Apache Spark™ has introduced Arrow-optimized APIs such as Pandas UDFs and the Pandas Functions API, providing high performance for Python workloads. Yet, many users continue to rely on regular Python UDFs due to their simple interface, especially when advanced Python expertise is not readily available. This talk introduces a powerful new feature in Apache Spark that brings Arrow optimization to regular Python UDFs. With this enhancement, users can leverage performance gains without modifying their existing UDFs — simply by enabling a configuration setting or toggling a UDF-level parameter. Additionally, we will dive into practical tips and features for using Arrow-optimized Python UDFs effectively, exploring their strengths and limitations. Whether you’re a Spark beginner or an experienced user, this session will allow you to achieve the best of both simplicity and performance in your workflows with regular Python UDFs.

In this course, you’ll learn the fundamentals of preparing data for machine learning using Databricks. We’ll cover topics like exploring, cleaning, and organizing data tailored for traditional machine learning applications. We’ll also cover data visualization, feature engineering, and optimal feature storage strategies. By building a strong foundation in data preparation, this course equips you with the essential skills to create high-quality datasets that can power accurate and reliable machine learning and AI models. Whether you're developing predictive models or enabling downstream AI applications, these capabilities are critical for delivering impactful, data-driven solutions. Pre-requisites: Familiarity with Databricks workspace, notebooks, as well as Unity Catalog. An intermediate level knowledge of Python (scikit-learn, Matplotlib), Pandas, and PySpark. As well as with concepts of exploratory data analysis, feature engineering, standardization, and imputation methods). Labs: Yes Certification Path: Databricks Certified Machine Learning Associate

The course intends to equip professional-level machine learning practitioners with knowledge and hands-on experience in utilizing Apache Spark™ for machine learning purposes, including model fine-tuning. Additionally, the course covers using the Pandas library for scalable machine learning tasks. The initial section of the course focuses on comprehending the fundamentals of Apache Spark™ along with its machine learning capabilities. Subsequently, the second section delves into fine-tuning models using the hyperopt library. The final segment involves learning the implementation of the Pandas API within Apache Spark™, encompassing guidance on Pandas UDFs (User-Defined Functions) and the Functions API for model inference. Pre-requisites: Familiarity with Databricks workspace and notebooks; knowledge of machine learning model development and deployment with MLflow (e.g. basic understanding of DS/ML concepts, common model metrics and python libraries as well as a basic understanding of scaling workloads with Spark) Labs: Yes Certification Path: Databricks Certified Machine Learning Professional