Alternatives to Snitch AI

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 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.

Transparency, choice and control are key to trust. Organizations have the opportunity to leverage these moments to build trust, and provide more valuable experiences. People expect greater control over their data. We offer privacy and data governance automation to help organizations better understand and comply with regulatory requirements. We also operationalize risk mitigation to ensure transparency and choice for individuals. Your organization will be able to achieve data privacy compliance quicker and build trust. Our platform helps to break down silos between processes, workflows, teams, and people to operationalize regulatory compliance. It also allows for trusted data use. Building proactive privacy programs that are rooted in global best practice and not just reacting to individual regulations is possible. To drive mitigation and risk-based decision-making, gain visibility into unknown risks. Respect individual choice and integrate privacy and security by default in the data lifecycle.

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.

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Machine learning models can be deployed at scale with greater accuracy. With more models in production, R&D can be turned into ROI. Seldon reduces time to value so models can get to work quicker. Scale with confidence and minimize risks through transparent model performance and interpretable results. Seldon Deploy cuts down on time to production by providing production-grade inference servers that are optimized for the popular ML framework and custom language wrappers to suit your use cases. Seldon Core Enterprise offers enterprise-level support and access to trusted, global-tested MLOps software. Seldon Core Enterprise is designed for organizations that require: - Coverage for any number of ML models, plus unlimited users Additional assurances for models involved in staging and production - You can be confident that their ML model deployments will be supported and protected.

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!

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.

Mona is a flexible and intelligent monitoring platform for AI / ML. Data science teams leverage Mona’s powerful analytical engine to gain granular insights about the behavior of their data and models, and detect issues within specific segments of data, in order to reduce business risk and pinpoint areas that need improvements. Mona enables tracking custom metrics for any AI use case within any industry and easily integrates with existing tech stacks. In 2018, we ventured on a mission to empower data teams to make AI more impactful and reliable, and to raise the collective confidence of business and technology leaders in their ability to make the most out of AI. We have built the leading intelligent monitoring platform to provide data and AI teams with continuous insights to help them reduce risks, optimize their operations, and ultimately build more valuable AI systems. Enterprises in a variety of industries leverage Mona for NLP/NLU, speech, computer vision, and machine learning use cases. Mona was founded by experienced product leaders from Google and McKinsey&Co, is backed by top VCs, and is HQ in Atlanta, Georgia. In 2021, Mona was recognized by Gartner as a Cool Vendor in AI Operationalization and Engineering.

Our easy-to-implement AI Governance Control Plane ensures AI usage adheres to corporate policies. SurePath AI helps you to reduce complexity, gain visibility and increase AI adoption in a secure manner. Native integrations with your existing security solutions. Private models and enterprise data sources. Native support for SSO, SCIM and SIEM. Detect AI usage at the network level. Control access to sensitive data and inspect requests. Redact sensitive data from requests to public models. Modifications in-line of requests can be made to improve productivity and reduce risk. Redirect traffic to private AI models. Use SurePath AI’s private model access control as your own, internally branded enterprise AI Portal. Policy-based controls enrich request with only enterprise data that users have been granted access to. This gives meaningful responses based upon relevant business context. Users' prompts will automatically be enhanced to align output with enterprise objectives.

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.

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.

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.

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.

Select the subset of data that has the greatest impact on the accuracy of your model. This allows you to improve your model by using the best data in retraining. Reduce data redundancy and bias and focus on edge cases to get the most from your data. Lightly's algorithms are capable of processing large amounts of data in less than 24 hour. Connect Lightly with your existing buckets to process new data automatically. Our API automates the entire data selection process. Use the latest active learning algorithms. Combining active- and selfsupervised learning algorithms lightly for data selection. Combining model predictions, embeddings and metadata will help you achieve your desired distribution of data. Improve your model's performance by understanding data distribution, bias and edge cases. Manage data curation and keep track of the new data for model training and labeling. Installation is easy via a Docker Image and cloud storage integration. No data leaves your infrastructure.

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.

Easy management, deployment and monitoring of machine learning models for supercharging MLOps. Available for all organizations on the best AI platform. You can use navio for various machine learning operations across your entire artificial intelligence landscape. Machine learning can be integrated into your business workflow to make a tangible, measurable impact on your business. navio offers various Machine Learning Operations (MLOps), which can be used to support you from the initial model development phase to the production run of your model. Automatically create REST endspoints and keep track the clients or machines that interact with your model. To get the best results, you should focus on exploring and training your models. You can also stop wasting time and resources setting up infrastructure. Let navio manage all aspects of product ionization so you can go live quickly with your machine-learning models.

