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
Machine Learning and Deep Learning Day (24th October, 2019)
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
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.
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.
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.
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.
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)
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.
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.
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.
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.
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.
Use this opportunity to improve the visibility of your organization
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
"1.21gws have created nice platform and connected with various top industry experts under one roof.
Thanks for organizing this kind of event Diksha and Nitin. Really looking forward to more of such kind of conferences in future."
"Plenty of informative and sharing of experience was very good"
Finastra software solutions
"Great presentation, None of them was boring". All Equal informative.
Director of Engineering - Machine Learning
Oracle India Pvt Ltd
"Good format! Good focus and quality of delegates"
It was a very worthwhile conference
Round Table was very helpful
Enjoyed the Interaction
All Presentations were Great. Very Informative
Presentations had good content
Who can attend Machine Learning and Deep Learning Day in Atlanta?
• Data Engineers/Developers / Scientists
• Analytics Professionals
• Startup Professionals
• President/Vice president
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?
Do I need to register for the event?
Yes, all conference attendees must register in advance to attend the event.