Alternatives to PwC Model Edge
Compare PwC Model Edge alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to PwC Model Edge in 2026. Compare features, ratings, user reviews, pricing, and more from PwC Model Edge competitors and alternatives in order to make an informed decision for your business.
-
1
Vertex AI
Google
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. Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex. -
2
LogicGate Risk Cloud
LogicGate
LogicGate’s leading GRC process automation platform, Risk Cloud™, enables organizations to transform disorganized risk and compliance operations into agile process applications, without writing a single line of code. LogicGate believes that flexible, easy-to-use enterprise technology can change the trajectory of organizations and the lives of their employees. We are dedicated to transforming the way companies manage their governance, risk, and compliance (GRC) programs, so they can manage risk with confidence. LogicGate’s Risk Cloud platform and cloud-based applications, combined with raving fan service and expertly crafted content, enable organizations to transform disorganized risk and compliance operations into agile processes, without writing a single line of code. -
3
Amazon SageMaker
Amazon
Amazon SageMaker is an advanced machine learning service that provides an integrated environment for building, training, and deploying machine learning (ML) models. It combines tools for model development, data processing, and AI capabilities in a unified studio, enabling users to collaborate and work faster. SageMaker supports various data sources, such as Amazon S3 data lakes and Amazon Redshift data warehouses, while ensuring enterprise security and governance through its built-in features. The service also offers tools for generative AI applications, making it easier for users to customize and scale AI use cases. SageMaker’s architecture simplifies the AI lifecycle, from data discovery to model deployment, providing a seamless experience for developers. -
4
TensorFlow
TensorFlow
An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.Starting Price: Free -
5
Docker
Docker
Docker takes away repetitive, mundane configuration tasks and is used throughout the development lifecycle for fast, easy and portable application development, desktop and cloud. Docker’s comprehensive end-to-end platform includes UIs, CLIs, APIs and security that are engineered to work together across the entire application delivery lifecycle. Get a head start on your coding by leveraging Docker images to efficiently develop your own unique applications on Windows and Mac. Create your multi-container application using Docker Compose. Integrate with your favorite tools throughout your development pipeline, Docker works with all development tools you use including VS Code, CircleCI and GitHub. Package applications as portable container images to run in any environment consistently from on-premises Kubernetes to AWS ECS, Azure ACI, Google GKE and more. Leverage Docker Trusted Content, including Docker Official Images and images from Docker Verified Publishers.Starting Price: $7 per month -
6
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 -
7
Yields.io
Yields.io
Streamline your model lifecycle with confidence, and automate real-time model validation and monitoring through our AI-driven model risk management technology- the Chiron MRM Platform. Model validation can be a lengthy and costly process. With our enterprise risk management technology, our Clients are able to reduce the cost of model validation by a factor of 10. Chiron’s monitoring functionality allows for the early detection of model failure, leading to better models and lower capital requirements. To trust models as drivers of decisions, you need to have a transparent and auditable view of the models used within your organization. Chiron Enterprise offers a customizable model inventory to keep track of all models throughout their lifecycle and a configurable workflow engine to streamline processes. Scale your model risk activities while enforcing structured and consistent workflows within your teams. -
8
ValidMind
ValidMind
ValidMind is the most efficient solution for organizations to automate testing, documentation, and risk management for AI and statistical models. The ValidMind platform is a suite of tools helping data scientists, businesses, and risk/compliance stakeholders identify and document potential risks in their AI models, and ensure they deliver on expected regulatory outcomes. Our integrated platform makes it easy to review risk areas across all your teams' models and prioritize areas for compliance and risk mitigation. ValidMind enables organizations to break down information silos and reduce the complexity associated with sharing and collaborating on model documentation, validation reports, and risk findings through the model lifecycle. -
9
Fairly
Fairly
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. -
10
Modelscape
MathWorks
The Modelscape solution enables financial institutions to reduce the complexity of managing the lifecycle of financial models while improving model documentation, transparency, and compliance. By implementing the solution throughout the model lifecycle, you can use templated model workflows, automated documentation, and artifact linking. Scale algorithms, models, and apps both horizontally and vertically. Provide support for enterprise infrastructure, tooling, and languages such as Python, R, SAS, and MATLAB. Track issues across the model lifecycle with full model lineage, issue, and usage reporting. Use the executive dashboard for model data, custom algorithm execution, automated workflows, and web-based access to a comprehensive, auditable inventory of all models and dependencies. Develop, back-test, and document models and methodologies. Improve transparency, reproducibility, and reusability of models. Automatically generate model documentation and reports. -
