Find and compare the best Artificial Intelligence software for Azure Machine Learning in 2025
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Use the comparison tool below to compare the top Artificial Intelligence software for Azure Machine Learning on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Azure Data Science Virtual Machines
Microsoft
$0.005DSVMs are Azure Virtual Machine Images that have been pre-configured, configured, and tested with many popular tools that are used for data analytics and machine learning. A consistent setup across the team promotes collaboration, Azure scale, management, Near-Zero Setup and full cloud-based desktop to support data science. For one to three classroom scenarios or online courses, it is easy and quick to set up. Analytics can be run on all Azure hardware configurations, with both vertical and horizontal scaling. Only pay for what you use and when you use it. Pre-configured Deep Learning tools are readily available in GPU clusters. To make it easy to get started with the various tools and capabilities, such as Neural Networks (PYTorch and Tensorflow), templates and examples are available on the VMs. ), Data Wrangling (R, Python, Julia and SQL Server). -
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NVIDIA Triton Inference Server
NVIDIA
FreeNVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production. -
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Superwise
Superwise
FreeYou 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. -
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Cranium
Cranium
The AI revolution has arrived. The regulatory landscape is constantly changing, and innovation is moving at lightning speed. How can you ensure that your AI systems, as well as those of your vendors, remain compliant, secure, and trustworthy? Cranium helps cybersecurity teams and data scientists understand how AI impacts their systems, data, or services. Secure your organization's AI systems and machine learning systems without disrupting your workflow to ensure compliance and trustworthiness. Protect your AI models from adversarial threats while maintaining the ability to train, test and deploy them. -
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BotCore
Acuvate Software
BotCore is an accelerator that allows organizations to create customized conversational bots powered artificial intelligence. It can use any AI service and will scale for future services. It can be fully deployed into Microsoft Azure and takes advantage of many of its features. -
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MLflow
MLflow
MLflow is an open-source platform that manages the ML lifecycle. It includes experimentation, reproducibility and deployment. There is also a central model registry. MLflow currently has four components. Record and query experiments: data, code, config, results. Data science code can be packaged in a format that can be reproduced on any platform. Machine learning models can be deployed in a variety of environments. A central repository can store, annotate and discover models, as well as manage them. The MLflow Tracking component provides an API and UI to log parameters, code versions and metrics. It can also be used to visualize the results later. MLflow Tracking allows you to log and query experiments using Python REST, R API, Java API APIs, and REST. An MLflow Project is a way to package data science code in a reusable, reproducible manner. It is based primarily upon conventions. The Projects component also includes an API and command line tools to run projects. -
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ModelOp
ModelOp
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. -
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Microsoft Intelligent Data Platform
Microsoft
The Microsoft Intelligent Data Platform (MDP) is an integrated data-AI platform that helps organizations to adapt quickly, add intelligence to their applications, and generate predictions insights. It unifies databases and analytics, allowing businesses to spend more time creating value than managing their data estate. The platform provides real-time data intelligence and seamless integration of data, enabling powerful decision-making. It allows organizations to gain real-time insights by breaking down data silos. Platform capabilities include accelerating innovations, improving productivity with automation and AI and enhancing agility through anticipating changes and better decision-making. It also provides comprehensive data security throughout the lifecycle of data, protecting hybrid and multicloud environments.
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