Alternatives to WhyLabs

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.

Amazon CloudWatch is a monitoring service that provides observability and data for developers, DevOps engineers, site reliability engineers (SREs), IT managers, and other users. CloudWatch gives you data and actionable insights that will help you monitor your applications, respond quickly to system-wide performance changes and optimize resource utilization. It also provides a unified view on operational health. CloudWatch gathers operational and monitoring data in the form logs, metrics and events. This gives you a single view of AWS resources, applications and services that are hosted on AWS and on-premises. CloudWatch can be used to detect anomalous behavior, set alarms, visualize logs side-by, take automated actions, troubleshoot problems, and uncover insights to help you keep your applications running smoothly.

Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility.

ServiceNow Cloud Observability provides real-time visibility and monitoring of cloud infrastructure, applications and services. It allows organizations to identify and resolve performance problems by integrating data from different cloud environments into a single dashboard. ServiceNow Cloud Observability's advanced analytics and alerting features help IT and DevOps departments detect anomalies, troubleshoot issues, and ensure optimal performance. The platform supports AI-driven insights and automation, allowing teams the ability to respond quickly to incidents. Overall, the platform improves operational efficiency while ensuring a seamless user-experience across cloud environments.

Splunk Observability Cloud provides a comprehensive real-time monitoring platform that helps organizations gain visibility into their cloud native environments, infrastructures, applications, and service. It combines metrics with logs and traces to create a unified platform that provides seamless visibility from end-to-end across complex architectures. Splunk Observability helps teams identify and resolve performance problems, reduce downtime and improve system reliability with its powerful analytics and AI-driven insights. It provides real-time data in high resolution and supports a variety of integrations. This allows IT and DevOps to detect anomalies, optimize the performance, and ensure that their cloud and hybrid environment is healthy and efficient.

LMOps is a stack that allows you to launch production-ready applications for monitoring, model management and more. Portkey is a replacement for OpenAI or any other provider APIs. Portkey allows you to manage engines, parameters and versions. Switch, upgrade, and test models with confidence. View aggregate metrics for your app and users to optimize usage and API costs Protect your user data from malicious attacks and accidental exposure. Receive proactive alerts if things go wrong. Test your models in real-world conditions and deploy the best performers. We have been building apps on top of LLM's APIs for over 2 1/2 years. While building a PoC only took a weekend, bringing it to production and managing it was a hassle! We built Portkey to help you successfully deploy large language models APIs into your applications. We're happy to help you, regardless of whether or not you try Portkey!

InsightFinder Unified Intelligence Engine platform (UIE) provides human-centered AI solutions to identify root causes of incidents and prevent them from happening. InsightFinder uses patented self-tuning, unsupervised machine learning to continuously learn from logs, traces and triage threads of DevOps Engineers and SREs to identify root causes and predict future incidents. Companies of all sizes have adopted the platform and found that they can predict business-impacting incidents hours ahead of time with clearly identified root causes. You can get a complete overview of your IT Ops environment, including trends and patterns as well as team activities. You can also view calculations that show overall downtime savings, cost-of-labor savings, and the number of incidents solved.

Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported.

Our easy-to-use monitor builder allows you to create customized monitors for your machinelearning models. Get alerts for issues such as concept drift, model performance degradation and bias. Aporia can seamlessly integrate with any ML infrastructure. It doesn't matter if it's a FastAPI server built on top of Kubernetes or an open-source deployment tool such as MLFlow, or a machine-learning platform like AWS Sagemaker. Zoom in on specific data segments to track the model's behavior. Unexpected biases, underperformance, drifting characteristics, and data integrity issues can be identified. You need the right tools to quickly identify the root cause of problems in your ML models. Our investigation toolbox allows you to go deeper than model monitoring and take a deep look at model performance, data segments or distribution.

Acuvity provides the most comprehensive AI governance and security platform for your employees and apps. DevSecOps allows developers to focus on AI Innovation while DevSecOps implements AI Security without code changes. Pluggable AI security ensures complete coverage without outdated libraries or inadequate coverage. By utilizing GPUs only to run LLM models, you can reduce costs. Full visibility of all GenAI models and apps, plugins and services that are being used and explored by your teams. Granular observability of all GenAI interactions, with comprehensive logging. AI usage in enterprises needs a specialized framework that can address new AI risks and comply with emerging AI regulation. Employees can use AI with confidence, without exposing confidential information. Legal wants to make sure that AI-generated content is free of copyright or regulatory issues.