11
Crowe Model Risk Manager
Crowe
Your program is one weak point away from missing critical risks. Risk models are getting more complicated as banks link sophisticated calculations, businesswide models, and model owners. Hazards could be hiding between model disconnects. But organizations don’t have to watch programs fracture. Crowe Model Risk Manager can provide a software platform to link model risk management from beginning to end. Centralized software with real-time visualization makes it easier to manage workflows, track issues, generate reports, and demonstrate compliance. Banks can move past spreadsheets and emails to a connected and comprehensive view. With our software solution, a better understanding of each aspect of your model risk management can become easily accessible and understandable. Model owners can see their responsibilities along with clear next steps and activity monitoring. Banks can set up automated actions and workflows to improve efficiency and keep the program moving. -
12
JFrog ML
JFrog
JFrog ML (formerly Qwak) offers an MLOps platform designed to accelerate the development, deployment, and monitoring of machine learning and AI applications at scale. The platform enables organizations to manage the entire lifecycle of machine learning models, from training to deployment, with tools for model versioning, monitoring, and performance tracking. It supports a wide variety of AI models, including generative AI and LLMs (Large Language Models), and provides an intuitive interface for managing prompts, workflows, and feature engineering. JFrog ML helps businesses streamline their ML operations and scale AI applications efficiently, with integrated support for cloud environments. -
13
Azure Machine Learning
Microsoft
Accelerate the end-to-end machine learning lifecycle with Azure Machine Learning Studio. 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. -
14
Valohai
Valohai
Models are temporary, pipelines are forever. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform that automates everything from data extraction to model deployment. Automate everything from data extraction to model deployment. Store every single model, experiment and artifact automatically. Deploy and monitor models in a managed Kubernetes cluster. Point to your code & data and hit run. Valohai launches workers, runs your experiments and shuts down the instances for you. Develop through notebooks, scripts or shared git projects in any language or framework. Expand endlessly through our open API. Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable.Starting Price: $560 per month -
15
Connected Risk
Empowered Systems
Connected Risk allows your team to achieve all of your governance, risk, and compliance (GRC) needs in one single solution. Built off of our next-generation, low-code/no-code platform, EmpoweredNEXT, Connected Risk’s powerful backbone allows you to expand your solution with practical applications designed specifically around your team’s needs. Holistic and connected risk management is designed to manage your governance, risk, and compliance programs in an integrated lifecycle specifically for your organization. Trusted by top global organizations every day to manage their governance, risk, and compliance needs. Enterprise risk management equips your organization with the tools needed to benefit from both risk and disruption. Regulatory change management enables your compliance team to actively manage change in a connected and structured manner. Model risk management empowers your organization to create and maintain your model inventory using effective workflow management. -
16
MLflow
MLflow
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components. Record and query experiments: code, data, config, and results. Package data science code in a format to reproduce runs on any platform. Deploy machine learning models in diverse serving environments. Store, annotate, discover, and manage models in a central repository. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs. An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects. -
17
NeoPulse
AI Dynamics
The NeoPulse Product Suite includes everything needed for a company to start building custom AI solutions based on their own curated data. Server application with a powerful AI called “the oracle” that is capable of automating the process of creating sophisticated AI models. Manages your AI infrastructure and orchestrates workflows to automate AI generation activities. A program that is licensed by the organization to allow any application in the enterprise to access the AI model using a web-based (REST) API. NeoPulse is an end-to-end automated AI platform that enables organizations to train, deploy and manage AI solutions in heterogeneous environments, at scale. In other words, every part of the AI engineering workflow can be handled by NeoPulse: designing, training, deploying, managing and retiring. -
18
navio
craftworks GmbH
Seamless machine learning model management, deployment, and monitoring for supercharging MLOps for any organization on the best AI platform. Use navio to perform various machine learning operations across an organization's entire artificial intelligence landscape. Take your experiments out of the lab and into production, and integrate machine learning into your workflow for a real, measurable business impact. navio provides various Machine Learning operations (MLOps) to support you during the model development process all the way to running your model in production. Automatically create REST endpoints and keep track of the machines or clients that are interacting with your model. Focus on exploration and training your models to obtain the best possible result and stop wasting time and resources on setting up infrastructure and other peripheral features. Let navio handle all aspects of the product ionization process to go live quickly with your machine learning models. -
