Best AI Governance Tools in New Zealand

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.

AI Governance empowers you to make risk-based decisions, whether you're purchasing AI from vendors and third-parties or building AI internally. Manage AI projects, track open-source and proprietary models, govern the training datasets and gain visibility into AI usage and development within your organization through standard intake mechanisms, project integrations and AI/ML scans. Facilitate trust during AI onboarding, delivery, and development. Streamline the decision-making process across compliance, risk management and technology stakeholders in order to ensure alignment with global standards, laws and organizational policies. All this can be done on a centralized, secure platform. Detect and address shadow AI, and deviations from the intended AI use. Collaboration between AI governance committees, cross-functional stakeholders and AI governance committees is encouraged. Control and monitor AI/ML technology within a centralized command center.

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.

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.

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.

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.

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.

Every model requires governance to ensure ethical and responsible decision-making in the business. IBM® watsonx.governance™ toolkit for AI governance allows you to direct, manage and monitor your organization's AI activities. It uses software automation to enhance your ability to mitigate risk, manage regulatory requirements, and address ethical concerns when it comes to both generative AI (ML) and machine learning models. Access automated and scalable compliance, governance and risk tools that cover financial management, IT governance, IT governance, operational risk and policy management. Proactively detect model risks and mitigate them while translating AI regulations to enforceable policies that can be enforced automatically.

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.

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.

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.

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.

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.

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.

Azure AI Content Security is a platform for content moderation that uses AI to ensure your content remains safe. AI models can detect offensive or inappropriate text and images in seconds, allowing you to create better online experiences. Language models analyze multilingual texts, both in short and long form with an understanding of context, semantics, and syntax. Using the latest Florence technology, vision models can recognize images and detect objects. AI content classifiers can identify content that is sexual, violent, hateful, or self-harming with high levels granularity. The severity of content moderation is measured on a scale from low to high.

Augmented intelligence can take your data-driven business to the next level. BasisAI manages the entire development process of AI systems. Complete lifecycle AI solution: From bespoke AI algorithms to production-grade AI applications and ongoing multi-year administration. Rapid time to market: From your data to scalable, containerized deployment of real-time AI engines in weeks. No black boxes. AI governance, fairness, and compliance built-in You can retain control over your enterprise data on AWS, GCP, or any other cloud infrastructure. We offer strategic guidance to help you build the right structures, frameworks, and technologies that will allow you to achieve long-term scaling. We help you move beyond algorithms and experiments to take control of your capability development through exploratory use cases sessions and system design workshops.

Mind Foundry is an artificial Intelligence company that combines research, innovation, usability, and usability to empower teams using AI that is built for people. Mind Foundry was founded by world-leading academics. It develops AI solutions to help public and private sector organisations tackle high-stakes issues. Mind Foundry focuses on human outcomes and long-term impacts of AI interventions. Our platform is intrinsically collaborative and powers AI design, testing, and deployment. It enables stakeholders to responsibly manage their AI investments with a key focus on performance and efficiency as well as ethical impact. It is based on scientific principles and the understanding that ethics and transparency can only be added after the fact. The combination of quantitative and experience design makes collaboration between humans, AI and AI easier, more efficient, and more powerful.

Standardize your AI governance efforts across different stakeholders, ensure regulatory readiness for your governance processes, and manage and measure your AI compliance and risks. You can transform your AI/ML projects from being managed by a variety of teams and processes into a centralized repository for trusted governance. Keep up-to-date on the latest regulations and standards by downloading AI Policy Packs. These packs meet all current and future regulations. Credo AI is an intelligence layer which sits on top your AI infrastructure and converts technical artifacts to actionable risk and compliance insights for product leaders and data scientists as well as governance teams. Credo AI is an intelligence layer which sits on top your technical and business infrastructure. It converts technical artifacts into compliance scores and risk scores.

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.

Empowering AI governance leaders with advanced insights & risk intelligence to drive responsible AI innovation and compliance.

Observability allows you to detect data issues and ML problems faster, to deliver continuous improvements and to avoid costly incidents. Start with reliable data. Monitor data in motion for quality issues. Pinpoint data and models drift. Identify the training-serving skew, and proactively retrain. Monitor key performance metrics continuously to detect model accuracy degradation. Identify and prevent data leakage in generative AI applications. Protect your generative AI apps from malicious actions. Improve AI applications by using user feedback, monitoring and cross-team collaboration. Integrate in just minutes with agents that analyze raw data, without moving or replicating it. This ensures privacy and security. Use the proprietary privacy-preserving technology to integrate the WhyLabs SaaS Platform with any use case. Security approved by healthcare and banks.

Use Qlik®, Staige™, to make AI real. It will provide a trusted foundation for data, automation, actionable forecasts, and a company-wide impact. AI is not just experiments and initiatives - it's a whole ecosystem of files, scripts and results. We've partnered up with the best sources to provide you with integrations that will save time, enable better management, and validate the quality of your data. Automate the delivery and management of real-time AWS data to data lakes or warehouses, and make this data easily accessible via a governed catalogue. With our new integration with Amazon Bedrock you can easily connect foundational large-language models (LLMs), including A21 Labs Amazon Titan, Anthropic Cohere and Meta. AWS customers can leverage AI-driven insights with ease using seamless integration with Amazon Bedrock.

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.

SolasAI software detects and removes discrimination & bias from a customer’s decisioning models. It can be used in a variety of applications, including credit & insurance underwriting and predictive marketing. We provide trust and transparency in artificial intelligence, machine-learning, and standard statistical model. SolasAI can help you if you're tired of paying for expensive experts that don't agree and then leaving the hard work of fixing problems to your expensive data scientists who are overworked. We keep up with the latest signals and decisions from courts, regulators and law makers as well as the newest and best technology trends in AI and fairness. SolasAI has this built in so you don't need to do it yourself.

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.