Challenges of ML projects in 2023

Abstract

In 2023, machine learning (ML) projects will continue to present a host of challenges for organizations across industries. These challenges include issues related to data quality and quantity, the need for more sophisticated algorithms and models, concerns around fairness and bias, and the growing importance of model interpretability and explainability. In addition, organizations will need to grapple with the increasing complexity of ML projects, as well as the need to integrate ML workflows with existing business processes and systems. This talk will explore these challenges in detail, and offer practical strategies and solutions for overcoming them, based on real-world examples and best practices in the field of ML operations (MLOps).