Algorithm selection and hyperparameter tuning can take a lot of time. There are many different options and testing all of them consumes a lot of resources. More and more people are looking to machine learning to help improve machine learning development. In this session, we will examine how to use machine learning to pick the best algorithm with Azure Synapse Analytics and Azure ML. The demonstrations will show how to use Apache Spark and Python in Azure Synapse together with Azure ML to create a machine learning solution. The solution will pick the best algorithm based on your criteria to determine what the important elements are. For example, you may be looking to create a regression experiment which has the best RMSE value and know there are some algorithms which you do not want to evaluate. You will see how to incorporate your criteria, tune your hyper parameters to select the best solution, and select the iterations and length of time you want the analysis to take. Once the algorithm is selected, the demonstrations will show how to incorporate the appropriate algorithm and deploy a solution which can be called in a development pipeline.