Seminar: Deep Learning in the Enterprise

Seminar: Machine Learning & Deep Learning in the Enterprise

Monday, December 11, 2017 - 1:00-4:45pm




Robert Bogucki
Chief Science Officer,

Jan Lasek
Data Scientist,

Vishwa Kolla
Head of Advanced Analytics (AA), John Hancock Insurance

Anand Oka
Principal Program Manager,
Anti-Fraud, Microsoft

Seminar Introduction

This half day seminar provides a comprehensive introduction to attendees on the entire machine learning (ML) and deep learning (DL) industry, including the different players, options, and technologies. A particular focus will be on DL to enable attendees to gain the basic concept of deep learning, which revolutionizes data science tasks such as image recognition, speech analysis or time series prediction.

Participants will:

  • gain a thorough overview of today’s machine learning and deep learning market,
  • learn when to use machine learning and when to use deep learning,
  • gain knowledge about how and where to get and use the right type of data for different applications,
  • learn where in your organization you can find the best type of applications,
  • learn what type of approach your company should use in terms of building your own team and/or working with third party service and software providers, and
  • what options are now available in the cloud.

Attendees will also gain a deep sense of understanding of what steps to take next and will be well equipped to begin individual exploration.

1:00-1:45PM State-of-the-Practice of Data Science in the Enterprise

Speaker: Jan Lasek, Data Scientist,

The first part of this seminar will focus on providing a comprehensive introduction to attendees on the entire machine learning (ML) and deep learning (DL) industry, including different options, players, and technologies.

This session will present an overview of the data science solutions and ecosystem, with an emphasis on actual examples of large corporate machine/deep learning deployments. Attendees will learn about the current ML and DL market and will hear about innovative uses of data science in different industries.

Attendee benefits include:

  • Learn about landscape and ecosystem of today’s data science market – the most popular and effective trends
  • Get an overview of the options that enterprises have in developing a business and technology strategy to help them determine the best way to take advantage of data science today
  • Numerous examples of data science solutions in different industries including FinTech, Healthcare, Consumer Service and others
  • This seminar is geared to technical business management level professionals, however deep understanding of computer science, programming or data science is not required

This portion of the seminar will answer:

  • How to unlock the power of data science?
  • What kind of data is used for data science purposes and what type of data should not be used?
  • What is the timeframe for data science projects to pay off?
  • How to do a proof of concept project?
  • How long will your initial deployment be?
  • What is the best software to use?
  • What are the maintenance requirements?
  • Q&A session
1:45-2:45pm The What, Why and How of Deep Learning

Speaker: Jan Lasek, Data Scientist,

This session focuses on how to determine which industry’s and types of applications are best suitable to use deep learning.  The presentation will be geared to business and technical managers and will describe use cases and statistics from different industries. Topics covered include:

  • what deep learning is (popular models and techniques) and how it is different than machine learning
  • what specific benefits can a business derive from using deep learning
  • what are the current automation solutions, new market and data driven solutions,
  • deep learning enterprise case studies and statistics will be discussed across multiple industries
  • how businesses can begin implementing deep learning today to develop competitive advantage, save money and drive better customer experience.
  • deployment timeframes
  • what is the best software/platform to use based on different scenarios and applications
  • what are the maintenance requirements,
  • Q&A session
2:45-3:15 Networking break on exhibit floor
3:15-3:55 Panel: Do You Really Need to Build Your Own Data Science Team?

Presenter: Robert Bogucki, Chief Science Officer,

This session will address how to most effectively plan for your DL project deployment and will cover:

  • When building a data science team is a good idea for my business?
  • When an inside data science team is crucial for your business
  • What kind of resources do you need to build a data science project on site?
  • What kind of skills your data science team should have?
  • How to build a data science team internally?
  • Best practices, in gaining ROI, including proof of concept analysis
  • What are the typical mistakes?
  • What are the costs
  • What are your other options than to build an internal data science team?
4:00-4:45pm Panel: Deep Learning for the Enterprise - Opportunities and Challenges

Moderator: Robert Bogucki, Chief Science Officer,


  • Vishwa Kolla, AVP and Head of Advanced Analytics, John Hancock Insurance
  • Anand Oka, Principal Program Manager, Anti-Fraud, Microsoft

This panel session will take attendees to the next step from the earlier strategic overview of the DL market.  Panelists will include experienced project managers who have run ML and DL engagements and professionals from large enterprises who have deployed ML and DL applications at their company.   During this panel, you will hear from world class heads of data science and innovation departments for major U.S. companies. Learn what they are doing at their company, how DL is disrupting their industries, and critical lessons learned in deploying enterprise class machine learning applications.

Attendees will learn about:

  • The current state of the market in terms of need for ML and DL solutions
  • Vision, manufacturing, bots – what seems to be most promising use of ML, now?
  • ML, DL and cybersecurity
  • How to choose the right partners
  • What are the limitations of DL
  • What costs and ROI should be expected for various types of ML deployments
  • Case studies describing what the costs, ROI and rewards have been from early adopters of DL
  • The enterprise and organizational challenges in bringing DL in-house
  • How to construct an effective plan for a successful DL deployment project(s)
  • How to deal with upper management expectations
  • In-house vs. Outsource considerations: When do you want to build your internal machine learning team and when is more beneficial to work with external experts?
  • What are new business sectors where DL solutions are not widely adopted yet, but which have potential?
  • What would be the sign that your business needs machine learning?
  • What would you recognize as the most promising trends and technologies for a future?
  • Q&A session