PySpark & Machine Learning Corporate Trainer Needed

We are seeking an experienced trainer to deliver a corporate training on PySpark and Machine Learning for a group of professional participants. This training provides a practical introduction to building scalable data processing and Machine Learning workflows using PySpark. Participants should come away understanding how Apache Spark operates within modern Big Data ecosystems, how to efficiently process large datasets using distributed computing, and how to build end-to-end ML pipelines using Spark MLlib. What We're Looking For: •Demonstrated experience delivering PySpark and/or Machine Learning training to corporate groups •Strong hands-on facilitation skills — comfortable running lab-based exercises •Ability to explain both Spark architecture fundamentals and applied ML workflows •Own training materials preferred, or willingness to adapt a provided outline into a full curriculum Topics to Cover: •Spark architecture: driver, executors, cluster manager, lazy evaluation, DAG and execution planning •RDD vs. DataFrame APIs — differences and when to use each •Creating and configuring SparkSession •PySpark DataFrames: transformations, actions, column expressions, filtering, joins, aggregations •Data quality checks and writing reusable, maintainable PySpark code •Performance fundamentals: partitioning, shuffle behavior, caching, persistence •Optimization techniques: broadcast joins, execution plan analysis •Feature engineering with Spark MLlib: handling missing values, encoding categorical variables, feature scaling •Preparing datasets and building reusable preprocessing pipelines for Machine Learning •Training regression and classification models at scale (Linear Regression, Logistic •Regression, Decision Trees, Random Forest) •Comparing models and interpreting results in distributed ML workflows •Deploying Machine Learning workflows in production-oriented environments Basic Python knowledge, fundamentals of data analysis, basic SQL, and an introductory understanding of Machine Learning concepts.