19
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. -
20
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.Starting Price: $1,050 per month
-
21
Entry Point AI
Entry Point AI
Entry Point AI is the modern AI optimization platform for proprietary and open source language models. Manage prompts, fine-tunes, and evals all in one place. When you reach the limits of prompt engineering, it’s time to fine-tune a model, and we make it easy. Fine-tuning is showing a model how to behave, not telling. It works together with prompt engineering and retrieval-augmented generation (RAG) to leverage the full potential of AI models. Fine-tuning can help you to get better quality from your prompts. Think of it like an upgrade to few-shot learning that bakes the examples into the model itself. For simpler tasks, you can train a lighter model to perform at or above the level of a higher-quality model, greatly reducing latency and cost. Train your model not to respond in certain ways to users, for safety, to protect your brand, and to get the formatting right. Cover edge cases and steer model behavior by adding examples to your dataset.Starting Price: $49 per month -
22
Amazon SageMaker Edge
Amazon
The SageMaker Edge Agent allows you to capture data and metadata based on triggers that you set so that you can retrain your existing models with real-world data or build new models. Additionally, this data can be used to conduct your own analysis, such as model drift analysis. We offer three options for deployment. GGv2 (~ size 100MB) is a fully integrated AWS IoT deployment mechanism. For those customers with a limited device capacity, we have a smaller built-in deployment mechanism within SageMaker Edge. For customers who have a preferred deployment mechanism, we support third party mechanisms that can be plugged into our user flow. Amazon SageMaker Edge Manager provides a dashboard so you can understand the performance of models running on each device across your fleet. The dashboard helps you visually understand overall fleet health and identify the problematic models through a dashboard in the console. -
23
Portkey
Portkey.ai
Launch production-ready apps with the LMOps stack for monitoring, model management, and more. Replace your OpenAI or other provider APIs with the Portkey endpoint. Manage prompts, engines, parameters, and versions in Portkey. Switch, test, and upgrade models with confidence! View your app performance & user level aggregate metics to optimise usage and API costs Keep your user data secure from attacks and inadvertent exposure. Get proactive alerts when things go bad. A/B test your models in the real world and deploy the best performers. We built apps on top of LLM APIs for the past 2 and a half years and realised that while building a PoC took a weekend, taking it to production & managing it was a pain! We're building Portkey to help you succeed in deploying large language models APIs in your applications. Regardless of you trying Portkey, we're always happy to help!Starting Price: $49 per month -
24
Koog
JetBrains
Koog is a Kotlin‑based framework for building and running AI agents entirely in idiomatic Kotlin, supporting both single‑run agents that process individual inputs and complex workflow agents with custom strategies and configurations. It features pure Kotlin implementation, seamless Model Control Protocol (MCP) integration for enhanced model management, vector embeddings for semantic search, and a flexible system for creating and extending tools that access external systems and APIs. Ready‑to‑use components address common AI engineering challenges, while intelligent history compression optimizes token usage and preserves context. A powerful streaming API enables real‑time response processing and parallel tool calls. Persistent memory allows agents to retain knowledge across sessions and between agents, and comprehensive tracing facilities provide detailed debugging and monitoring.Starting Price: Free -
25
Sagify
Sagify
Sagify complements AWS Sagemaker by hiding all its low-level details so that you can focus 100% on Machine Learning. Sagemaker is the ML engine and Sagify is the data science-friendly interface. You just need to implement 2 functions, a train and a predict in order to train, tune and deploy hundreds of ML models. Manage your ML models from one place without dealing with low level engineering tasks. No more flaky ML pipelines. Sagify offers 100% reliable training and deployment on AWS. Train, tune and deploy hundreds of ML models by implementing just 2 functions. -
26
The EY Trusted AI Platform provides insights to organizations on the sources and drivers of risk and guides an AI design team in quantifying AI risks. The EY Trusted AI Platform uses interactive, web-based schematic and assessment tools to build the risk profile of an AI system. It then uses an advanced analytical model to convert the user responses to a composite score comprising technical risk, stakeholder impact, and control effectiveness of an AI system. To help determine technical risk, the platform evaluates the technical design of an AI system, measuring risk drivers that include its underlying technologies, technical operating environment, and level of autonomy. To help determine stakeholder risk, the platform considers the goals and objectives of the AI system. It also considers the financial, emotional, and physical impact on the external and internal users, as well as the reputational, regulatory, and legal risks.
