Best ML Model Deployment Tools for Visual Studio Code
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Compare the Top ML Model Deployment Tools that integrate with Visual Studio Code as of June 2025
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This a list of ML Model Deployment tools that integrate with Visual Studio Code. Use the filters on the left to add additional filters for products that have integrations with Visual Studio Code. View the products that work with Visual Studio Code in the table below.
What are ML Model Deployment Tools for Visual Studio Code?
Machine learning model deployment tools, also known as model serving tools, are platforms and software solutions that facilitate the process of deploying machine learning models into production environments for real-time or batch inference. These tools help automate the integration, scaling, and monitoring of models after they have been trained, enabling them to be used by applications, services, or products. They offer functionalities such as model versioning, API creation, containerization (e.g., Docker), and orchestration (e.g., Kubernetes), ensuring that the models can be deployed, maintained, and updated seamlessly. These tools also monitor model performance over time, helping teams detect model drift and maintain accuracy. Compare and read user reviews of the best ML Model Deployment tools for Visual Studio Code currently available using the table below. This list is updated regularly.
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Databricks Data Intelligence Platform
Databricks
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker. -
2
Azure AI Foundry
Microsoft
Azure AI Foundry is a unified application platform for your entire organization in the age of AI. Azure AI Foundry helps bridge the gap between cutting-edge AI technologies and practical business applications, empowering organizations to harness the full potential of AI efficiently and effectively. Azure AI Foundry is designed to empower your entire organization—developers, AI engineers, and IT professionals—to customize, host, run, and manage AI solutions with greater ease and confidence. This unified approach simplifies the development and management process, helping all stakeholders focus on driving innovation and achieving strategic goals. Azure AI Foundry Agent Service is a powerful component designed to facilitate the seamless operation of AI agents throughout the entire lifecycle—from development and deployment to production. -
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Intel Open Edge Platform
Intel
The Intel Open Edge Platform simplifies the development, deployment, and scaling of AI and edge computing solutions on standard hardware with cloud-like efficiency. It provides a curated set of components and workflows that accelerate AI model creation, optimization, and application development. From vision models to generative AI and large language models (LLM), the platform offers tools to streamline model training and inference. By integrating Intel’s OpenVINO toolkit, it ensures enhanced performance on Intel CPUs, GPUs, and VPUs, allowing organizations to bring AI applications to the edge with ease. -
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Windows AI Foundry
Microsoft
Windows AI Foundry is a unified, reliable, and secure platform supporting the AI developer lifecycle from model selection, fine-tuning, optimizing, and deployment across CPU, GPU, NPU, and cloud. It integrates tools like Windows ML, enabling developers to bring their own models and deploy them efficiently across the silicon partner ecosystem, including AMD, Intel, NVIDIA, and Qualcomm, spanning CPU, GPU, and NPU. Foundry Local allows developers to pull in their favorite open source models and make their apps smarter. It offers ready-to-use AI APIs powered by on-device models, optimized for efficiency and performance on Copilot+ PC devices with minimal setup required. These APIs include capabilities such as text recognition (OCR), image super resolution, image segmentation, image description, and object erasing. Developers can customize Windows inbox models with their own data using LoRA for Phi Silica. -
5
DVC
iterative.ai
Data Version Control (DVC) is an open source version control system tailored for data science and machine learning projects. It offers a Git-like experience to organize data, models, and experiments, enabling users to manage and version images, audio, video, and text files in storage, and to structure their machine learning modeling process into a reproducible workflow. DVC integrates seamlessly with existing software engineering tools, allowing teams to define any aspect of their machine learning projects, data and model versions, pipelines, and experiments, in human-readable metafiles. This approach facilitates the use of best practices and established engineering toolsets, reducing the gap between data science and software engineering. By leveraging Git, DVC enables versioning and sharing of entire machine learning projects, including source code, configurations, parameters, metrics, data assets, and processes, by committing DVC metafiles as placeholders.
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