Putting Together an Application
Master the end-to-end process of developing and deploying data science applications. This course guides you through prototyping, creating production-ready graphics, deploying models as APIs, and monitoring performance in real-time. Build robust, scalable applications with confidence.
What You Will Learn
This program is designed for data scientists and developers looking to bridge the gap between model development and practical application deployment. We move beyond theoretical concepts, focusing on the essential tools and techniques required to bring your machine learning models to life as functional, user-facing applications.
You will gain hands-on experience with modern frameworks for interactive prototyping, learn to generate professional-grade data visualizations, and understand the critical steps involved in packaging and serving models via robust APIs. The curriculum also covers crucial post-deployment considerations, including effective monitoring strategies for large language models to ensure sustained performance and reliability.
Course Structure
Prototyping with Gradio
Quickly build and share interactive UIs for your machine learning models using Gradio.
Creating Graphics with Plotnine
Generate high-quality, production-ready statistical graphics for data visualization.
Deploying Models as APIs
Learn to package and serve your machine learning models as robust, scalable APIs.
Monitoring an LLM
Implement effective strategies for monitoring and maintaining large language models in production.
Wrapping Things Up
Consolidate your knowledge and prepare for the next steps in application development.
Equip yourself with the practical skills to transform models into impactful, production-ready applications.
Curriculum
- 5 Sections
- 5 Lessons
- Lifetime
- Prototyping with Gradio1
- Creating Graphics with Plotnine1
- Deploying Models as APIs1
- Monitoring an LLM1
- Wrapping Things Up1
Instructor
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