-
27
DVC
iterative.ai
Data Version Control (DVC) is an open source version control system tailored for data science and machine learning projects. It offers a Git-like experience to organize data, models, and experiments, enabling users to manage and version images, audio, video, and text files in storage, and to structure their machine learning modeling process into a reproducible workflow. DVC integrates seamlessly with existing software engineering tools, allowing teams to define any aspect of their machine learning projects, data and model versions, pipelines, and experiments, in human-readable metafiles. This approach facilitates the use of best practices and established engineering toolsets, reducing the gap between data science and software engineering. By leveraging Git, DVC enables versioning and sharing of entire machine learning projects, including source code, configurations, parameters, metrics, data assets, and processes, by committing DVC metafiles as placeholders. -
28
Metaflow
Netflix
Successful data science projects are delivered by data scientists who can build, improve, and operate end-to-end workflows independently, focusing more on data science, less on engineering. Use Metaflow with your favorite data science libraries, such as Tensorflow or SciKit Learn, and write your models in idiomatic Python code with not much new to learn. Metaflow also supports the R language. Metaflow helps you design your workflow, run it at scale, and deploy it to production. It versions and tracks all your experiments and data automatically. It allows you to inspect results easily in notebooks. Metaflow comes packaged with the tutorials, so getting started is easy. You can make copies of all the tutorials in your current directory using the metaflow command line interface. -
29
Mosaic AIOps
Larsen & Toubro Infotech
LTI’s Mosaic is a converged platform, which offers data engineering, advanced analytics, knowledge-led automation, IoT connectivity and improved solution experience to its users. Mosaic enables organizations to undertake quantum leaps in business transformation, and brings an insights-driven approach to decision-making. It helps deliver pioneering Analytics solutions at the intersection of physical and digital worlds. Catalyst for Enterprise ML & AI Adoption. ModelManagement. TrainingAtScale. AIDevOps. MLOps. MultiTenancy. LTI’s Mosaic AI is a cognitive AI platform, designed to provide its users with an intuitive experience in building, training, deploying and managing AI models at enterprise scale. It brings together the best AI frameworks & templates, to provide a platform where users enjoy a seamless & personalized “Build-to-Run” transition on their AI workflows. -
30
Apparity
Apparity
Apparity helps efficiently manage end user computing (EUC) risk in one powerful platform backed by phenomenal customer support. Apparity is designed to reliably identify, inventory, assess and control the end user applications that support your most critical business processes. This includes spreadsheets, models, databases, programming language scripts, BI tools and more. Our software platform adds enterprise-wide visibility by offering a complete audit of all EUC activity. How do we do this? It’s simple. With accurate file tracking and version control, you’ll be able to effectively manage your EUC inventory and ensure regulatory compliance. After implementation, end users will benefit from enhanced collaboration and increased process automation. -
31
SAS Risk Management
SAS Institute
No matter how your financial institution prioritizes risk, SAS has proven methodologies and best practices to help you establish a risk-aware culture, optimize capital and liquidity, and meet regulatory demands. Put on-demand, high-performance risk analytics in the hands of your risk professionals to ensure greater efficiency and transparency. Strike the right balance between short- and long-term strategies. And confidently address changing regulatory requirements. SAS has proven methodologies and best practices to help you establish a risk-aware culture, optimize capital and liquidity, and efficiently meet regulatory demands. Deploy a broad range of scalable credit models to continuously manage your loan portfolios. Improve regulatory compliance and instill powerful balance sheet management capabilities. Simulate over multiple scenarios. Produce results faster with a richer analysis to inform business decision-making. -
32
CIMCON Software
CIMCON Software
CIMCON Software minimizes operational risks inherent in end-user computing (EUC) files. Risks include regulatory reporting errors, non-compliance, cyber risk, and fraud. EUCs include documents such as spreadsheets, models, Access databases, scripted applications using VBScript, R, Python and self-service analytics tools such as Tableau and QlikView. Banks rely heavily on end-user computing applications (EUCs) such as Excel spreadsheets and scripts for day to day operations because they allow users to react quickly to changing market conditions or regulations. Whether they are used for creating financial models, finance, accounting or complying with regulatory requirements, they need to be managed effectively. CIMCON Software offers solutions that create an inventory of all EUCs in your organization, identify the most critical files, detect errors; provide a visual map of data dependencies, and provide on-going monitoring and control of your most important EUCs. -
