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Overview of the event

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn from data, without being explicitly programmed.

The "deep" in "deep learning" refers to the number of layers through which the data is transformed. Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to its original goals. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation.



Facts & Figures About The Event

7 Speakers
7 Topics
50 Tickets

Conference Schedule

Machine Learning and Deep Learning Day (07th February, 2019)
X Topic Abstract

Operational data do not always reflect the source of truth, especially for non-obvious conclusions. This talk will demonstrate how Machine Learning was used on text data to derive the source of truth, which was subsequently leveraged to build prediction models that’s driving millions of dollars in cost savings for AT&T. Innovative approaches were used to crowd-source (text) labeling and building a reusable NLP framework. We will conclude with an overview of sample ML/AI projects going on at AT&T’s Chief Data Office exemplifying the unwavering focus towards driving real business impact

Speaker Profile

Prabir Nandi is currently an Associate Director with AT&T Chief Data Office leading a team of data scientists, data engineers and software engineers. He has extensive Technical Leadership, Business Development & Innovation experience gained at IBM Research and AT&T Chief Data Office that includes leadership in innovating, developing, managing, commercializing and scaling technologies, methodologies and solutions in the areas of Machine Learning, Artificial Intelligence, Big Data Analytics, Enterprise Architecture, Business Process, Business Intelligence & Analytics, SOA, Knowledge Management, Customer Experience Management and API-based Cloud Architectures.

X Topic Abstract

Organizations are transitioning to high performance computing (HPC) infrastructure for their data science practice. However, infrastructure transition is only half the puzzle. The other half of the puzzle is to transition their teams. The transition has be carefully managed by understanding why HPC is different and establish new hiring practices, ongoing training, and organization support so that the organization is productive on HPC environment.

X Topic Abstract

AI and Machine Learning are being the prime focus area for most of the organizations to improve their business outcome. Many Data Science programs has been initiated but failed during implementation - only 20% of the programs are being successful. As data science needs different business strategy and change in current IT culture, in this session we will be sharing the key strategies, approach and experience to enable successful implementation of d ata science programs with real industry case studies and examples.


X Topic Abstract

Data Driven Science is reshaping the world and is opening new opportunities across industries. In recent years, Life Science companies started embracing digital information and applying data science to optimize the time from “Lab to Life”. Big Data and Machine Learning (ML) technologies provide an opportunity for Life Science firms to unlock answers to a host of high-value questions such as the true effectiveness of treatments by integrated data driven analysis of clinical trial and omics Data. This talk introduces omics data, NoSQL database, concepts of machine learning and provides insight on ML algorithms classified as Supervised, Unsupervised, Artificial Neural Network and Deep learning. Further we explore some Machine Learning Implementations in Lifesciences Industry. Finally, will show how to apply machine learning on integrated data for identifying the hidden patterns and better treatment decisions using Python libraries.


X Topic Abstract

Broad scale use of Artificial Intelligence (AI) is in the early stages—few companies are ready to harness its power in a way that truly replicates human reasoning. As companies endeavor to use AI to deliver best-in-class services, it is important to consider the impact these technologies have on individuals and society. As we strive to deliver customer-centered experiences we need to understand how the data we collect can affect people and what unintended consequences stem from broad-scale AI adoption. How can companies deliver curated experiences and protect consumers against potential threats? (publication coming by end of 2019)

X Topic Abstract

Over the last two decades, marketing to the consumer has already been redefined.
Digital Marketing and avenues to know your consumer and develop the messaging that catches their attention is currently being redefined all over again... using Artificial Intelligence and Machine Learning.
Business-to-Consumer (B2C) businesses will soon learn that building a competitive advantage will start with Data... analyzing huge amounts of data of consumers searching, buying and using their products, but also about their daily habits, wants and preferences.
Many of these insights will help businesses not only rethink their marketing approaches and messages, but even redesign their products or services to create a stronger value proposition and a lasting influence on consumer choices.
However, as businesses implement AI/ ML, they will also need to deal proactively with the impact of their decisions on the society and their businesses, especially the people it affects.

X Topic Abstract

Approximately 70% of commercial freight is moved around the US in trucks - "If you bought it, a truck brought it". As a fundamental backbone of the American way of life, the trucking industry faces several challenges from safety issues to an acute shortage of skilled drivers. Learn how data mining and machine learning techniques are leveraged to provide several end-to-end solutions to mitigate risks and make better business decisions.

