Alternatives to KuantSol
Compare KuantSol alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to KuantSol in 2026. Compare features, ratings, user reviews, pricing, and more from KuantSol competitors and alternatives in order to make an informed decision for your business.
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Fraud.net
Fraud.net, Inc.
Fraudnet's AI-driven platform empowers enterprises to prevent threats, streamline compliance, and manage risk in real-time. Our sophisticated machine learning models continuously learn from billions of transactions to identify anomalies and predict fraud attacks. Our unified solutions: comprehensive screening for smoother onboarding & improved compliance, continuous monitoring to proactively identify new threats, & precision fraud detection across channels and payment types. With dozens of data integrations and advanced analytics, you'll dramatically reduce false positives while gaining unmatched visibility. And, with no-code/low-code integration, our solution scales effortlessly as you grow. The results speak volumes: Leading payments companies, financial institutions, innovative fintechs, and commerce brands trust us worldwide—and they're seeing dramatic results: 80% reduction in fraud losses and 97% fewer false positives. Request your demo today and discover Fraudnet. -
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TiMi
TIMi
With TIMi, companies can capitalize on their corporate data to develop new ideas and make critical business decisions faster and easier than ever before. The heart of TIMi’s Integrated Platform. TIMi’s ultimate real-time AUTO-ML engine. 3D VR segmentation and visualization. Unlimited self service business Intelligence. TIMi is several orders of magnitude faster than any other solution to do the 2 most important analytical tasks: the handling of datasets (data cleaning, feature engineering, creation of KPIs) and predictive modeling. TIMi is an “ethical solution”: no “lock-in” situation, just excellence. We guarantee you a work in all serenity and without unexpected extra costs. Thanks to an original & unique software infrastructure, TIMi is optimized to offer you the greatest flexibility for the exploration phase and the highest reliability during the production phase. TIMi is the ultimate “playground” that allows your analysts to test the craziest ideas! -
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Synario
PFM Solutions
Synario is an industry-leading financial modeling platform designed to answer tomorrow's questions today. Make data-informed decisions with confidence, leading your organization to a brighter financial future with Synario. Leave archaic spreadsheet-based financial modeling behind and switch to a purpose-built modeling platform where advanced modeling, analysis, and insight come out-of-the-box. With automated financial statements and patented layering technology, Synario can give your finance team a full-field view of your financial future. Reach out to us to see how Synario could benefit your unique organization. Synario contains the best financial modeling tools to analyze even the most complex strategic or financial scenarios. Coupled with complete customizability, Synario can create all types of financial models with ease. Patented layering technology makes Synario financial models better able to replicate any scenario. -
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Fiddler AI
Fiddler AI
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. -
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Clindata Cloud
Clinical Data
Clindata Cloud receives pre-clinical / clinical / Risk Metric data from multiple data sources/sites, and empowers the clinical operations teams, with submission-ready data sets, analytics and risk-based monitoring alerts. Consolidate & harmonize study data from multiple data sources into a comprehensive study data model. Validate received data for completeness, accuracy, integrity and consistency and raise alerts and notifications in case of exceptions or risk patterns. Standardize data to CDISC data standards, to eliminate noise and create submission-ready data sets in real-time for continuous validation of data & analysis. Generate submission-ready analytics in real-time based on standardized data. -
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Amazon SageMaker Clarify
Amazon
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. -
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Grace Enterprise AI Platform
2021.AI
The Grace Enterprise AI Platform, the AI platform with full support for Governance, Risk & Compliance (GRC) for AI. Grace offers an efficient, secure, and robust AI implementation across any organization, standardizing processes, and workflows across all your AI projects. Grace covers the full range of rich functionality your organization needs to become fully AI proficient and helps ensure regulatory excellence for AI, to avoid compliance requirements slowing or stopping AI implementation. Grace lowers the entry barriers for AI users across all functional and operational roles in your organization, including technical, IT, project management, and compliance, while still offering efficient workflows for experienced data scientists and engineers. Ensuring that all activities are traced, explained, and enforced. This includes all areas within the data science model development, data used for model training and development, model bias, and more. -
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Scienaptic
Scienaptic AI
