Automated AI/ML makes it easier for data scientists to develop pipelines by searching over hyperparameters, algorithms, data preparation steps, and even pipeline topologies. 1) Lale: Type-Driven Auto-ML with Scikit-Learn (includes a 10 minute demo) Lale (https://github.com/ibm/lale) is an open-source library of high-level Python interfaces that simplifies and unifies the syntax of automated ML to be consistent with manual ML, with other automated tools, and with error checks. It also supports advanced features such as topology search and higher-order operators. 2) AutoMLPipeline: Symbolic ML Pipeline Composition and Parallel Evaluation (~15 minute demo) AutoMLPipeline (https://github.com/IBM/AutoMLPipeline.jl) is a Julia toolkit that makes it trivial to create complex ML pipeline structures using simple expressions and evaluate them in parallel. It leverages the built-in macro programming features of Julia to symbolically process and manipulate pipeline expressions. 3) AutoAI with Stakeholder Constraints (10 minute demo) Common applications of AI involve multiple stakeholders with requirements beyond a single objective of predictive performance. This toolkit automatically generates pipelines with favorable predictive performance while satisfying stakeholder constraints related to deployment (inference time and pipeline size) and fairness. It also provides an API to specify custom constraints.