Best Artificial Intelligence Software for Azure Machine Learning

DSVMs are Azure Virtual Machine images, pre-installed, configured and tested with several popular tools that are commonly used for data analytics, machine learning and AI training. Consistent setup across team, promote sharing and collaboration, Azure scale and management, Near-Zero Setup, full cloud-based desktop for data science. Quick, Low friction startup for one to many classroom scenarios and online courses. Ability to run analytics on all Azure hardware configurations with vertical and horizontal scaling. Pay only for what you use, when you use it. Readily available GPU clusters with Deep Learning tools already pre-configured. Examples, templates and sample notebooks built or tested by Microsoft are provided on the VMs to enable easy onboarding to the various tools and capabilities such as Neural Networks (PYTorch, Tensorflow, etc.), Data Wrangling, R, Python, Julia, and SQL Server.

NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.

Get in minutes what used to take years to build. Simple, customizable, scalable, secure, ML monitoring. Everything you need to deploy, maintain and improve ML in production. Superwise is an open platform that integrates with any ML stack and connects to your choice of communication tools. Want to take it further? Superwise is API-first and everything (and we mean everything) is accessible via our APIs. All from the comfort of the cloud of your choice. When it comes to ML monitoring you have full self-service control over everything. Configure metrics and policies through our APIs and SDK or simply select a monitoring template and set the sensitivity, conditions, and alert channels of your choice. Try Superwise out or contact us to learn more. Easily create alerts with Superwise’s ML monitoring policy templates and builder. Select from dozens of pre-build monitors ranging from data drift to equal opportunity, or customize policies to incorporate your domain expertise.

The AI revolution is here. Innovation is moving at light speed, and the regulation landscape is constantly evolving. How can you make sure that your AI systems — and those of your vendors — remain secure, trustworthy, and compliant? Cranium helps cybersecurity and data science teams understand everywhere that AI is impacting their systems, data or services. Secure your organization’s AI and machine learning systems to ensure they are compliant and trustworthy, without interrupting your workflow. Protect against adversarial threats without impacting how your team trains, tests and deploys AI models. Increase AI regulatory awareness and alignment within your organization. Showcase the security and trustworthiness of your AI systems.

BotCore is an accelerator that enables organizations to build customized conversational bots powered by artificial intelligence. It can leverage any AI service available today and will also scale for future services. It is fully deployable into Microsoft Azure and leverages many of the features available in it.

MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components. Record and query experiments: code, data, config, and results. Package data science code in a format to reproduce runs on any platform. Deploy machine learning models in diverse serving environments. Store, annotate, discover, and manage models in a central repository. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs. An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects.

ModelOp is the leading AI governance software that helps enterprises safeguard all AI initiatives, including generative AI, Large Language Models (LLMs), in-house, third-party vendors, embedded systems, etc., without stifling innovation. Corporate boards and C‑suites are demanding the rapid adoption of generative AI but face financial, regulatory, security, privacy, ethical, and brand risks. Global, federal, state, and local-level governments are moving quickly to implement AI regulations and oversight, forcing enterprises to urgently prepare for and comply with rules designed to prevent AI from going wrong. Connect with AI Governance experts to stay informed about market trends, regulations, news, research, opinions, and insights to help you balance the risks and rewards of enterprise AI. ModelOp Center keeps organizations safe and gives peace of mind to all stakeholders. Streamline reporting, monitoring, and compliance adherence across the enterprise.