General Training Session: Serverless Machine Learning with Tensorflow Part II
General Training Session: Serverless Machine Learning with Tensorflow Part II


This workshop will be conducted on Google Cloud Platform (GCP) and will use GCP's infrastructure to run TensorFlow. All you need is a laptop with a modern browser.]
In this workshop, we walk through the process of building a complete machine learning pipeline covering ingest, exploration, training, evaluation, deployment, and prediction:
Data pipelines and data processing: We will discuss how to explore and split large data sets correctly - for this tutorial we will be using SQL and Pandas on BigQuery and Cloud Datalab.
Model building: The wide-and-deep machine learning model in TensorFlow will be developed on a small sample locally. The preprocessing operations will be implemented using Apache Beam, so that the same preprocessing can be applied in streaming mode as well. The preprocessing and training of the model will be carried out Cloud Dataflow and Cloud ML Engine.
Model Inference and Deployment: The trained model will be deployed as a REST microservice and predictions invoked from a web application.


Carl is a 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. With over 16 years of experience in the IT industry, Carl worked with the world's leading technology companies across United States and Europe, including in leadership roles on programs and projects in the areas of big data, cloud computing, service-oriented architecture, machine learning, and computational natural language processing. Carl is an author of over 20 articles in professional, trade, and academic journals, an inventor with 6 patents at USPTO, and holds 3 corporate awards from IBM for his innovative work. You can find out more about Carl on his blog

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