Tumeryk Inc. is a leader in advanced generative AI-based security solutions. We offer tools such as the AI Trust Score for real-time monitoring and risk management. Our platform enables organizations to secure AI systems and ensure reliable, trustworthy, policy-aligned deployments. The AI Trust Score quantifies risk associated with generative AI systems. This allows for compliance with regulations such as the EU AI Act ISO 42001 and NIST RMF 6000.1. This score evaluates the trustworthiness and scores generated prompt responses. It accounts for risks such as bias, jailbreak tendency, off-topic answers, toxicity and Personally Identifiable Information data leakage. It can be integrated in business processes to determine whether content should either be accepted, flagged or blocked. This allows organizations to minimize risks associated with AI generated content.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

Arize's machine-learning observability platform automatically detects and diagnoses problems and improves models. Machine learning systems are essential for businesses and customers, but often fail to perform in real life. Arize is an end to-end platform for observing and solving issues in your AI models. Seamlessly enable observation for any model, on any platform, in any environment. SDKs that are lightweight for sending production, validation, or training data. You can link real-time ground truth with predictions, or delay. You can gain confidence in your models' performance once they are deployed. Identify and prevent any performance or prediction drift issues, as well as quality issues, before they become serious. Even the most complex models can be reduced in time to resolution (MTTR). Flexible, easy-to use tools for root cause analysis are available.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

Responsible AI is a business problem and not a technical problem. We solve the problem by connecting teams to one platform, allowing you to reduce risk, maximize your potential, and put your intentions into action. Cloud-based governance applications can unite every stage of your AI/ML journey. GovernML is the catalyst you need to bring AI/ML systems to the world. We offer user-friendly workflows that track the entire lifecycle of your AI journey. This is good news for your bottom line and risk mitigation. Monitaur offers cloud-based governance solutions that track your AI/ML model from policy to proof. SOC 2 Type II certified, we can enhance your AI governance and provide bespoke solutions through a single platform. GovernML is responsible AI/ML systems that are available to the world. You can now create scalable, user-friendly workflows to document the entire lifecycle of your AI journey from one platform.

Models can be opaque about what data they failed to perform well on and why. Galileo offers a variety of tools that allow ML teams to quickly inspect and find ML errors up to 10x faster. Galileo automatically analyzes your unlabeled data and identifies data gaps in your model. We get it - ML experimentation can be messy. It requires a lot data and model changes across many runs. You can track and compare your runs from one place. You can also quickly share reports with your entire team. Galileo is designed to integrate with your ML ecosystem. To retrain, send a fixed dataset to the data store, label mislabeled data to your labels, share a collaboration report, and much more, Galileo was designed for ML teams, enabling them to create better quality models faster.

You can now build what took years. Simple, customizable, scalable, secure, ML monitoring. Everything you need to deploy and maintain ML in production. Superwise integrates with any ML stack, and can connect to any number of communication tools. Want to go further? Superwise is API-first. All of our APIs allow you to access everything, and we mean everything. All this from the comfort of your cloud. You have complete control over ML monitoring. You can set up metrics and policies using our SDK and APIs. Or, you can simply choose a template to monitor and adjust the sensitivity, conditions and alert channels. Get Superwise or contact us for more information. Superwise's ML monitoring policy templates allow you to quickly create alerts. You can choose from dozens pre-built monitors, ranging from data drift and equal opportunity, or you can customize policies to include your domain expertise.

Simplified quality assurance for machine learning. Snitch eliminates all noise so you can find the most relevant information to improve your models. With powerful dashboards and analysis, you can track your model's performance beyond accuracy. Identify potential problems in your data pipeline or distribution shifts and fix them before they impact your predictions. Once you've deployed, stay in production and have visibility to your models and data throughout the entire cycle. You can keep your data safe, whether it's cloud, on-prem or private cloud. Use the tools you love to integrate Snitch into your MLops process! We make it easy to get up and running quickly. Sometimes accuracy can be misleading. Before you deploy your models, make sure to assess their robustness and importance. Get actionable insights that will help you improve your models. Compare your models against historical metrics.

