2-Day Training Courses

Amazon Web Services (AWS) offers a breadth and depth of services to easily build, train, and deploy TensorFlow models. Shashank Prasanna, Vikrant Kahlir, and Rama Thamman give you hands-on experience working with these services. Read more.

The TensorFlow library provides for the use of computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Robert Schroll introduces TensorFlow's capabilities in Python, moving from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications. Read more.

Maxim Lukiyanov, Vaidyaraman Sambasivam, Mehrnoosh Samekihow, and Santhosh Pillai demonstrate how AzureML helps data scientists be more productive when working through developing TensorFlow models for production. You'll see the whole model development lifecycle from training to deployment and ML ops to model interpretability. Read more.

Valliappa Lakshmanan shows you how to use Google Cloud Platform to design and build machine learning (ML) models and how to deploy them into production. You'll walk through the process of building a complete machine learning pipeline from ingest and exploration to training, evaluation, deployment, and prediction. Read more.

Aurélien Géron dives into creating production ML pipelines with TensorFlow Extended (TFX) and using TFX to move from ML coding to ML engineering. You'll walk through the basics and put your first pipeline together, then learn how to customize TFX components and perform deep analysis of model performance. Read more.