Our platform has pre-built APIs for both traditional and alternative credit data sources to accelerate data ingestion for sharper credit decisions. Powerful predictor library based on years of credit experience; pre-configured features that drive better credit decisions. Fully explainable proprietary AI, ML credit modeling methodology that delivers significant lift. Run multiple champion challengers concurrently. Credit models and strategies in one unified workflow. Rapid deployment of new credit models and strategies. Our AI credit underwriting models are explainable, FCRA-compliant, and robust. They come with simplified and automated adverse action reasoning. Comprehensive documentation on logic underlying model, robustness, and limitations of model. Model attributes go through thorough disparate impact analysis to ensure no bias in design. Our AI models provide rich and diverse set of adverse action reasons. -
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Hawk
Hawk.ai
Hawk AI combines AI with traditional rule-based approaches to monitor financial transactions in real-time, delivering next-generation anti-money laundering compliance for financial institutions. The solution offers classic rule-based models, which are enhanced by auto-closing features based on machine learning models that learn from the investigator’s own decisions through our case manager. Hawk AI uses an unsupervised machine learning model, Anomaly Detection, to identify new patterns of crime through insights from the overarching nature of the platform spanning multiple financial institutions. The platform provides full transparency of machine decisions to deliver the necessary clarity for regulators that require “explainable AI”, as well as instill trust in the machine's decisions. Using Artificial Intelligence to maximize automation, Hawk AI delivers a significant cost benefit through an up to 70% reduction of required resources. -
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CognitiveScale Cortex AI
CognitiveScale
Developing AI solutions requires an engineering approach that is resilient, open and repeatable to ensure necessary quality and agility is achieved. Until today these efforts are missing the foundation to address these challenges amid a sea of point tools and fast changing models and data. Collaborative developer platform for automating development and control of AI applications across multiple personas. Derive hyper-detailed customer profiles from enterprise data to predict behaviors in real-time and at scale. Generate AI-powered models designed to continuously learn and achieve clearly defined business outcomes. Enables organizations to explain and prove compliance with applicable rules and regulations. CognitiveScale's Cortex AI Platform addresses enterprise AI use cases through modular platform offerings. Our customers consume and leverage its capabilities as microservices within their enterprise AI initiatives. -
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Altair Knowledge Studio
Altair
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. -
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Oracle Data Science
Oracle
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. -
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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.
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Graviti
Graviti
Unstructured data is the future of AI. Unlock this future now and build an ML/AI pipeline that scales all of your unstructured data in one place. Use better data to deliver better models, only with Graviti. Get to know the data platform that enables AI developers with management, query, and version control features that are designed for unstructured data. Quality data is no longer a pricey dream. Manage your metadata, annotation, and predictions in one place. Customize filters and visualize filtering results to get you straight to the data that best match your needs. Utilize a Git-like structure to manage data versions and collaborate with your teammates. Role-based access control and visualization of version differences allows your team to work together safely and flexibly. Automate your data pipeline with Graviti’s built-in marketplace and workflow builder. Level-up to fast model iterations with no more grinding. -
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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
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KServe
KServe
Highly scalable and standards-based model inference platform on Kubernetes for trusted AI. KServe is a standard model inference platform on Kubernetes, built for highly scalable use cases. Provides performant, standardized inference protocol across ML frameworks. Support modern serverless inference workload with autoscaling including a scale to zero on GPU. Provides high scalability, density packing, and intelligent routing using ModelMesh. Simple and pluggable production serving for production ML serving including prediction, pre/post-processing, monitoring, and explainability. Advanced deployments with the canary rollout, experiments, ensembles, and transformers. ModelMesh is designed for high-scale, high-density, and frequently-changing model use cases. ModelMesh intelligently loads and unloads AI models to and from memory to strike an intelligent trade-off between responsiveness to users and computational footprint.Starting Price: Free -
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Amazon SageMaker Studio
Amazon
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. Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning models. -
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Atmantara
Atmantara GmbH