Unutilized data is the biggest obstacle to scaling AI-powered decision making. IBM Cloud Pak®, for Data is a unified platform that provides a data fabric to connect, access and move siloed data across multiple clouds or on premises. Automate policy enforcement and discovery to simplify access to data. A modern cloud data warehouse integrates to accelerate insights. All data can be protected with privacy and usage policy enforcement. To gain faster insights, use a modern, high-performance cloud storage data warehouse. Data scientists, analysts, and developers can use a single platform to create, deploy, and manage trusted AI models in any cloud.

IBM Guardium AI Security continuously identifies and fixes vulnerabilities in AI data models and application usage. AI deployments can be monitored continuously and automatically. Detect security flaws and incorrect configuration. Manage security interactions among users, models, data and applications. This is a part of the IBM Guardium Data Security Center which empowers security teams and AI teams to work together across the organization by integrating workflows, providing a common view on data assets and centralizing compliance policies. Guardium AI Security reveals each AI model that is associated with a deployment. It reveals the data, model and application usage of each AI deployment. You can also see which applications are accessing the model. You can view the vulnerabilities of your model, the data that underlies it, and the applications that access it. Each vulnerability has a score that indicates its criticality. This helps you prioritize your next steps. You can export the list of vulnerability for reporting.

55% of organizations adopt AI to remain competitive. Harmonic equips security teams with robust tools to ensure secure implementation. Harmonic's security reach is extended as employees adopt new tools, particularly from remote locations. Harmonic ensures that no shadow AI escapes detection. Harmonic's advanced security measures will help you to minimize the risk of data exposure, and ensure compliance. Your sensitive information will remain private and secure. The traditional data security methods cannot keep up with the rapid advances in AI. Many security teams are stuck with broad, restrictive measures which severely impact productivity. Harmonic offers a smarter solution. Our solutions give security professionals the visibility and tools they need to protect sensitive, unstructured information effectively without compromising efficiency.

AI and non AI models require risk management and oversight. Fairly is a continuous monitoring tool for advanced model governance. With Fairly, data science and cyber-security teams can easily collaborate with risk and compliance teams to ensure that models are reliable and secured. Fairly makes it simple to stay up to date with policies and regulations regarding procurement, validation, and audit of non AI, predictive AI, and generative AI. Fairly simplifies model validation and auditing by providing direct access to ground truth within a controlled environment, for both in-house models and third-party ones, without adding additional overhead to development or IT teams. Fairly's platform guarantees compliant, ethical, and secure models. Fairly helps teams identify compliance, operational, and model risks and mitigate them according to internal policies, external regulations, and monitor, report, and assess them.

Scale the next generation of AI applications with confidence. Scale AI with confidence across your organization using industry-leading technologies that manage risk, improve accuracy and privacy, reinforce transparency and simplify compliance. Use templates and tools that integrate responsible AI into open source, machine-learning operations, and generative AI workflows to empower cross-functional teams in building the next generation of AI apps. With Azure security and tooling for responsible AI, you can detect and mitigate harmful usage. Monitor text and images for offensive or inappropriate content. Rapid machine-learning models can be deployed and seamless collaboration is enabled with prompt flow. This accelerates time to value. Create generative AI applications, and copilots on one platform.

The open-source ML observability Platform. From validation to production, evaluate, test, and track ML models. From tabular data up to NLP and LLM. Built for data scientists and ML Engineers. All you need to run ML systems reliably in production. Start with simple ad-hoc checks. Scale up to the full monitoring platform. All in one tool with consistent APIs and metrics. Useful, beautiful and shareable. Explore and debug a comprehensive view on data and ML models. Start in a matter of seconds. Test before shipping, validate in production, and run checks with every model update. By generating test conditions based on a reference dataset, you can skip the manual setup. Monitor all aspects of your data, models and test results. Proactively identify and resolve production model problems, ensure optimal performance and continually improve it.