X Topic Abstract

With so many profound developments in technology, such as Cloud Computing, IoT …etc we are seeing a complete change in how we collect, process, and make decision based on data. We are not tied to transactional, or digital data any more. Voice, video, and images can be used to make business decisions, everyday physical things can be connected onto the network… With the growth of the technology comes an imperative, we need to embrace the complexity and define strategy around how to collect, process, analyze, and visualize data as well as how to use it to teach computers and machines make decision without human interruption. During my presentation I am going to talk about best practices to create a data driven culture. I will discuss how to move from Data to Analytics to Machine Learning to AI, and skill sets required for each.

Speaker Profile

Ebru Evliyaoglu Akyuz has 18+ years experience in Machine Learning and Data Science. She joined Microsoft in February 2018 as an Artificial Intelligence Solution Architect. She helps companies drive innovation and achieve more in the area of Artificial Intelligence and Machine Learning. She empowers and educates senior customer executives, developers, and architects on AI technologies and solutions. Her areas of expertise are Cognitive Services, Azure Machine Learning, Deep Learning. Previously, she was Director of Digital Analytics at CNN. She was responsible for establishing data strategy and enabling advanced analytics for all CNN Digital properties. She also held various positions at companies such as Cox , and UPS focusing on Digital Analytics and Data Science.

X Topic Abstract

Use of AI as a means to gain competitive advantage and performance improvement is a top of nind issue for senior leadership at all large companies. However unlike digital natives, most traditional companies have not be able to leverage artificial intelligence and intelligence automation technology in a significant way that has led meaningful shareholder value creation. The speaker will present his views on how large companies can speed up their adoption of AI and IA technologies to become AI powered enterprise.



X Topic Abstract

Most organizations are able to adopt RPA relatively easily. However, moving up the value chain to machine learning, cognitive automation, NLP and other advanced technologies will require a systemic strategy. My white paper reveals 7 ways to easily embrace Machine Learning after implementing RPA.



Our Sponsors

Use this opportunity to improve the visibility of your organization

Instant Sponsorship
Avail instant sponsorship at just USD 2000

Instant sponsorship includes
• Branding of your company as Bronze Sponsor – Company's Logo on the event page with cross link to your website.
• One Speaking Slot (45 min -non sales talk).
• 10% discount on registration fee for any delegate from your organization.
• Full day attendance at the event with lunch
• 1 x Roll up stand / Brochure distribution at the event
• Online Interview post of your company's senior executive at our media portal

For Silver, Gold Platinum & Titanium Sponsorship opportunites, please request for Sponsorship Brochure via email at contact@1point21gws.com, naveen@1point21gws.info

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Our Speakers


Babu Kariyaden Parambath

Head - Data Science & Analytics

Tata Consultancy Services


Prabir Nandi

Associate Director, Data Insights, Chief Data Office

AT&T


Ramprasad Renganathan

Principal Data Scientist

Omnitracs


Phani Shankar Srinivasan Ponnapalli

Principal Statistical Programmer

Syneos Health


Heather Smith

Head of Data Science

MHP Americas


Ranjit Gangadharan

Senior Director

Capgemini America Inc


Rajkumar Bondugula

Principal Data Scientist / Sr. Director

Equifax


Ebru Evliyaoglu Akyuz

Artificial Intelligence & Advanced Analytics - Cloud Solution Architect

Microsoft


Abhishek Breja

Global Technology Head for Intelligent Automation & Transformation

Fiserv


Prashanth Krishnamoorthy

Senior Director - Americas Geo Lead

UiPath

Want to become Speaker Please register here Register

Our Pricing

Group of three or more(Early Bird)

USD 300

Till November 30, 2019
Group of three or more(Standard)

USD 349

Till February 07, 2020
Individual(Early Bird)

USD 409

Till November 30, 2019
Individual (Standard)

USD 469

Till February 07, 2020


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Our Testimonial

FAQs

Who can attend Machine Learning and Deep Learning Day in Atlanta?

• Data Engineers/Developers / Scientists
• Analytics Professionals
• Startup Professionals
• Scientists/Researchers
• Professors
• President/Vice president
• Chairs/Directors

And last but not the least……….
Anyone interested in Machine Learning & thrives to make the future developed and better

Why to attend Machine Learning and Deep Learning Day in Atlanta?

Understand the state of development of Machine learning by exchanges, clearing houses, central counter parties and payment systems, and what it means for you.

What will you learn about?

Detecting where underlying problems and frictions exist in your organisation that will be alleviated by Machine learning technologies. Using Machine learning as a tool for innovation across your organisation

Are there any prerequisites to attend this program?

No

Do I need to register for the event?

Yes, all conference attendees must register in advance to attend the event.