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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 (06th February, 2020)
X Topic Abstract

Learn how to get started with AI/ML in your business. Before jumping into a project, know what are the key questions to ask about use cases, data, talent, and more. We will discuss best practices and common pitfalls when adopting AI in your company for the first time. We will also discuss how to lead AI/ML teams to implement use cases successfully.

X Topic Abstract

In an effort to make sure you have the right product at the right time in order to maximize profitability and to fully capitalize on demand, organizations are exploring how Machine Learning (ML) models can help them to better optimize inventory management. UiPath is unique in that it allows organizations to drag/drop those ML models directly into process automations for an end-to-end inventory management solution. During this talk, we'll describe the challenge and demo how UiPath is helping organizations deliver positive business outcomes by combining the power of ML with the power of RPA.

Speaker Profile

Dan Bannoura is a seasoned leader within the Customer Success team at UiPath. UiPath is unique in that it has a team of Customer Success Engineers who work alongside its customers throughout their automation journey and guides them to recognize positive business outcomes in the shortest amount of time. Dan and his team have helped the largest companies in the world start their journey and ultimately scale to where they have fully functioning Global Robotic Process Automation Operation Center.

X Topic Abstract

Team conversations in a modern enterprise are either persistent or ephemeral. Persistent conversation may happen via emails, chats (Slack/Teams), or Documents. Ephemeral conversations may happen through voice/video conversations, and are often lost. These conversations are a source of rich, contextual information that can greatly improve productivity if captured and made available through intelligent uses cases like search and alerts. They also greatly help alignment in teams that are distributed not only across distance but also time zones.

We built a conversational cloud that captures all interactions between teams in an enterprise and persist, index, analyze and make them consumable in meaningful ways. This is powered by an enterprise conversational intelligence graph that leverages advance in Machine Learning for Speech-to-Text and Natural Language Processing and Knowledge Graphs to transform(speech-to-text), features (deep Language models) and organize the insights/information (Knowledge Graphs) and enable enterprises to leverage the insights for various downstream tasks:

● Contextual Meeting summarization
● Topic extraction and scoring
● Recommend watchers
● Related meetings/docs
● Smart Search
● Smart Alerts
● SME identification

Transformer based Language models are used for featurizing the text for various downstream computation tasks. The language models are frequently enriched using the accumulated information since the previous snapshot. Language models are fine-tuned for various auxiliary tasks to featurize diverse linguistic properties of the text. Variations of BERT and GPT trained on different auxiliary tasks are used for different downstream tasks. The choice of architecture depends on the use case - for example, for generalized, task-independent featurization of sentence/paragraph, GPT based architectures are efficient and for labelled classifiers, BERT based architectures are preferred. Conventional NLP algorithms/patterns are used for tasks where Machine Learning algorithms are not feasible - Key-phrase extraction, grammar rules for subject identification and other grammar patterns for various tasks. Textual information is featurized and organized as a graph for efficiency. Graphs give the ability to capture non-trivial relationships between various entities - people, teams, meetings, documents, topics etc. Graph-based algorithms like PageRank, Louvain communities and graph clustering enables the capture of various entity properties for efficient information retrieval. Neural Network-based Graph Embeddings and sub-graph embeddings enable us to capture deeper contextual node/subgraph properties and relationships. Node2Vec, Graph2Vec gives the latent representations of nodes/graphs capturing various structural and proximity features of graph entities that are learned by performing the graph traversal and capturing the structural behaviour and/or by optimizing for proximity loss function such that the closest nodes will have similar representations in latent space. The weighted aggregation of these two approaches captures the representations that can be fed into Machine Learning algorithms for supervised/semi-supervised tasks.

Speaker Profile

Krishna Sai is a seasoned technology and engineering leader and entrepreneur with 2 decades of experience in scaling and leading global teams, innovating and building winning products in a variety of technologies. Passionate about meaningful application of technologies that deliver value and better our lives.

● Currently Co-Founder/CTO of Ether Labs, an AI-First Enterprise Collaboration Company based in Austin/Bangalore.

● VP/CTO, APAC & LATAM at Groupon: Responsible for product & engineering for Groupon’s APAC and LATAM business ($750M) in 13 countries, and developing several supply and consumer services and applications for Groupon’s global business ($7B).

● Co-Founder and CTO at Living Tree (Acquired by Groupon): Built a technology startup based on a contextual social network involved in connecting communities across verticals, in particular education. Successful exit through acquisition by Groupon in Dec 2014.

● Vice President (Technology) and Chief Architect at Polycom: $1.5B Unified Communications Company, responsible for the technology strategy, IP, solutions architecture, innovation and key M&A activities.

● Vice President (Engineering), Polycom’s Video Solutions and Telepresence portfolio.

● Two startups in the areas of computer graphics / image processing, and VOIP.

● MS (Computer Engineering, Louisiana State University), BE (Bangalore University).

X Topic Abstract

A real-business application of analytics in “Improving Customer Experiences with Real-Time Insights”. This session will provide a step by step experiential journey on “How data science is helping IBM to predict the customer experience journey and proactively address the issues, leading to the improvement of Net Promoter Score”. The session would also highlight the importance of using CRISP-DM (Cross Industry Standard Process for Data Mining) and Agile in Data Science projects. The methodology involved consuming historical NPS data; using machine learning and artificial intelligence to identify the most important features and created an algorithm to predict the customer experience.

X Topic Abstract

Marketing data has exploded in recent years. The among of data that can be captured either from POS transactions to operations to website activities to conversions has provided a unique view into a customer's online and offline worlds. The richness of this data, coupled with the power of predictive modeling can provide insights that can be acted upon with the ultimate goal of enhancing customer experience and increasing LTV to the company.

The objective of this talk to touch upon some of the ways in which predictive modeling can be used to either predict several aspects of a customer's life cycle such as acquisition, cross-sell / up-sell and retention.

X Topic Abstract

a) Business Problem Statement - Value Stream View
b) Deploying and using AI-ML for analytics trend and trace-ability
c) Breaking patterns using AI and Graph database
d) Improving end-user experience with AI

X Topic Abstract

ML and RPA are powerful capabilities by themselves, but the business value recognized is exponentially greater when they work in concert with one another. This talk will cover how you can drag your ML models directly into your RPA workflows and how you can deploy/manage those models within the UiPath Platform for an end-to-end experience. Not only will this talk discuss how this is done, but we’ll also discuss two real world use cases along with a demonstration where you’ll see this integration in action.

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

Our Sponsors


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Our Speakers


Neeraj Madan

Data Scientist

IBM


Savitha Namuduri

Sr. Director

Hearts & Science


Carlos Lara

Founder and Ceo

Carlos Lara AI


Rama Venky Susarla

Strategic Product Owner/Strategic Program Manager

Micron Technology


Rob Freedman

Senior account executive

UiPath


Daniel Bannoura

Director

UiPath


Krishna Sai

Co-founder/CTO

Ether Labs

Want to become Speaker Please register here Register

Our Pricing

Group of three or more(Early Bird)

USD 300

Till December 31, 2019
Group of three or more(Standard)

USD 349

Till February 06, 2020
Individual(Early Bird)

USD 409

Till December 31, 2019
Individual (Standard)

USD 469

Till February 06, 2020


Our Testimonial

FAQs

Who can attend Machine learning and Deep Learning Day in Dallas?

• 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 Dallas?

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.