OneTrust Data & AI Governance is an integrated platform that consolidates insights from data, models, risk assessments, and metadata. It provides comprehensive visibility to data products and AI developments. It accelerates data driven innovation by increasing approval speed for data products and AI system. The solution ensures business continuity by continuously monitoring data and AI systems. This ensures regulatory compliance, effective management of risk, and reduced application downtime. It simplifies compliance through centrally defining and orchestrating data policies, as well as natively enforcing them. The scanning, classification and tagging sensitive data are key features that ensure reliable application of data governance across structured and nonstructured sources. It promotes responsible use of data by enforcing a robust data governance frame work that enforces role-based access.

IBM Watson OpenScale provides visibility into the creation and use of AI-powered applications in an enterprise-scale environment. It also allows businesses to see how ROI is delivered. IBM Watson OpenScale provides visibility to companies about how AI is created, used, and how ROI is delivered at business level. You can create and deploy trusted AI using the IDE you prefer, and provide data insights to your business and support team about how AI affects business results. Capture payload data, deployment output, and alerts to monitor the health of business applications. You can also access an open data warehouse for custom reporting and access to operations dashboards. Based on business-determined fairness attributes, automatically detects when artificial Intelligence systems produce incorrect results at runtime. Smart recommendations of new data to improve model training can reduce bias.

Scores are available for factual accuracy and context retrieval, as well as guideline adherence and tonality. You can't improve if you don't measure. UpTrain continuously monitors the performance of your application on multiple evaluation criteria and alerts you if there are any regressions. UpTrain allows for rapid and robust experimentation with multiple prompts and model providers. Since their inception, LLMs have been plagued by hallucinations. UpTrain quantifies the degree of hallucination, and the quality of context retrieved. This helps detect responses that are not factually accurate and prevents them from being served to end users.

With just one line of code, you can deploy all major ML frameworks like Pytorch and Onnx. Do you not have your own models? Prebuilt models can be deployed to reduce latency and cost. You can fine-tune models for specific tasks to reduce latency and costs while increasing performance. It's easy to do and you don't have to worry about infrastructure. Integrate with the top ML observability platform to be alerted on feature or prediction drift, compare models versions, and resolve issues quickly. To resolve model performance problems, discover the root causes of prediction and feature drift. Find out which features contribute the most to your model's performance.

VictoriaMetrics Anomaly Detection, a service which continuously scans data stored in VictoriaMetrics to detect unexpected changes in real-time, is a service for detecting anomalies in data patterns. It does this by using user-configurable models of machine learning. VictoriaMetrics Anomaly Detection is a key tool in the dynamic and complex world system monitoring. It is part of our Enterprise offering. It empowers SREs, DevOps and other teams by automating the complex task of identifying anomalous behavior in time series data. It goes beyond threshold-based alerting by utilizing machine learning to detect anomalies, minimize false positives and reduce alert fatigue. The use of unified anomaly scores and simplified alerting mechanisms allows teams to identify and address potential issues quicker, ensuring system reliability.

Datatron provides tools and features that are built from scratch to help you make machine learning in production a reality. Many teams realize that there is more to deploying models than just the manual task. Datatron provides a single platform that manages all your ML, AI and Data Science models in production. We can help you automate, optimize and accelerate your ML model production to ensure they run smoothly and efficiently. Data Scientists can use a variety frameworks to create the best models. We support any framework you use to build a model (e.g. TensorFlow and H2O, Scikit-Learn and SAS are supported. Explore models that were created and uploaded by your data scientists, all from one central repository. In just a few clicks, you can create scalable model deployments. You can deploy models using any language or framework. Your model performance will help you make better decisions.

Fiddler is a pioneer in enterprise Model Performance Management. Data Science, MLOps, and LOB teams use Fiddler to monitor, explain, analyze, and improve their models and build trust into AI. The unified environment provides a common language, centralized controls, and actionable insights to operationalize ML/AI with trust. It addresses the unique challenges of building in-house stable and secure MLOps systems at scale. Unlike observability solutions, Fiddler seamlessly 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 and increase revenue.