Your ML teams can auto-diagnose and improve their data and models to create robust and performant AI models. Only platform that can automatically diagnose data and models, empowering ML team to deliver robust and performant AI model faster. Camera noise, shadows, sign stickers, and other factors are covered. Confirmed using real-world images of models that consistently fail. While improving model accuracy by 0.2%. Our mission is to transform the way that the next generation AI systems are built. We need to create AI systems that are trusted by both users and companies if we want to use AI in our homes, offices, hospitals, roads, and businesses. We are leading AI researchers and professors at ETH Zurich. Our expertise includes formal methods, symbolic reasoning and machine learning. LatticeFlow was founded with the goal to create the first platform that allows companies to develop robust AI models that can be used in the wild.

Our platform dynamically evaluates and mitigates AI risk in models and datasets relating to bias, proxy bias, and fairness. Our Responsible AI technology provides greater visibility into AI models, and makes them more understandable and interpretable. Our AI robustness assurance algorithms are based on research and identify and mitigate risk related to lack robustness. Our platform provides a comprehensive review of the AI landscape, including MRM and AI regulations, and provides a deeper risk analysis. AI risks in both internal AI systems and AI systems provided by a third party need to be assessed, and remedied. SigmaRed platform allows comprehensive third-party AI Risk Management (AI TPRM). It reduces cycle time for AI risk assessments and provides deep visibility, control and stakeholder-based reports.

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.

Pipelines are permanent, models are temporary. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform to automate everything, from data extraction to model deployment. Automate everything, from data extraction to model installation. Automatically store every model, experiment, and artifact. Monitor and deploy models in a Kubernetes cluster. Just point to your code and hit "run". Valohai launches workers and runs your experiments. Then, Valohai shuts down the instances. You can create notebooks, scripts, or shared git projects using any language or framework. Our API allows you to expand endlessly. Track each experiment and trace back to the original training data. All data can be audited and shared.

ModelOp is a leading AI governance tool that helps enterprises safeguard AI initiatives including generative AI and Large Language Models. It also protects in-house vendors, third-party vendors and embedded systems without stifling the innovation. Corporate boards and C suites demand the rapid adoption of generative AI, but face financial risks, regulatory, privacy, security, and ethical issues. Governments at all levels, including federal, state and local, are implementing AI regulations and overseeing the industry quickly. This forces enterprises to prepare and comply with rules that prevent AI from going awry. Connect with AI Governance specialists to stay informed on market trends, regulations and news. You can also get insights and opinions from experts. ModelOp Center helps organizations stay safe and provides peace of mind for all stakeholders. Streamline reporting and compliance across the enterprise.

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.

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.

Amazon DevOps Guru, powered by machine learning (ML), is a service that makes it easy to improve operational performance and availability of applications. DevOps Guru detects abnormal operating patterns and helps you to identify them before they impact your customers. To identify abnormal application behavior, such as increased latency, error rates or resource limitations, DevOps Guru employs ML models that are based on data collected over years by Amazon.com Operational Excellence and Amazon.com. It helps to detect critical errors that could cause service interruptions. The DevOps Guru automatically alerts you when it detects a critical issue. It provides context and details about the root cause and the possible consequences.

Automaton AI's Automaton AI's DNN model and training data management tool, ADVIT, allows you to create, manage, and maintain high-quality models and training data in one place. Automated optimization of data and preparation for each stage of the computer vision pipeline. Automate data labeling and streamline data pipelines in house Automate the management of structured and unstructured video/image/text data and perform automated functions to refine your data before each step in the deep learning pipeline. You can train your own model with accurate data labeling and quality assurance. DNN training requires hyperparameter tuning such as batch size, learning rate, and so on. To improve accuracy, optimize and transfer the learning from trained models. After training, the model can be put into production. ADVIT also does model versioning. Run-time can track model development and accuracy parameters. A pre-trained DNN model can be used to increase the accuracy of your model for auto-labeling.

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.

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.

Simplify your MLOps pipelines. ZenML allows you to manage, deploy and scale any infrastructure. ZenML is open-source and free. Two simple commands will show you the magic. ZenML can be set up in minutes and you can use all your existing tools. ZenML interfaces ensure your tools work seamlessly together. Scale up your MLOps stack gradually by changing components when your training or deployment needs change. Keep up to date with the latest developments in the MLOps industry and integrate them easily. Define simple, clear ML workflows and save time by avoiding boilerplate code or infrastructure tooling. Write portable ML codes and switch from experiments to production in seconds. ZenML's plug and play integrations allow you to manage all your favorite MLOps software in one place. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code.

Manage data security and risk across your entire stack. Data collection and data shaping occurs in your customer-facing assets. Source code with data flow and third-party components. Data breaches, the sharing/selling of information and targeted advertising without consent are all factors that contribute to a lack of trust in how companies handle their data. Don't lose your customers' trust if you expose them to privacy risks. Our data protection plans will protect sensitive information for your organization and ensure their privacy. Our privacy program will protect all the data of your business. Our privacy compliance software will protect your enterprise from fines if you do not comply with security policies. Zendata's platform does not require any code to protect your customers' information and ensure compliance.

