Carl Osipov


Carl Osipov is a staff program manager focused on helping Google’s customers and business partners get trained and certified to run machine learning and data analytics workloads on Google Cloud. Carl has more than 17 years of experience in the IT industry and has held leadership roles for programs and projects in the areas of big data, cloud computing, service-oriented architecture, machine learning, and computational natural language processing at some of the world’s leading technology companies across the United States and Europe. Carl has written over 20 articles for professional, trade, and academic journals and holds six patents from the USPTO. He has received three corporate awards from IBM for his innovative work. You can find out more about Carl on his blog.

Data Science without a Data Scientist: Machine Learning Model Discovery with Cloud AutoML

Level: Beginner+

You have already done the hard work: you cleaned up your dataset and prepared it for analysis. So what should you do next? 

This session kicks off by demonstrating why you should have an easy-to-build benchmark model for your dataset before you start experimenting with a highly customized model. You will also learn about the tools that are available from Google Cloud to automate the process of creating benchmark models for most datasets with a minimal effort.

During live demonstrations, you will learn how to train Google BigQuery ML and AutoML services to answer questions about both structured and unstructured data while ensuring security and maintaining control over who should have the permissions to access both the data and the insights. 

Join us at the session and be ready to take away pragmatic and actionable ideas on how to get more from your data with less effort!

Serverless Machine Learning with TensorFlow

Level: Intermediate

Carl Osipov walks you through building a complete machine learning pipeline from ingest, exploration, training, and evaluation to deployment and prediction. This workshop will be conducted on the Google Cloud Platform (GCP) and will use GCP’s infrastructure to run TensorFlow.The topics include: 1) Data pipelines and data processing: How to explore and split large datasets correctly using SQL and Python Pandas on BigQuery 2) Model building: How to develop a wide-and-deep machine learning model in TensorFlow on a small sample locally (using Apache Beam for preprocessing operations so that the same preprocessing can be applied in streaming mode as well and Cloud Dataflow and Cloud ML Engine for preprocessing and training of the model 3) Model inference and deployment: How to deploy the trained model as a REST microservice with predictions invoked from a web application