Openlayer will accept your data and models. Work with the team to align performance and quality expectations. You can quickly identify the reasons behind failed goals and find a solution. You have all the information you need to diagnose problems. Retrain the model by generating more data that looks similar to the subpopulation. Test new commits in relation to your goals, so that you can ensure a systematic progress without regressions. Compare versions side by side to make informed decisions. Ship with confidence. Save time on engineering by quickly determining what drives model performance. Find the quickest ways to improve your model. Focus on cultivating high quality and representative datasets and knowing the exact data required to boost model performance.

Data analysts, engineers, scientists, and other scientists can be brought together. Automate self-service analytics and machine learning operations. Get results today, build for tomorrow. Dataiku DSS is a collaborative data science platform that allows data scientists, engineers, and data analysts to create, prototype, build, then deliver their data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) You can also use a drag-and-drop visual interface or Python, R, Spark, Scala, Hive notebooks at every step of the predictive dataflow prototyping procedure - from wrangling to analysis and modeling. Visually profile the data at each stage of the analysis. Interactively explore your data and chart it using 25+ built in charts. Use 80+ built-in functions to prepare, enrich, blend, clean, and clean your data. Make use of Machine Learning technologies such as Scikit-Learn (MLlib), TensorFlow and Keras. In a visual UI. You can build and optimize models in Python or R, and integrate any external library of ML through code APIs.

To develop AI solutions, engineers must have a resilient, open, repeatable engineering approach to ensure quality and agility. These efforts have not been able to address the challenges of today's complex environment, which is filled with a variety of tools and rapidly changing data. Platform for collaborative development that automates the control and development of AI applications across multiple persons. To predict customer behavior in real-time, and at scale, we can derive hyper-detailed customer profiles using enterprise data. AI-powered models that can continuously learn and achieve clearly defined business results. Allows organizations to demonstrate compliance with applicable rules and regulations. CognitiveScale's Cortex AI Platform is designed to address enterprise AI use cases using modular platform offerings. Customers use and leverage its capabilities in microservices as part of their enterprise AI initiatives.

FairNow provides organizations with the AI governance tools needed to ensure global compliance, and manage AI risks. FairNow's features, which are centralized, simplified, and empower the entire team, are loved by CPOs and CAIOs. FairNow's platform constantly monitors AI models in order to ensure that each model is fair, audit-ready, and compliant. Top features include: - Intelligent AI risk assessments: Conduct real-time assessment of AI models using their deployment locations in order to highlight potential reputational, financial and operational risks. - Hallucination Detection : Detect errors and unexpected responses. Automated bias evaluations: Automate bias assessments and mitigate algorithmic biased as they happen. Plus: - AI Inventory Centralized Policy Center - Roles & Controls FairNow's AI Governance Platform helps organizations build, purchase, and deploy AI with confidence.

IBM Watson Machine Learning, a full-service IBM Cloud offering, makes it easy for data scientists and developers to work together to integrate predictive capabilities into their applications. The Machine Learning service provides a set REST APIs that can be called from any programming language. This allows you to create applications that make better decisions, solve difficult problems, and improve user outcomes. Machine learning models management (continuous-learning system) and deployment (online batch, streaming, or online) are available. You can choose from any of the widely supported machine-learning frameworks: TensorFlow and Keras, Caffe or PyTorch. Spark MLlib, scikit Learn, xgboost, SPSS, Spark MLlib, Keras, Caffe and Keras. To manage your artifacts, you can use the Python client and command-line interface. The Watson Machine Learning REST API allows you to extend your application with artificial intelligence.

Bigeye is a data observability platform that allows teams to measure, improve and communicate data quality at any scale. A data quality problem can cause an outage that causes trust in the data. Bigeye starts with monitoring to rebuild trust. Before executives see it in a dashboard, find missing or broken reporting data. Before models are retrained, be aware of potential issues in training data. You need to get rid of that uncomfortable feeling that most data is correct most of the time. The status of a pipeline job doesn't tell the entire story. Monitoring the actual data is the best way to make sure data is available for use. Monitoring data-level freshness will ensure that pipelines run on schedule even when ETL orchestrators are down. Learn about any changes in event names, region codes or product types and other categorical data. To ensure that everything is working as it should, detect drops or spikes of row counts, nulls, or blank values.

