Chicago, IL – October 18, 2017



6:00 – 6:40 pm – Network Reception – Open Bar & Hors d’oeuvres

6:40 – 6:45 pm – Welcoming Ceremony

6:45 – 7:25 pm – Big Data Architecture – The Nutanix Enterprise Cloud

7:30 – 8:10 pm – Machine Learning

8:15 – 8:55 pm – Deep Learning

8:55 – 9:00 pm – Closing Ceremony – Prizes & Giveaways

6:45 – 7:25 pm – The Nutanix Enterprise Cloud

One of the core concepts of Hadoop was always bringing the compute processes to the data.

Public cloud environments promise scale without hardware management but compromise this principle by separating compute from data as well as requiring almost constant data migration and re-architecture.

The rate of Big Data advancements in frameworks, software and toolsets, leveraging additional ways of accessing data and building data pipelines, can yield more innovation given the accessibility and paired scalability of compute and storage.

This presentation focuses on how Nutanix is an essential platform for distributed throughput and optimization of Hadoop workloads, bringing the core concepts of cloud to the data, instead of forcing companies to move all of their data to the cloud.


What is Hyperconverged Infrastructure?

Gartner Positions Nutanix in the Leaders Quadrant of the Magic Quadrant for Integrated Systems


Nutanix makes infrastructure invisible, elevating IT to focus on the applications and services that power their business.

The Nutanix enterprise cloud platform leverages web-scale engineering and consumer-grade design to natively converge compute, virtualization and storage into a resilient, software-defined solution with rich machine intelligence.


Predictable performance, cloud-like infrastructure consumption, robust security, and seamless application mobility for a broad range of enterprise applications.

7:30 – 8:10 pm – Machine Learning

Presentation Agenda

  • Machine Learning – Why so popular now?
  • 3 Important Trends – We can all be Data Companies
  • Use Cases – What’s driving Machine Learning
  • Machine Learning Technologies
  • Getting Started in Machine Learning

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed, a method of data analysis that automates analytical model building.

Machine learning focuses on the development of computer programs that can change when exposed to new data, using algorithms that iteratively learn from data and allowing computers to find hidden insights without being explicitly programmed w where to look.

An important breakthrough in Machine Learning was the realization, credited to Arthur Samuel in 1959, that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves.

Drawing a Distinction – Artificial Intelligence (AI) and Machine Learning

AI is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”.

Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.

What is the difference between Machine Learning and Artificial Intelligence? Bernard Marr, Forbes Magazine, December 6, 2016

8:15 – 8:55 pm – Deep Learning

Presentation Agenda

  • Defining Deep Learning
  • The Distinction between Deep Learning & Machine Learning
  • Use Cases – What’s driving Deep Learning
  • Deep Learning Technologies
  • Getting Started in Deep Learning

Deep learning is a branch of machine learning that tries to mimic the structure of the human brain. Layers that created within the algorithm pick specific parts of the subject to learn.

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics.

Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer.

Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

What is the difference between Deep Learning, Machine Learning and AI? Bernard Marr, Forbes Magazine, December 8, 2016


Help businesses, the community and the planet realize the benefits, value, promise and potential solutions that Big Data & Analytics provide through education and engagement.

We are Big Data & Analytics market leaders dedicated to educating, empowering and engaging communities by hosting events across the United States on all topics regarding Big Data & Analytics.


At our events, partners and other regional, national and international experts on Analytics, Artificial Intelligence and Big Data present the latest content on these exciting subject areas, including ‘How To’, best practices, real world use cases, trends and what the future may bring.