Atmantara is an enterprise AI infrastructure platform built for financial institutions, banks, insurers, and fintechs, to deploy and scale custom ML models securely and efficiently. Designed for regulated, data-rich environments, Atmantara streamlines the full ML lifecycle from ingestion to production through a unified, developer-friendly platform. Prebuilt & custom models for: • Fraud Detection • Credit Risk Scoring • Churn Prediction • Claims Automation • Debt Collection • Portfolio Optimization • Regulatory Compliance • Payment Optimization • Document Understanding • Customer Insights With real-time pipelines, secure model deployment, and full auditability, Atmantara lets your teams operationalize AI with speed and confidence.Starting Price: $199/month -
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AdvisoryWorld
AdvisoryWorld
Elegant user experiences built on top of our robust API. Leading investment advisors trust AdvisoryWorld web-applications to power their wealth management workflows. Find and analyze investments based on dozens of criteria such as asset classification, manager tenure, performance statistics and style detail. Compare and contrast investments with diversification and performance based statistics including Asset Allocation, Hypothetical Historical Performance, MPT Statistics (e.g. Standard Deviation, Sharpe, Beta, Alpha), P/E Ratio, Duration, Expense Ratio, Country, Region, Sector and Equity Overlap, Standardized Returns (FINRA Required) and more. Build customizable Asset Fact Sheets and side-by-side comparisons with ease. Build and analyze portfolios with institutional-grade financial modeling tools framed within a light-weight user experience. Review the effect of dividend and capital gain reinvestment, fees & sales charges and rebalancing rates on the growth of a portfolio. -
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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.
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Neuton AutoML
Neuton.AI
Neuton, a no-code AutoML solution, makes Machine Learning available to everyone. Explore data insights and make predictions leveraging Automated Artificial Intelligence. • NO coding • NO need for technical skills • NO need for data science knowledge Neuton provides comprehensive Explainability Office©, a unique set of tools that allow users to evaluate model quality at every stage, identify the logic behind the model analysis, understand why certain predictions have been made. • Exploratory Data Analysis • Feature Importance Matrix with class granularity • Model Interpreter • Feature Influence Matrix • Model-to-Data Relevance Indicators historical and for every prediction • Model Quality Index • Confidence Interval • Extensive list of supported metrics with Radar Diagram Neuton enables users to implement ML in days instead of months.Starting Price: $0 -
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Invert
Invert
Invert offers a complete suite for collecting, cleaning, and contextualizing data, ensuring every analysis and insight is based on reliable, organized data. Invert collects and standardizes all your bioprocess data, with powerful, built-in products for analysis, machine learning, and modeling. Clean, standardized data is just the beginning. Explore our suite of data management, analysis, and modeling tools. Replace manual workflows in spreadsheets or statistical software. Calculate anything using powerful statistical features. Automatically generate reports based on recent runs. Add interactive plots, calculations, and comments and share with internal or external collaborators. Streamline planning, coordination, and execution of experiments. Easily find the data you need, and deep dive into any analysis you'd like. From integration to analysis to modeling, find all the tools you need to manage and make sense of your data. -
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Datatron
Datatron
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. -
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Robust Intelligence
Robust Intelligence
The Robust Intelligence Platform integrates seamlessly into your ML lifecycle to eliminate model failures. The platform detects your model’s vulnerabilities, prevents aberrant data from entering your AI system, and detects statistical data issues like drift. At the core of our test-based approach is a single test. Each test measures your model’s robustness to a specific type of production model failure. Stress Testing runs hundreds of these tests to measure model production readiness. The results of these tests are used to auto-configure a custom AI Firewall that protects the model against the specific forms of failure to which a given model is susceptible. Finally, Continuous Testing runs these tests during production, providing automated root cause analysis informed by the underlying cause of any single test failure. Using all three elements of the Robust Intelligence platform together helps ensure ML Integrity. -
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AIxBlock
AIxBlock
AIxBlock: The first unified and decentralized platform for end-to-end AI development and workflow automation - built natively on MCP. AIxBlock is a MCP-based, decentralized end-to-end AI development and workflow automation platform purpose-built for AI engineer teams. It empowers users to build, train, deploy AI models and build AI automation workflows using those models through a unified environment that integrates decentralized compute, models, datasets, and labeling resources - all at a fraction of the traditional cost. AIxBlock is the modular AI ecosystem - purpose-built for custom model creation, workflow automation, and open interoperability across MCP client tools like Cursor, Claude, WindSurf, etc.Starting Price: $19 per month -
