AI/ML
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Jul 6, 2023
Conversational AI Made Easy: Developing a Chatbot Demo from Scratch Using Rasa and Docker
We walk through building and deploying a conversational AI chatbot using Rasa and Docker, highlighting the importance of containerization for scalability, consistency, and simplified management of machine learning models.
Ajeet Singh RainaandHarsh ManvarRead now
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Jun 20, 2023
Full-Stack Reproducibility for AI/ML with Docker and Kaskada
Learn how Docker and Kaskada improve and accelerate the machine learning development cycle.
Marc SherwoodandRyan MichaelRead now
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Mar 23, 2023
Effortlessly Build Machine Learning Apps with Hugging Face’s Docker Spaces
Learn about the Hugging Face Hub and how to use its Docker Spaces to build machine learning apps effortlessly.
Ajeet Singh Raina,Nate Raw,andOmar SansevieroRead now
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Mar 23, 2023
Docker and Hugging Face Partner to Democratize AI
We’re excited to announce that Hugging Face and Docker are partnering to democratize AI and make it more accessible to software engineers!
Omar SansevieroRead now
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Aug 25, 2022
How to Develop and Deploy a Customer Churn Prediction Model Using Python, Streamlit, and Docker
Customer churn is challenging, but we can combat it! Learn how Python, Streamlit, and Docker help you build a predictive model to minimize churn.
Ajeet Singh RainaandSoniya MehtaRead now
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Aug 16, 2022
Build and Deploy a Retail Store Items Detection System Using No-Code AI Vision at the Edge
In this tutorial, you’ll learn how to build a retail store items detection system using Docker and Node-RED, a low-code programming language for event-driven applications.
Ajeet Singh RainaandLakshanthaRead now
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Jun 16, 2022
How to Train and Deploy a Linear Regression Model Using PyTorch
Get an introduction to PyTorch, then learn how to use it for a simple problem like linear regression — and a simple way to containerize your application.
Shashank SharmaRead now
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Feb 16, 2021
How to Deploy GPU-Accelerated Applications on Amazon ECS with Docker Compose
Many applications can take advantage of GPU acceleration, in particular resource intensive Machine Learning (ML) applications. The development time of such applications may vary based on the hardware of the machine we use for development. Containerization will facilitate development due to reproducibility, and will make the setup easily transferable to other machines. Most importantly, a containerized application is easily deployable to platforms such as Amazon ECS, where it can take advantage of different hardware configurations.
Anca IordacheRead now