Serverless Machine Learning with TensorFlow

Carl Osipov

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

Hands on Deep Learning with Keras, Tensorflow, and Apache Spark™ (Official Databricks Workshop)

Zoltan C. Toth


This course is aimed at the practitioner data scientist who is eager to get started with deep learning, as well as software engineers and technical managers interested in a thorough, hands-on overview of deep learning and its integration with Apache Spark.

The course covers the fundamentals of neural networks and how to build distributed Tensorflow models on top of Spark DataFrames. Throughout the class, you will use Keras, Tensorflow, Deep Learning Pipelines, and Horovod to build and tune models. This course is taught entirely in Python.

Each topic includes lecture content along with hands-on labs in the Databricks notebook environment.

Learning Objectives

After taking this class, students will be able to:
  • Build a neural network with Keras
  • Explain the difference between various activation functions and optimizers
  • Track experiments with MLflow
  • Apply models at scale with Deep Learning Pipelines
  • Perform transfer learning
  • Build distributed Tensorflow models with Horovod


  • Intro to Neural Networks with Keras
    • Neural network architectures
    • Activation functions
    • Evaluation metrics
    • Batch sizes, epochs, etc.
  • MLflow
    • Reproducible ML/DL
  • Convolutional Neural Networks
    • Convolutions
    • Batch Normalization
    • Max Pooling
    • ImageNet Architectures
  • Deep Learning Pipelines
    • Model inference at scale
  • Horovod
    • Distributed Tensorflow training
    • Ring-All Reduce

What is your ideal business case for AI? Non-technical workshop

Levente Szabados

The revolution is in full swing. We read about new and new achievements from research labs around the globe - mostly from the “big players”. But with all this hype, what is the ideal use case for you and your organization?How can you know, what is just up and coming, and what is already practical reality? Leaning on the expertise on our consulting partner AI Partners, we would like to help you to separate fact from fiction and find your own potential use-cases. Based on the analysis of 3500+ AI startups and 500 specific use cases with KPIs, we are ready to give you a picture of how the market evolves, what benefits you can expect from an AI project implemented in your business, and what pitfalls should you avoid.

(The workshop is targeted to business audience, so no technological expertise is required.)


  1. “Why now?” - Historical context, is there really a revolution going on?

  2. “What do we mean by AI anyhow”

  3. How do companies adapt AI? - Learnings from 500 use cases

  4. From build to buy - What is the appropriate way for my problems?

  5. Workshop: elaborating use-cases for your organisation under expert supervision (Interactive modul to identify specific use-cases to understand the practical difficulties and opportunities in implementing AI based technologies)

  6. How will my organization change?

  7. Outlook - How will my world change?

Practical Artificial Intelligence with Swift

Mars Geldard
Paris Buttfield-Addison

This tutorial explores the latest in machine learning using TensorFlow, and on-device, local AI with Swift and Apple platforms.

Learn how to apply the Vision, Core ML, and CreateML frameworks to solve practical problems in object detection, face recognition, and more. These frameworks run on-device, so they work quickly with no network access, making them cost effective and user-privacy conscious.

You’ll combine Apple’s frameworks with open source libraries such as TensorFlow, to create an iOS app that makes it look easy to detect faces and facial features, detect and classify objects in photos, and expose these features to the user.

Topics include:

  • The basics of machine learning: The differences between, and reasons to be interested in, supervised learning, unsupervised learning, and reinforcement learning and the different types of problems each can address
  • What Apple’s CoreML, CreateML, and Vision frameworks do
  • How to set up your Swift-based iOS development environment for machine learning
  • How to work with TensorFlow, the popular open source Python neural network and machine learning library, to create, manipulate, and bring models into CoreML
  • How to implement machine learning-based features in your iOS apps and load trained models for use in machine learning

In a privacy-conscious world, practical on-device machine learning is the future. Learn it here.

Attendees will need to bring a Mac laptop capable of running the latest public version of Xcode (free). Attendee should have programming experience using any modern language. Swift experience is not necessarily required.