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ResolutionPro
ResolutionPro
ResolutionPro is a financial analytics library for Excel that can be used to calculate MTM and risk. Free trial available. Calculate valuations and risk characteristics for all commonly traded financial instruments including interest Rate Swap Pricing - Zero curve construction & swap valuation. Options, currency options, Equity options, Commodity options. Bond Pricing - Government & corporate bonds, and FRNs. IRO Pricing, Interest rate options, Swaptions, Caps, Floors. Exotic Options, Exotic options pricing. Utilities, accrual and data calculation, Probability & Statistics, and Rates & Discount Factors. Quick and easy derivative pricing. Perform independent valuation for trade and risk management. Incorporate derivative models into your existing systems. Run complex scenario analysis easily with Resolution's built-in Sensitivity Tool. Apply industry-standard pricing methodologies to an extensive range of instruments.Starting Price: $100 one-time payment -
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Ethos
Ethos
Ethos is a comprehensive Model Risk Management (MRM) platform designed to streamline every stage of the MRM lifecycle for financial institutions. From development and documentation to validation and automated reporting, Ethos simplifies complex processes through real-time dashboards, automated workflows, and robust governance structures. The platform ensures full compliance with regulatory standards such as SR 11-7, OCC 2011-12, NIST, GLBA, CCAR, Basel III, DFAST, and FASB. Ethos offers features like Governed Inventories, serving as a single source of truth for all models to promote trust and alignment across all lines of defense. Its Workflow Engine automates and standardizes the entire MRM lifecycle, enhancing operational efficiency and fostering collaboration among teams. Built by financial industry experts, Ethos delivers accuracy, compliance, and resilience, enabling organizations to manage both traditional and AI-driven models effectively. -
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MultiScoring
LAPS Consulting
Multiscoring FinTech Solution Online decision engine. Data analysis, behavior, score. Contact Us Our solution Decision engine Validation of rules, data processing for the granting of financial products Scoring Combination of variables from different bureaus. Smart queries Integration Connectivity with different sources of information, apis, web services, webhooks, database Forms We offer white label forms for data collection. Administrative Console Analyze your results using statistical dashboards, Modify the rules and / or variables of your model. API Use our integration interface to connect different information sources, desktop solutions, webs and mobile devices. information Personal, Labor, Financial, Domiciles, Banks, Score, Identity Validation, Vehicles, Garnishments, Situations. How we work. Analysis. Developing. Production. Agree scope. Select information sources. Define the models. Adjust the rules. Develop integrations. Implement the model. Make all the settings. -
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Amazon EC2 Trn1 Instances
Amazon
Amazon Elastic Compute Cloud (EC2) Trn1 instances, powered by AWS Trainium chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and latent diffusion models. Trn1 instances offer up to 50% cost-to-train savings over other comparable Amazon EC2 instances. You can use Trn1 instances to train 100B+ parameter DL and generative AI models across a broad set of applications, such as text summarization, code generation, question answering, image and video generation, recommendation, and fraud detection. The AWS Neuron SDK helps developers train models on AWS Trainium (and deploy models on the AWS Inferentia chips). It integrates natively with frameworks such as PyTorch and TensorFlow so that you can continue using your existing code and workflows to train models on Trn1 instances.Starting Price: $1.34 per hour -
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Deeploy
Deeploy
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. -
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Modzy
Modzy
Easily deploy, manage, monitor, and secure AI models in production. Modzy is the Enterprise AI platform designed to make it easy to scale trustworthy AI to your enterprise. Use Modzy to accelerate your deployment, management, and governance of trusted AI through the power of: Enterprise-grade platform features including security, APIs, and SDKs with unlimited model deployment, management, governance and monitoring at scale. Deployment options—your hardware, private, or public cloud. Includes AirGap deployments and tactical edge. Governance and auditing for centralized AI management, so you'll always have insight into AI models running in production in real-time. World's fastest Explainability (beta) solution for deep neural networks, creating audit logs to understand model predictions. Cutting-edge security features to block data poisoning and full-suite of patented Adversarial Defense to secure models running in production.Starting Price: $3.79 per hour -
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AgenaRisk
Agena
