML in minutes, without the help of developers. Run experiments with simultaneous publication of different versions of models.

A frustratingly slow process requiring development resources or know-how, resulting in most models never seeing the light of day. Ship full-stack apps in minutes. Deploy models instantly, automatically generate API endpoints, and quickly build UI with drag-and-drop components. You shouldn’t need to become a DevOps engineer to get models into production. With Baseten, you can instantly serve, manage, and monitor models with a few lines of Python. Assemble business logic around your model and sync data sources without the infrastructure headaches. Start immediately with sensible defaults, and scale infinitely with fine-grained controls when you need them. Read and write to your existing data stores or with our built-in Postgres database. Create clear, engaging interfaces for business users with headings, callouts, dividers, and more.

Proven commercial value in weeks, for high value use cases across all major verticals. ElectrifAi has the largest library of pre-built machine learning models that seamlessly integrate into existing workflows to provide fast and reliable results. Get our domain expertise through pre-trained, pre-structured, or brand-new models. Building machine learning is risky and time-consuming. ElectrifAi delivers superior, fast and reliable results with over 1,000 ready-to-deploy machine learning models that seamlessly integrate into existing workflows. With comprehensive capabilities to deploy proven ML models, we bring you solutions faster. We make the machine learning models, complete the data ingestion and clean up the data. Our domain experts use your existing data to train the selected model that works best for your use case.

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.

Building a model is an iterative process that can take weeks, months, or even years, and reproducing model results, maintaining version control, and auditing past work are complex. Model building is an iterative process. Ideally, you record not only each step but also how you arrived there. A model shouldn’t be a file hidden away somewhere, but instead a tangible object that all parties can track and analyze consistently. Prevision.io allows you to record each experiment as you train it along with its characteristics, automated analyses, and versions as your project progress, whether you created it using our AutoML or your own tools. Automatically experiment with dozens of feature engineering strategies and algorithm types to build highly performant models. In a single command, the engine automatically tries out different feature engineering strategies for every type of data (e.g. tabular, text, images) to maximize the information in your datasets.

BryteFlow builds the most efficient automated environments for analytics ever. It converts Amazon S3 into an awesome analytics platform by leveraging the AWS ecosystem intelligently to deliver data at lightning speeds. It complements AWS Lake Formation and automates the Modern Data Architecture providing performance and productivity. You can completely automate data ingestion with BryteFlow Ingest’s simple point-and-click interface while BryteFlow XL Ingest is great for the initial full ingest for very large datasets. No coding is needed! With BryteFlow Blend you can merge data from varied sources like Oracle, SQL Server, Salesforce and SAP etc. and transform it to make it ready for Analytics and Machine Learning. BryteFlow TruData reconciles the data at the destination with the source continually or at a frequency you select. If data is missing or incomplete you get an alert so you can fix the issue easily.

Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models. You simply provide a tabular dataset and select the target column to predict, and SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click or iterate on the recommended solutions to further improve the model quality. You can use Amazon SageMaker Autopilot even when you have missing data. SageMaker Autopilot automatically fills in the missing data, provides statistical insights about columns in your dataset, and automatically extracts information from non-numeric columns, such as date and time information from timestamps.

Make AI a trusted part of your business. Build better, deploy faster, and audit easily with the flexibility of a cloud-native platform and the power to deploy anywhere. Easily import models and search cataloged models from across your organization. Save time by annotating data rapidly with model-in-the-loop hinting. Understand the full provenance of your data, models, workflows, and inferences. Deploy models where you need them, including for edge and IoT use cases. Getting valuable insights from your data is not just for data scientists. With Chariot’s low-code interface, meaningful collaboration can take place across teams. Train models rapidly using your organization's production data. Deploy models with one click and monitor models in production at scale.

Empowering the enterprise to do real data science at speed and scale with a full-featured machine learning platform. Spend less time managing tools and infrastructure, so you can focus on building machine learning applications that move your business forward. Anaconda Enterprise takes the headache out of ML operations, puts open-source innovation at your fingertips, and provides the foundation for serious data science and machine learning production without locking you into specific models, templates, or workflows. Software developers and data scientists can work together with AE to build, test, debug, and deploy models using their preferred languages and tools. AE provides access to both notebooks and IDEs so developers and data scientists can work together more efficiently. They can also choose from example projects and preconfigured projects. AE projects are automatically containerized so they can be moved between environments with ease.

Our Natural Language Processing(NLP) APIs provide best-in-class accuracy at an affordable rate and cover all aspects of NLP under a single roof. Save weeks of time training and creating language models. Take advantage of our best-in-class APIs to kickstart your app development. We provide the building blocks to create your own apps effectively like chatbots, sentiment analysis, etc. Text classification on multiple domains and 100+ languages. Perform effective sentiment analysis. We grow when your business does. So we have put together simple pricing that allows you to easily scale your business when it needs to evolve. Perfect for individual developers who are creating apps or building proof of concepts. Head to the Dashboard and get your API Key. Place this in the header of all your API calls. Use our SDK in your preferred language to start coding. Or you can refer to the auto-generated code blocks provided in 18 programming languages.

Get Rasgo in your Python environment with PyRasgo, our open source Python library, or get the full Rasgo experience in our powerful and beautifully designed UI. Generate intuitive and detailed feature profiles directly in your panda’s dataframe or in the Rasgo UI. Analyze key data statistics, data quality issues, data drift, and value distribution. Prune and select features to reach a final set of modeling ready features for your model training or inference workflow. Leverage our exhaustive library of feature transformation functions to turn your raw data into useful features. Visualize critical insights, such as feature importance, feature explainability, and feature correlation, before you spend time training your model. Work collaboratively with your colleagues to create feature collections or duplicate existing feature collections to customize for your model.