33
Gate22
ACI.dev
Gate22 is an enterprise-grade AI governance and MCP (Model Context Protocol) control platform that centralizes, secures, and observes how AI tools and agents access and use MCP servers across an organization. It lets administrators onboard, configure, and manage both external and internal MCP servers with fine-grained, function-level permissions, team-based access control, and role-based policies so that only approved tools and functions can be used by specific teams or users. Gate22 provides a unified MCP endpoint that bundles multiple MCP servers into a simplified interface with just two core functions, so developers and AI clients consume fewer tokens and avoid context overload while maintaining high accuracy and security. The admin view offers a governance dashboard to monitor usage patterns, maintain compliance, and enforce least-privilege access, while the member view gives streamlined, secure access to authorized MCP bundles.Starting Price: Free -
34
Protecht ERM
Protecht Group
While others fear risk, we embrace it. With offices in Los Angeles, London and Sydney, Protecht redefines the way people think about risk management. We help companies increase performance and achieve strategic objectives by better understanding, monitoring and managing risk. Protecht provides an integrated platform of risk management, compliance, training and advisory services to businesses that need to manage enterprise risks and regulatory compliance. In North America, Protecht solutions focus on banks, credit unions and financial institutions. With the Protecht ERM platform - no-code, integrated GRC software - you can manage all enterprise risks in a single place: - Dashboard summaries of Key Risk Indicators (KRIs), Key Control Indicators (KCIs), and Key Performance Indicators (KPIs) - Vendor risk (VRM & TPRM) - Cyber, IT, ISMS, and privacy risk - Model & AI risk - BCM - Risk assessments, RCSA, risk registers - Compliance management - Incidents, issues, policies -
35
Citrusˣ
Citrusˣ
Citrusˣ’s end-to-end platform for AI transparency and explainability allows organizations to maintain confidence in their models. Data scientists can use the Summary and Validation pages on the web UI and SDK to validate the performance of their models, investigate results, and address issues. Data science managers and Chief data officers can track the work of their teams, compare models, and ensure KPIs are being met. Risk officers and MRMs can use the web UI and reports to verify the soundness of the model, assess the risks, and ensure AI is being used responsibly and fairly according to regulatory requirements. Executives and regulators can use summarized custom reports to verify the model's strength and accuracy, understand the model's decisions, identify risks, and ensure compliance to protect the organization from potential lawsuits and maintain its reputation. -
36
Kubeflow
Kubeflow
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model. In particular, Kubeflow's job operator can handle distributed TensorFlow training jobs. Configure the training controller to use CPUs or GPUs and to suit various cluster sizes. Kubeflow includes services to create and manage interactive Jupyter notebooks. You can customize your notebook deployment and your compute resources to suit your data science needs. Experiment with your workflows locally, then deploy them to a cloud when you're ready. -
37
LogicManager
LogicManager
LogicManager is a holistic Enterprise Risk Management (ERM) platform that empowers organizations to make risk-informed decisions, drive performance, and demonstrate accountability across the enterprise. Unlike siloed tools, LogicManager connects governance, risk, and compliance activities in a centralized, no-code environment—turning insights into action through its patented Risk Ripple® Intelligence. From policy management and control testing to incident tracking and board reporting, LogicManager streamlines workflows, strengthens internal controls, and provides real-time visibility across departments. With built-in automation, relationship mapping, and AI-powered guidance from LogicManager Expert, users can identify emerging threats, align with strategic goals, and reduce complexity. Backed by award-winning support, LogicManager transforms risk management into a collaborative, proactive function that protects reputations and drives long-term value. -
38
DataRobot
DataRobot