AGENARISK uses the latest developments from the field of Bayesian artificial intelligence and probabilistic reasoning to model complex, risky problems and improve how decisions are made. You can use AgenaRisk models to make predictions, perform diagnostics and make decisions by combining data and knowledge about complex causal and other dependencies in the real world. Our clients use AgenaRisk to model a variety of problems involving risk and uncertainty including operational risk, actuarial analysis, intelligence analysis risk, systems safety and reliability, health risk, cyber-security risk and strategic financial planning. AgenaRisk designs and markets groundbreaking products using Bayesian Network technology. Our technology and accompanying methodology has been published in top academic AI, machine learning, actuarial, decision science and cognitive science journals. -
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TruEra
TruEra
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. -
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ioModel
Twin Tech Labs
ioModel is designed to provide existing analytics teams access to powerful machine learning models without having to write code, significantly reducing development and maintenance costs. Furthermore, analysts can then validate and understand the efficacy of models developed on the platform using well understood and proven statistical validation techniques. The ioModel Research Platform will do for machine learning what the spreadsheet did for general computing. The ioModel Research Platform is developed entirely using open source technology and is itself available (without support or warranty) under the GPL License on GitHub. We invite our community to collaborate with us on the roadmap, development, and governance of the Platform. We’re committed to working openly and transparently to drive forward analytics, modeling, and innovation.. -
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Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. Features are used repeatedly by multiple teams and feature quality is critical to ensure a highly accurate model. Also, when features used to train models offline in batch are made available for real-time inference, it’s hard to keep the two feature stores synchronized. SageMaker Feature Store provides a secured and unified store for feature use across the ML lifecycle. Store, share, and manage ML model features for training and inference to promote feature reuse across ML applications. Ingest features from any data source including streaming and batch such as application logs, service logs, clickstreams, sensors, etc.
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Anaconda
Anaconda
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. -
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Focus
Paragon Business Solutions
Focus is a central tool that improves model governance, transparency, efficiency and effectiveness. Focus ensures you adhere to best practice for regulatory requirements in a controlled, systemic way. Define and adhere to policy and process, with comprehensive records, reporting and remediation to help you stay on track. With easy, controlled access to all models, reports and documents and up to the minute status, tasks and actions dashboards, it also facilitates better prioritization and resource planning in a single practical solution. - Defined data and model dependencies and taxonomy - Centralised model inventory - Model risks reported and remediation plans tracked - Model lifecycle events and workflow management - Full audit trail, tracking and reporting - User configurable reporting and querying - Implementation flexibility -
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Ludwig
Uber AI
Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Build custom models with ease: a declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Optimized for scale and efficiency: automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. Expert level control: retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations. Modular and extensible: experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning. -
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Salford Predictive Modeler (SPM)
Minitab
The Salford Predictive Modeler® (SPM) software suite is a highly accurate and ultra-fast platform for developing predictive, descriptive, and analytical models. The Salford Predictive Modeler® software suite includes the CART®, MARS®, TreeNet®, Random Forests® engines, as well as powerful new automation and modeling capabilities not found elsewhere. The SPM software suite’s data mining technologies span classification, regression, survival analysis, missing value analysis, data binning and clustering/segmentation. SPM algorithms are considered to be essential in sophisticated data science circles. The SPM software suite‘s automation accelerates the process of model building by conducting substantial portions of the model exploration and refinement process for the analyst. We package a complete set of results from alternative modeling strategies for easy review. -
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GM Valuation
Greenmatch
GM Valuation is the adequate instrument for the transaction valuation and periodical reassessment of your renewable energy projects. The wide range of performance indicators allow you to have the full control over your projects at any time. With the certified financial model you can assess and structure your renewable energy projects reliably. Manage your internal and external contacts. Easily share projects with the embedded invitation function. You decide yourself which parties are allowed to interact with each other and who gets read and write access. Do not lose time discussing differing spreadsheet calculations, instead focus on the best negotiation result possible. Irrespective of your project's complexity, with GM Valuation you can model all of your projects in one single and highly flexible standard. GM Valuation takes into account every possible remuneration system. From simple fixed-price models to certificate systems in interaction with electricity-price surveys -