Sagify is a complement to AWS Sagemaker. It hides all low-level details so you can focus 100% of Machine Learning. Sagemaker is the ML engine, and Sagify the data science-friendly interface. To train, tune, and deploy hundreds ML models, you only need to implement two functions, a train AND a predict. You can manage all your ML models from one location without having to deal with low-level engineering tasks. No more sloppy ML pipelines. Sagify offers 100% reliable AWS training and deployment. Only 2 functions are required to train, tune and deploy hundreds ML models.

FinetuneFast allows you to fine-tune AI models, deploy them quickly and start making money online. Here are some of the features that make FinetuneFast unique: - Fine tune your ML models within days, not weeks - The ultimate ML boilerplate, including text-to-images, LLMs and more - Build your AI app to start earning online quickly - Pre-configured scripts for efficient training of models - Efficient data load pipelines for streamlined processing Hyperparameter optimization tools to improve model performance - Multi-GPU Support out of the Box for enhanced processing power - No-Code AI Model fine-tuning for simple customization - Model deployment with one-click for quick and hassle free deployment - Auto-scaling Infrastructure for seamless scaling of your models as they grow - API endpoint creation for easy integration with other system - Monitoring and logging for real-time performance monitoring

Create statistically optimized representations for your data to train accurate ML models with limited, sparse and high-dimensional data. Dark Matter accelerates training and improves model performance by learning how to extract complex relationships from your existing data. This is done without extensive feature engineering and resource-intensive deep-learning. Data scientists can spend less time on data to solve hard problems. Dark Matter significantly improved the model precision and f1 score in predicting customer convertion in the online retail sector. When trained on an embedded optimization learned from sparse and high-dimensional data, model performance metrics improved across board. The banking industry improved its predictions of customer churn by training XGBoost with a better representation. No matter what model or domain you are in, you can improve your pipeline.

It works with Jupyter Notebooks or any other Python environment. Modelbit will deploy your model and all its dependencies to production by calling modelbi.deploy. Modelbit's ML models can be called from your warehouse just as easily as a SQL function. They can be called directly as a REST-endpoint from your product. Modelbit is backed up by your git repository. GitHub, GitLab or your own. Code review. CI/CD pipelines. PRs and merge request. Bring your entire git workflow into your Python ML models. Modelbit integrates seamlessly into Hex, DeepNote and Noteable. Modelbit lets you take your model directly from your cloud notebook to production. Tired of VPC configurations or IAM roles? Redeploy SageMaker models seamlessly to Modelbit. Modelbit's platform is available to you immediately with the models that you have already created.

Open-Source AI layer for databases. Machine Learning capabilities can be integrated directly into your data domain to increase efficiency and productivity. MindsDB makes it easy to create, train, and then test ML models. Then publish them as virtual AI tables into databases. Integrate seamlessly with all major databases. SQL queries can be used to manipulate ML models. You can increase model training speed using GPU without affecting the performance of your database. Learn how the ML model arrived at its conclusions and what factors affect prediction confidence. Visual tools that allow you to analyze model performance. SQL and Python queries that return explanation insights in a single code. You can use What-if analysis to determine confidence based upon different inputs. Automate the process for applying machine learning using the state-of the-art Lightwood AutoML library. Machine Learning can be used to create custom solutions in your preferred programming language.

Amazon SageMaker Clarify is a machine learning (ML), development tool that provides purpose-built tools to help them gain more insight into their ML training data. SageMaker Clarify measures and detects potential bias using a variety metrics so that ML developers can address bias and explain model predictions. SageMaker Clarify detects potential bias in data preparation, model training, and in your model. You can, for example, check for bias due to age in your data or in your model. A detailed report will quantify the different types of possible bias. SageMaker Clarify also offers feature importance scores that allow you to explain how SageMaker Clarify makes predictions and generates explainability reports in bulk. These reports can be used to support internal or customer presentations and to identify potential problems with your model.

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.

CentML speeds up Machine Learning workloads by optimising models to use hardware accelerators like GPUs and TPUs more efficiently without affecting model accuracy. Our technology increases training and inference speed, lowers computation costs, increases product margins using AI-powered products, and boosts the productivity of your engineering team. Software is only as good as the team that built it. Our team includes world-class machine learning, system researchers, and engineers. Our technology will ensure that your AI products are optimized for performance and cost-effectiveness.

Vaex.io aims to democratize the use of big data by making it available to everyone, on any device, at any scale. Your prototype is the solution to reducing development time by 80%. Create automatic pipelines for every model. Empower your data scientists. Turn any laptop into an enormous data processing powerhouse. No clusters or engineers required. We offer reliable and fast data-driven solutions. Our state-of-the art technology allows us to build and deploy machine-learning models faster than anyone else on the market. Transform your data scientists into big data engineers. We offer comprehensive training for your employees to enable you to fully utilize our technology. Memory mapping, a sophisticated Expression System, and fast Out-of-Core algorithms are combined. Visualize and explore large datasets and build machine-learning models on a single computer.