AI Cloud is a new approach built for the demands, challenges and opportunities of AI today. A single system of record, accelerating the delivery of AI to production for every organization. All users collaborate in a unified environment built for continuous optimization across the entire AI lifecycle. The AI Catalog enables seamlessly finding, sharing, tagging, and reusing data, helping to speed time to production and increase collaboration. The catalog provides easy access to the data needed to answer a business problem while ensuring security, compliance, and consistency. If your database is protected by a network policy that only allows connections from specific IP addresses, contact Support for a list of addresses that an administrator must add to your network policy (whitelist). -
39
Gesund.ai
Gesund.ai
Gesund is the world’s first compliant AI factory on a mission to help bring clinical-grade AI solutions to market. To help comply with regulatory requirements, our platform audits and validates 3rd party medical AI solutions for safety, effectiveness, and equity. Gesund orchestrates the entire AI/ML lifecycle for all stakeholders by bringing models, data, and experts together in a no-code environment. Standardized, unified, and diversified data customized for your ML needs and regulatory requirements. Gesund.ai assesses model validation needs and provides a suitable mix of high-quality data from its multiple and diverse clinical partner sites. Model owner shares clinical study with Gesund.ai for curation of appropriate dataset(s), and uploads their model onto Gesund.ai's federated validation platform, which resides on hospital premise or private cloud. The model runs against a previously unseen validation data set that has been curated on the hospital side. -
40
AssetFuture
AssetFuture
Work smarter with better data using AssetFuture’s technology to generate insights and forecast your asset lifecycle. We partner with you to create clarity with data. Get transparency with intelligent classification, virtualization, and predictive modeling of the asset lifecycle. Get up-to-date data, lifecycle modeling, and dashboards to control maintenance costs, plan capital spending, and develop future asset strategies. AssetFuture is a technology platform that enables organizations to effectively predict cost, risk, and performance of the lifecycle of the built environment. Our customers have a deep insight into the performance and cost metrics of their Asset Portfolios and make better decisions, faster and with confidence. Real-time visualization of predicted Capital Expenditure across your whole portfolio, or by individual assets. AssetFuture gives you the flexibility to prioritize spending, develop asset renewal programs and execute them effectively. -
41
Eclipse Dirigible
Eclipse Foundation
Eclipse Dirigible™ is a high-productivity application platform that provides development tools and a runtime environment. It supports the full development lifecycle of applications by leveraging an in-system programming model and rapid application development techniques. Eclipse Dirigible provides capabilities for end-to-end development process from database modeling and management, through RESTful services authoring using various dynamic languages, to pattern-based user interface generation, role-based security, external services integration, testing, debugging, operations, and monitoring. All the Eclipse Dirigible project's source code and sample applications are licensed under Eclipse Public License v 2.0 and maintained at GitHub. You can develop student projects, test different technologies and scenarios, learn popular programming languages. Eclipse Dirigible provides everything you need for your development project. -
42
alvaModel
Alvascience
alvaModel is a software tool for building, validating, comparing, and applying QSAR and QSPR models. It supports regression and classification workflows based on molecular descriptors and fingerprints, with a strong focus on model transparency, interpretability, and scientific robustness. The software includes multiple data splitting strategies, variable selection methods, modeling algorithms, and comprehensive internal and external validation procedures. alvaModel provides diagnostic plots, applicability domain analysis, and model comparison tools to support the identification of reliable and predictive models. Designed according to best practices in chemometrics, alvaModel facilitates the development of interpretable models consistent with the OECD principles for QSAR validation, making it suitable for research and regulatory-oriented applications. The graphical interface guides users through the entire modeling workflow while allowing full control over each modeling step. -
43
Mitratech PolicyHub
Mitratech
Solve daunting policy and procedure challenges with Mitratech's PolicyHub, a policy management platform. Complete and cost effective, PolicyHub has features such as policy management, automated knowledge assessments, audit, and reporting. PolicyHub gives an organization the edge it needs to demonstrate corporate responsibility and defensible compliance program. PolicyHub also enables users to create detailed reporting in real-time and instantly react to investigations or audits. -
