Managing Real-Time Context Data Data Transformation and Persistence using Apache NIFI Setting up a Google Cloud EnvironmentCreating a DataProc Cluster and connecting it to Jupyter Notebook Using Google Cloud Storage service Submitting a PySpark Job on DataProc Modelling a Machine Learning Solution on PySpark for Multi-classification Data processing is key to ensure Machine Learning models' performance.
Data processing is key to ensure Machine Learning models' performance. But commonly, data is collected and stored in its raw format, and to get insights from it, post-processing is required. What if all of this could be automated and managed through pipelines?
This webinar not only demonstrates how to collect data in real-time, transform it, and persist it using Draco to be ready for further use, but it also shows how to build an end-to-end AI service with PySpark hosted in the cloud.