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Xilinx
Xilinx
The Xilinx’s AI development platform for AI inference on Xilinx hardware platforms consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP. Supports mainstream frameworks and the latest models capable of diverse deep learning tasks. Provides a comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices. You can find the closest model and start re-training for your applications! Provides a powerful open source quantizer that supports pruned and unpruned model quantization, calibration, and fine tuning. The AI profiler provides layer by layer analysis to help with bottlenecks. The AI library offers open source high-level C++ and Python APIs for maximum portability from edge to cloud. Efficient and scalable IP cores can be customized to meet your needs of many different applications. -
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MosaicML
MosaicML
Train and serve large AI models at scale with a single command. Point to your S3 bucket and go. We handle the rest, orchestration, efficiency, node failures, and infrastructure. Simple and scalable. MosaicML enables you to easily train and deploy large AI models on your data, in your secure environment. Stay on the cutting edge with our latest recipes, techniques, and foundation models. Developed and rigorously tested by our research team. With a few simple steps, deploy inside your private cloud. Your data and models never leave your firewalls. Start in one cloud, and continue on another, without skipping a beat. Own the model that's trained on your own data. Introspect and better explain the model decisions. Filter the content and data based on your business needs. Seamlessly integrate with your existing data pipelines, experiment trackers, and other tools. We are fully interoperable, cloud-agnostic, and enterprise proved. -
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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.Starting Price: Free
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44
Descartes Labs
Descartes Labs
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. -
45
SS&C Algorithmics
SS&C Technologies
SS&C Algorithmics MDAS offers end-to-end managed data services as well as access to sophisticated risk management, analytics and investment decision support tools on a highly scalable platform without the significant costs of maintaining a full on-premise deployment. With its integrated risk framework including a broad coverage of asset classes and access to an extensive range of financial models, SS&C Algorithmics provides features of an on-premise financial risk solution with bundled data on cloud. SS&C Algorithmics combines the benefits of turn-key risk services with flexibility of solution customization and operational robustness. Cloud Advantage - Highly scalable, flexible and secure platform with increased performance provides more value at lower costs. Advanced Analytics - Extensive coverage in asset classes, analytical models and risk perspectives across instruments, risk factors and investment strategies. -
46
Alibaba Cloud Model Studio
Alibaba
Model Studio is Alibaba Cloud’s one-stop generative AI platform that lets developers build intelligent, business-aware applications using industry-leading foundation models like Qwen-Max, Qwen-Plus, Qwen-Turbo, the Qwen-2/3 series, visual-language models (Qwen-VL/Omni), and the video-focused Wan series. Users can access these powerful GenAI models through familiar OpenAI-compatible APIs or purpose-built SDKs, no infrastructure setup required. It supports a full development workflow, experiment with models in the playground, perform real-time and batch inferences, fine-tune with tools like SFT or LoRA, then evaluate, compress, accelerate deployment, and monitor performance, all within an isolated Virtual Private Cloud (VPC) for enterprise-grade security. Customization is simplified via one-click Retrieval-Augmented Generation (RAG), enabling integration of business data into model outputs. Visual, template-driven interfaces facilitate prompt engineering and application design. -
47
AI Squared
AI Squared
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. -
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MyDataModels TADA
MyDataModels
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.Starting Price: $5347.46 per year -
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Domino Enterprise MLOps Platform
Domino Data Lab
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. -
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Snorkel AI
Snorkel AI
AI today is blocked by lack of labeled data, not models. Unblock AI with the first data-centric AI development platform powered by a programmatic approach. Snorkel AI is leading the shift from model-centric to data-centric AI development with its unique programmatic approach. Save time and costs by replacing manual labeling with rapid, programmatic labeling. Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets. Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data. Version and audit data like code, leading to more responsive and ethical deployments. Incorporate subject matter experts' knowledge by collaborating around a common interface, the data needed to train models. Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators.
