Building and Deploying ML Applications takes machine learning work from trained models to production-ready solutions in one intensive day. The course covers solution architectures, deploying models for performance, and the tools used to turn models into real apps and services. Attendees get hands-on practice building machine learning apps, including a web-based app built with Gradio. By the end, they can turn a model from a set of parameters into an app or process that delivers real business value.
The day opens with architectures for apps and services, comparing batch, real-time, and edge deployment patterns. Attendees then dig into backend inference, tuning request handling and hardware choices so predictions return quickly under real traffic. A Gradio-focused module has attendees build a working web interface around a trained model, wiring up inputs and outputs without writing custom frontend code. The day wraps up with ML service APIs, where attendees expose model predictions through endpoints other applications can call directly.
Who Should Attend
Developers, Data Scientists, Data Engineers, Machine Learning Engineers, MLOps professionals, DevOps professionals
What Attendees Will Learn
Upon completing Building and Deploying ML Applications, participants will be able to:
- Understand solution architectures and deployment patterns for machine learning apps and services
- Learn how to effectively perform machine learning model inference for a streamlined user experience
- Develop practical experience in developing web-based machine learning apps using Gradio
- Learn how to make seamless machine learning services
- Build the skills to turn machine learning models into apps and services that deliver business value
Prerequisites
Participants should have intermediate programming skills (preferably in Python), understanding of mathematics and statistics, basic experience with machine learning / neural networks, a grasp of computer science fundamentals, familiarity with common operating systems and basic command-line operations, and a willingness to review pre-course materials.
These prerequisites will help participants engage effectively with the course material and hands-on labs, making the learning experience more rewarding.