A platform for AI governance, designed by lawyers with regulatory experience and tailored to your policies and use cases. Businesses must learn how to navigate and comply new legislation and guidelines. AI failures can lead to a loss of customer trust and a decline in product engagement. Teams are faced with AI systems that are more complex and have a greater number of use cases. Our assessments and live model control will help you monitor compliance with your AI systems. Alert users of potential issues or risk. Implementing good AI Governance practices can take a lot of time. Use the built-in automation for importing model data and artifacts and updating documentation. Understand AI compliance within your organization. Give senior stakeholders a complete picture of their AI to make strategic decisions. Share reports with curated audiences. We provide a comprehensive set of policies to ensure legal and regulatory conformance through pre-configured assessment.

WitnessAI builds the guardrails to make AI productive, safe, and usable. Our platform allows enterprises the freedom to innovate, while enjoying the power of generative artificial intelligence, without compromising on privacy or security. With full visibility of applications and usage, you can monitor and audit AI activity. Enforce a consistent and acceptable use policy for data, topics, usage, etc. Protect your chatbots, employee activity, and data from misuse and attack. WitnessAI is building an international team of experts, engineers and problem solvers. Our goal is to build an industry-leading AI platform that maximizes AI's benefits while minimizing its risks. WitnessAI is a collection of security microservices which can be deployed in your environment on-premise, in a sandbox in the cloud, or within your VPC to ensure that data and activity telemetry remain separate from other customers. WitnessAI, unlike other AI governance solutions provides regulatory separation of your information.

AI-powered code scanning can be used to identify and fix broken authentications, logic bugs, outdated dependency, and much more. ZeroPath is easy to set up and provides continuous human-level application protection, PR reviews, etc. ZeroPath can be set up in less than 2 minutes with your existing CI/CD. Supports Github GitLab and Bitbucket. ZeroPath reports fewer false-positives and finds more bugs than comparables. Find broken authentication and logic bugs. ZeroPath releases a press release instead of reporting bugs when it is confident that it will not break your application. Make sure your products are secure, without slowing development.

AI-powered cloud observation platform. Middleware platform helps you identify, understand and resolve issues across your cloud infrastructure. AI will detect and diagnose all issues infra, application and infrastructure and provide better recommendations for fixing them. Dashboard allows you to monitor metrics, logs and traces in real time. The best and fastest results with the least amount of resources. Bring all metrics, logs and traces together into a single timeline. A full-stack platform for observability will give you complete visibility into your cloud. Our AI-based algorithms analyze your data and make suggestions for what you should fix. Your data is yours. Control your data collection, and store it in your cloud to save up to 10x the cost. Connect the dots to determine where the problem began and where it ended. Fix problems before users report them. The users get a comprehensive solution for cloud observability at a single location. It's also too cost-effective.

We help you transform AI by protecting it against cyber threats, privacy concerns, and safety incidents. We help you understand the ways cybercriminals can exploit AI applications using information about your AI models and data. We help you test the resilience of your AI application with scenario-based attacks simulations by a threat actor with advanced capabilities. We audit your AI application integrity using a comprehensive analysis that is based on robustness focused stress testing methodology. We have developed a new attack against AI-driven facial detection systems. Due to this attack, a AI system will recognize you in a different way.

CUJOAI is the global leader in artificial intelligence development and application. This allows for better security, control, and privacy of connected devices at home and in businesses. CUJOAI brings together fixed network, public Wi-Fi and mobile operators around the globe a complete portfolio to provide end users with a seamless integrated suite of Digital Life Protection services. This allows them to improve their network monitoring, intelligence, and protection capabilities. End-user networks are given unprecedented visibility and actionable insights by leveraging artificial intelligence and advanced technology for data access. This includes analyzing connected devices, identifying security and privacy threats, and analyzing applications and services. Real-time network data and artificial intelligence combine to create safer and more intelligent environments for everyone and their connected devices.

