Join us at MAPD April 13th and Analytics April 14-16, 2024 in Orlando, Florida
AMPL will be a Silver Sponsor and will be at booth #201 in the exhibit area.
Our conference presentations will feature an introduction to Python and AMPL for Prescriptive Analytics. The Technology Workshop and Tutorial will show you how to build prescriptive analytics applications quickly, from prototyping to deployment, with Pandas, Colab, Streamlit, and amplpy – and feature tips on leveraging AI for rapid model development.
Location: Windsong 2
Prescriptive Analytics from Model to App: Learn How You Can Build Optimization Applications Quickly and Reliably, with AMPL, Python, Streamlit – and AI
Presented by: Bob Fourer, Filipe Brandão, and Gyorgy Matyasfalvi
Optimization is the most widely adopted technology of Prescriptive Analytics, but also the most challenging to implement. This presentation takes you through the steps as a proven approach that combines the best features of two implementation environments:
You’ll see how new AI technology is enabling a rapid development process for both AMPL and Python, reducing the time and effort to produce a working application that’s ready for end-users.
We begin by introducing model-based optimization, the key approach to streamlining the optimization modeling cycle and building successful applications today. Using AMPL’s natural modeling language, you formulate optimization problems more like you think about them, while AMPL’s customized solver interfaces automate the often-complicated reformulations required by advanced solver algorithms.
Our presentation next shows how AMPL and Python work together for building optimization into enterprise systems. AMPL integrates with Python through the “amplpy” package, allowing for smooth data interchange between Python data structures, Pandas dataframes, and AMPL models. In contrast to Python-only modeling solutions, amplpy leverages AMPL’s straightforward model formulation and efficient model processing, while maintaining access to Python’s vast ecosystem for data preparation, solution analysis, and visualization.
The workshop concludes with a rapid deployment demonstration, bringing together AMPL, Python, and AI. Our example features generative AI’s ability to produce both AMPL models and Python programs, and Streamlit’s features for turing Python scripts into shareable web apps.
Location: Windsong 2
Python and AMPL: Build Prescriptive Analytics applications quickly with amplpy, Pandas, Streamlit — and AI
Presented by: Filipe Brandão and Robert Fourer
Python and its vast ecosystem are great for data pre-processing, solution analysis, and visualization, but Python’s design as a general-purpose programming language makes it less than ideal for expressing the complex optimization problems typical of prescriptive analytics. AMPL is a declarative language that is designed for describing optimization problems and that integrates naturally with Python.
In this presentation, you’ll learn how the combination of AMPL modeling with Python environments and tools has made optimization software more natural to use, faster to run, and easier to integrate with enterprise systems. Following a quick introduction to model-based optimization, we will show how AMPL and Python work together in a range of contexts:
You’ll also see how generative AI technology is enabling a rapid development process for both AMPL and Python, reducing the time and effort to produce a working application that’s ready for end-users.
See us in person at an upcoming conference, in-person meeting or webinar.
Explore all our past talks, slides and videos from past conferences and online lectures.