Machine Learning for the Enterprise
International Conference 2019

Brochure Image

by Gianmario Spacagna, Danilo Poccia, Frank Greco, Chris Swan, Laurent Picard, Laurent Bugnion , Francesca Lazzeri, Zoran Sevarac , Eric Bruno download a PDF brochure Download Event Brochure

From Monday October 28 2019 to Tuesday October 29 2019

Price: 1,500.00 Euro

Register now

Residenza di Ripetta
Via di Ripetta 231
00186 Roma (RM)

more information about our venue...


Machine Learning (ML) represents a massive change in the computing industry. It is a long-term trend that offers the potential for significant advantages for many enterprises.

Accurate prediction is critical for practically all enterprises. Without a degree of confidence in business forecasting, organizations would have a difficult time delivering successful products and services in a cost-effective manner.

Machine Learning provides the capability to offer deep predictive and prescriptive decision-making intelligence. This type of enterprise data analysis is vital to all businesses. Based on a subset of Artificial Intelligence (AI), the core of ML is all about recognizing patterns in your data and making predictions against that data.

Based on powerful statistical pattern recognition algorithms and large datasets, ML has many very useful applications.

The “Machine Learning for the Enterprise Conference” is focused on learning the techniques of AI/ML, realizing the promises and understanding the issues and challenges of applying AI/ML in all companies for business benefit.

This Conference will explain the details of ML, describe the statistical foundations used in ML, suggested tools and techniques, and discuss some of the major issues in deploying powerful ML systems.

Main Topics

  • The Foundations of AI/ML and Deep Learning
  • What a Neural Network is
  • The Different Types of Learning Models: Structured, Unstructured, Semi-Structured and Reinforcement
  • The Data Preparation Phase of an ML Project
  • Classification and Regression
  • About Detailed Use Cases
  • Privacy Issues with ML Applications
  • AI/ML and Ethics
  • Using ML with Cloud Computing
  • What Software Tooling is Required to Develop ML Features
  • Transitioning your Development Team to use Machine Learning
  • The Impact of AI/ML on Next Generation Hardware and Software