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Artificial Intelligence
Artificial Intelligence
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DeepLearning.AI

DSPy: Build and Optimize Agentic Apps

  • up to 1 hour
  • Intermediate

Join this course to learn how to build and optimize agentic applications using DSPy, a flexible open-source framework. Gain skills in creating modular, traceable, and debuggable GenAI applications, and optimize them with DSPy Optimizer for improved accuracy and consistency.

  • DSPy framework
  • MLflow tracing
  • Agentic application development
  • Prompt tuning
  • Few-shot learning

Overview

This course offers a comprehensive guide to using DSPy for building and optimizing LLM-powered applications. You'll learn to write programs using DSPy's signature-based programming model, debug them with MLflow tracing, and enhance their accuracy with DSPy Optimizer. The course covers chaining DSPy modules, visualizing programs with MLflow, and improving program quality through prompt tuning and few-shot examples.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

Software Engineers

Ideal for software engineers looking to build and debug multi-step agents with a reliable and maintainable approach.

AI Developers

Perfect for AI developers interested in creating GenAI applications using DSPy and optimizing them with DSPy Optimizer.

Data Scientists

Suitable for data scientists who want to enhance their skills in building agentic applications and debugging with MLflow.

Why should you take this course?

Artificial Intelligence

This course provides a reliable and maintainable way to build and debug multi-step agents using DSPy. It covers key topics like DSPy framework, MLflow tracing, and prompt tuning, making it ideal for software engineers, AI developers, and data scientists looking to advance their skills in GenAI applications.

Pre-Requisites

1 / 3

  • Basic understanding of programming concepts

  • Familiarity with machine learning frameworks

  • Interest in building GenAI applications

What will you learn?

Introduction
An overview of the course and its objectives.
Introduction to DSPy
Learn the fundamentals of DSPy and its signature-based programming model.
DSPy Programming - Signatures and Modules
Explore DSPy's programming model with code examples.
Debug Your DSPy Agent with MLflow Tracing
Learn to debug DSPy applications using MLflow tracing with code examples.
Optimizing Agents with DSPy Optimizer
Discover how to optimize GenAI applications using DSPy Optimizer with code examples.
Conclusion
Wrap up the course with key takeaways.
Quiz
Test your knowledge with a short quiz.
Appendix – Tips, Help, and Download
Additional resources and code examples for further learning.

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