We’ve just added several new sessions to the ODSC East 2024 lineup! Today we are excited to bring you just a few of the machine learning sessions you’ll be able to participate in if you attend. Check them out below.

Who Wants to Live Forever? Reliability Engineering and Mortality

​​Allen Downey, PhD | Curriculum Designer at Brilliant.org | Professor Emeritus at Olin College

Reliability engineering is the study of survival and failure in engineered systems. It reveals surprising patterns in the world, including many examples where used is better than new — that is, we expect a used part to last longer than a new one.

This talk will take you through the tools of reliability engineering including survival curves, hazard functions, and expected remaining lifetimes. And we’ll consider examples from a variety of domains, including light bulbs, computer systems, and life expectancy for humans and institutions.

Causal AI: from Data to Action

Dr. Andre Franca | CTO | connectedFlow

Join this session to demystify the world of Causal AI, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. Key points of this talk are: In this talk, we will focus on:

  • The dangers of using post-hoc explainability methods as tools for decision making, and how traditional ML isn’t suited in situations where want to perform interventions on the system.
  • How to figure out what is causal and what isn’t, with a brief introduction to methods of structure learning and causal discovery
  • Accurately estimate the impact we can make on our system – how to use this knowledge to derive the best possible actions to make?

Feature Stores in Practice: Build and Deploy a Model with Featureform, Redis, Databricks, and Sagemaker

Simba Khadder | Founder & CEO | Featureform

What is a Feature Store really and what is the true scope of its benefits? In this session, you’ll take a deep dive into the three distinct types of Feature Stores and their uses in the machine learning ecosystem.

In a hands-on section, you’ll get practical experience training and deploying an end-to-end fraud detection model utilizing Featureform, Redis, Databricks, and Sagemaker. By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows.

Using Graphs for Large Feature Engineering Pipelines

Wes Madrigal | ML Engineer | Mad Consulting

Feature engineering from raw entity-level data is complex, but there are ways to reduce that complexity. In this session you’ll explore how composable compute graphs can help reduce that complexity and illustrate their efficacy through a case study from logistic and supply chain.

Better Features for Real-time Decisions Using Feature Engines

Mike Del Balso | Co-founder and CEO | Tecton

Nick Acosta | Developer Advocate | Tecton

In this session, Mick Del Balso and Nick Acosta will take on one of the biggest challenges in production Machine Learning. Utilizing a use case, you’ll explore how  feature engines can make it easy to build & productionize powerful feature pipelines. You’ll then get hands-on with the code that will enable you to create a modern technical architecture that simplifies the process of managing real-time ML models and features.

technical architecture that simplifies the process of managing real-time ML models and features.

Machine Learning with XGBoost

Matt Harrison | Python & Data Science Corporate Trainer | Consultant | MetaSnake

Join one of the leading experts in Python for this upcoming ODSC East session. Learn how to use XGBoost and see firsthand how to create, tune, evaluate, and interpret a model.

Idiomatic Pandas

Matt Harrison | Python & Data Science Corporate Trainer | Consultant | MetaSnake

Join leading Python expert and experienced instructor Matt Harrison for a tutorial that will cut to the biggest issues that he’s encountered in his extensive career. In particular you will cover proper types, chaining, aggregation, debugging.

Developing Credit Scoring Models for Banking and Beyond

Aric LaBarr, PhD | Associate Professor of Analytics | Institute for Advanced Analytics at NC State University

Join this session to learn how you can apply strategic binning of variables to create interpretable models for any industry. This strategic binning is an often-used method for displaying the patterns found in a machine learning classification model.

This training will help you work through how to build successful credit scoring models in both R and Python, as well as how to layer the interpretable scorecard on top of these models for ease of implementation, interpretation, and decision making.

Tutorial: Introduction to Apache Arrow and Apache Parquet, using Python and Pyarrow

Andrew Lamb | Chair of the Apache Arrow Program Management Committee | Staff Software Engineer | InfluxData

Build new skills in Apache Arrow and Apache Parquet in this upcoming ODSC East tutorial. If you’ve never used these tools before, this session will take you through the basics like how to load data to/from pyarrow arrays, csv and parquet files, and how to use pyarrow to quickly perform analytic operations such as filtering, aggregation, joining and sorting.

You’ll also explore the open Arrow ecosystem and how Arrow facilitates interoperability with pandas, pol.rs, DataFusion, DuckDB and other technologies.

No-Code and Low-Code AI: A Practical Project Driven Approach to ML

​​Gwendolyn D. Stripling, PhD | Lead AI & ML Content Developer | Google Cloud

In a no-code or low-code world you don’t have to have mastered coding to deploy machine learning models.

In this workshop, you’ll explore no-code and low-code frameworks, how they are used in the ML workflow, how they can be used for data ingestion and analysis, and how they can be used for building, training, and deploying ML models.

In particular, you’ll explore Google’s Vertex AI for both no-code and low-code ML model training, and Google’s Colab, a free Jupyter Notebook service. You will also become familiar with how to assess when to use low-code, no-code, and custom ML training frameworks.

Workflow-based GeoAI Analysis with No/Low-Code Visual Programming

Lingbo Liu, PhD | Postdoctoral Research Fellow | Harvard University

A data science novice, but ready to dive head first into GeoAI Analysis? Join this upcoming training session which will demonstrate how you can use low-code/no-code programming platforms to effectively integrate geospatial analysis with a variety of AI algorithms, including machine learning, deep learning, and Explainable AI. By the end, you will be ready to harness the platform for advanced spatial analysis and the development of sophisticated AI models.


Can’t wait to start learning from these incredible speakers and experts? Get your ODSC East Pass today to save 50%. But you’d better act fast. This limited-time offer ends soon!