44
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. -
45
Sup AI
Sup AI
Sup AI is a multi-LLM platform that merges outputs from several top large language models, such as GPT, Claude, Llama, and more, to generate richer, more accurate, and better-validated answers than any single model could provide. It applies real-time “logprob confidence scoring,” analyzing each token’s probability to detect uncertainty or hallucination; when a model’s confidence falls below a threshold, the response is halted, helping ensure that delivered answers remain high-quality and trustworthy. Sup’s “multi-model fusion” then compares, contrasts, and consolidates outputs from different models, cross-verifying and synthesizing the best parts into a final result. Sup also supports “multimodal RAG” (retrieval-augmented generation) to incorporate external data (text, PDFs, images) into context-aware responses, giving the AI access to factual sources and helping it “never forget” relevant information.Starting Price: $20 per month -
46
Heeler
Heeler
Heeler is an application security platform that helps development and security teams automate the detection, prioritization, and remediation of open source and application risks by unifying contextual data from code, runtime, deployment, dependencies, and business logic into a single actionable model. It combines static and runtime analysis, software composition analysis, threat modeling, and secrets scanning with a context engine that maps how code runs in production, enabling real-time threat prioritization based on exploitability and business impact rather than raw vulnerability counts. Heeler automatically generates validated remediation guidance and can even produce merge-ready pull requests to upgrade libraries or fix issues, reducing manual research and accelerating fixes. It provides end-to-end visibility across the software development lifecycle, tracking vulnerabilities from identification through resolution and monitoring fixes across deployments.Starting Price: $250 per developer -
47
Provides a centralized inventory for all of an organization’s critical decision assets and manages them across the entire lifecycle. Served as a single pane of glass for decision risk management, FICO Decision Central enables the most effective decision governance, minimizes risk, facilitates a continuous learning and improving loop, and promotes centralized collaboration across the enterprise. Workflows and process management reviews, approvals, and tracks open tasks across the complete model lifecycle from project proposal, development, deployment, maintenance, evolution, all the way to retirement. Granular reporting and adequate controls satisfy the most demanding regulatory compliance requirements coming with new regulations, ratings policies, capital requirements, and financial and accounting reporting standards. Automatically assess, validate, report, monitor, track, and enhance decision model performance over the entire lifecycle of decision strategy.
-
48
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
-
49
Baidu AI Cloud Machine Learning (BML), an end-to-end machine learning platform designed for enterprises and AI developers, can accomplish one-stop data pre-processing, model training, and evaluation, and service deployments, among others. The Baidu AI Cloud AI development platform BML is an end-to-end AI development and deployment platform. Based on the BML, users can accomplish the one-stop data pre-processing, model training and evaluation, service deployment, and other works. The platform provides a high-performance cluster training environment, massive algorithm frameworks and model cases, as well as easy-to-operate prediction service tools. Thus, it allows users to focus on the model and algorithm and obtain excellent model and prediction results. The fully hosted interactive programming environment realizes the data processing and code debugging. The CPU instance supports users to install a third-party software library and customize the environment, ensuring flexibility.
-
50
Symbolica
Symbolica
Extant models are expensive to train, complex to deploy, difficult to validate, and infamously prone to hallucination. Symbolica is redesigning how machines learn from the ground up. We use the powerfully expressive language of category theory to develop models capable of learning algebraic structure. This enables our models to have a robust and structured model of the world; one that is explainable and verifiable. We're aiming to allow developers and end users to understand and specify how and why model outputs were produced. This interpretability and control over model outputs - including the ability to delete proprietary information from the training set - is imperative for mission-critical applications.