CrowdStrike's Charlotte AI is a cutting-edge, AI-driven cybersecurity product that combines machine learning with behavioral analysis to enhance threat detection. It continuously monitors network traffic, endpoints and cloud environments in order to identify patterns or anomalies that may indicate malicious behavior. Charlotte AI uses advanced algorithms to predict and detect sophisticated cyber attacks in real-time. This reduces response times and improves overall threat prevention. Charlotte AI's ability to analyze large amounts of data to provide actionable insights allows teams to address vulnerabilities and prevent incidents from occurring. Charlotte AI is a part of CrowdStrike’s broader cybersecurity suite, which helps organizations stay ahead of new threats with cutting-edge automated defense capabilities.

Even the best AI models may have hidden risks. Identifying and addressing potential problems before they affect your business will ensure smooth AI adoption and compliance. AI applications are susceptible to sophisticated and new attacks. Protect your models and applications against data poisoning, prompt injecting, and other emerging threats. Use cutting-edge AI services in the public domain with confidence. We ensure responsible use, prevent data leaks and let you focus on innovation. The TROJAI platform allows organizations to comply to privacy regulations and benchmarks like the OWASP AI framework by testing models before deployment, and protecting applications against things such as sensitive information loss once deployed.

ZenGuard AI, a security platform, is designed to protect AI-driven agents for customer experience from potential threats and ensure they operate safely. ZenGuard is a low-latency security platform developed by experts at leading tech companies such as Google, Meta and Amazon. It mitigates risks associated with AI agents that use large language models. Protects AI agents from prompt injection attacks, by detecting and neutralizing attempts to manipulate. This ensures secure LLM operation. Identifies and manages confidential information to prevent data leaks, and ensure compliance with privacy laws. Content policies are enforced by preventing AI agents from discussing forbidden topics, maintaining brand integrity and ensuring user safety. The platform provides a user friendly interface for policy configuration and allows real-time updates of security settings.

Scalable, self-managed applications that are resilient and self-managed can be achieved by combining observability and automated orchestration. Monitoring and observability tools generate huge volumes of data every second. They capture metrics, logs and traces on all aspects of dynamic, complex applications. It's up to humans to make sense of and troubleshoot all this data. They are stuck in a never-ending loop of responding to alerts and identifying root causes before deciding the best course of action. The process hasn’t changed much in decades and is still labor-intensive and reactive. Causely eliminates the need for manual troubleshooting, by capturing causality within software. This closes the gap between observability to action. For the first time ever, the entire lifecycle for detecting, root cause analysis and remediation of application defects is fully automated. Causely identifies and resolves defects in real-time so that applications can scale at high performance.

The Xilinx AI development platform for AI Inference on Xilinx hardware platforms consists optimized IP, tools and libraries, models, examples, and models. It was designed to be efficient and easy-to-use, allowing AI acceleration on Xilinx FPGA or ACAP. Supports mainstream frameworks as well as the most recent models that can perform diverse deep learning tasks. A comprehensive collection of pre-optimized models is available for deployment on Xilinx devices. Find the closest model to your application and begin retraining! This powerful open-source quantizer supports model calibration, quantization, and fine tuning. The AI profiler allows you to analyze layers in order to identify bottlenecks. The AI library provides open-source high-level Python and C++ APIs that allow maximum portability from the edge to the cloud. You can customize the IP cores to meet your specific needs for many different applications.

Protect AI scans your ML lifecycle for security vulnerabilities and helps you to deliver compliant and secure ML models and AI apps. Enterprises need to understand the unique threat landscape of their AI & ML system throughout its lifecycle and quickly address it to eliminate any potential risks. Our products offer threat visibility, security testing, remediation, and remediation. Jupyter Notebooks provide powerful tools for data scientists to analyze data, create models, test experiments, and share the results with their peers. The notebooks include live code, visualizations and data as well as text. They pose security risks, and current cybersecurity solutions are not able to evaluate them. NB Defense is completely free to use. It scans a single or a collection of notebooks for common security problems, identifies and guides you in remediation.

Deeploy allows you to maintain control over your ML models. You can easily deploy your models to our responsible AI platform without compromising transparency, control and compliance. Transparency, explainability and security of AI models are more important today than ever. You can monitor the performance of your models with confidence and accountability if you use a safe, secure environment. Over the years, our experience has shown us the importance of human interaction with machine learning. Only when machine-learning systems are transparent and accountable can experts and consumers provide feedback, overrule their decisions when necessary, and grow their trust. We created Deeploy for this reason.