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20 - 21 May 2020
Global Online Live Conference (Time Zone - SAST, South Africa)

World Machine Learning Summit

Theme : Data Science, Deep Learning, Machine Learning and NLP
20 - 21 May 2020
Global Online Live Conference (Time Zone - SAST, South Africa)

World Machine Learning Summit

Theme : Data Science, Deep Learning, Machine Learning and NLP
20 - 21 May 2020
Global Online Live Conference (Time Zone - SAST, South Africa)

World Machine Learning Summit

Theme : Data Science, Deep Learning, Machine Learning and NLP
Ways to convince Your Boss Ways to Save

Briefly Know About This Event

World Machine Learning Summit-2020, Online organized by 1point21GWs will focus on technical and practical verticals including use cases around Real-time analytics, AI regulations, NLP, Machine Learning and Deep Learning .

The conference is gathering young and innovative minds, practitioners, experts to motivate and give the delegates new ways to work and achieve through data. Machine learning 2020 flourishes to see a developed and better future.

It is 2 days 2 tracks conference in Online on May 20 -21, 2020.

Day 1: May 20, 2020
Theme : Tools, APIs, Frameworks & Applications

Day 2: May 21, 2020
Theme : Trending & Deep Learning





  • 20+

    Speakers

  • 20+

    Topics

  • 500

    Tickets

  • 2

    Days

Day 1: 20th May, 2020

Theme : Tools, APIs, Frameworks & Applications

X Topic Abstract

We will discuss through the examples from the healthcare space while bringing different perspective on how people are taking artificial intelligence in the healthcare space
what are the challenges, and what are the complexity in this area.

X Topic Abstract

One of the earliest and most successful use cases of machine learning has been helping users to discover new content and discover related or new products and services. Machine learning algorithms can help customers discover new content based on previous viewing behaviour (the Netflix model) and new / related products based on previous purchases (The Amazon model). However, algorithms that target the challenge of discoverability have typically only worked well where customer behaviour is static or only changes slowly, such as movie and book recommendations. More recently, advances in machine learning algorithms have enabled recommending items that do not meet these criteria. In this talk, I will first discuss the conceptual problems of applying machine learning to the discovery of time sensitive material and products. I will then discuss how recommendation algorithms can be adapted to solve these problems. Finally, I will share how we applied machine learning to the challenge of discoverability in OVOPlay - a sports streaming service that has had to overcome the challenge of limited metadata, a broad ranges of customers and a range of products from telco to entertainment.

Speaker Profile

X Topic Abstract

Build Kafka Kstream application for real time analytics and Machine learning. Focus on building ML infrastructure including the deployment of an analytic model in a Kafka application for real-time analytics and predictions.

Points covered in the session:
1. Intro about Machine Learning and Real time application.
2. Current trend and the hidden cost in developing a Machine learning framework.
3. Intro to Apache Kafka and Machine Learning,
4. How to leverage Apache Kafka to build and deply machine learning model in scalable way.
5. Ideal architecture for a scalable and flexible machine learning framework.

Today, more than 30% of the Fortune 500 companies use Apache Kafka including Uber, Airbnb, LinkedIn, Spotify, PayPal, etc.


Speaker Profile:

More than a decade of experience in building data driven software products. Have previously worked for SAP, Dell, Cisco and EY. Currently working for Deltatre as Data Architect.

X Topic Abstract

Assisted or Supervised models of machine learning are a great boon to automate many mundane tasks in ERP world. Supervised model combined with hypothesis models helps in creating “situations” and in predicting possible outcomes to a given situation. Simple cases of Critical Value Methods and p-value methods can be used to provide insights before user takes an action in ERP. In this presentation we will use the above methods to solve a problem of how invoices are matched with vendor payments.


Use Case:

Account Receivable accountants have to match incoming payments with open customer invoices that the payments are intended to clear. This is a laborious task riddled with many incorrect matches and subsequent reversals and painstakingly slow process.
Using Machine Learning capabilities, the matching engine can learn from accountants’ past manual actions, capturing much richer detail of customer- and country-specific behavior and use hypothesis models to propose possible outcomes.

X Topic Abstract

The intro to AML,setting up the environment , building a regression model , classification modelling to regression and analyzing results

X Topic Abstract

The intro to AML,setting up the environment , building a regression model , classification modelling to regression and analyzing results

X Topic Abstract

I shall specifically stress on how are we building active machine learning workflows in designing the experience these products cater to their users while also touching upon the architecture which makes it feasible for implementation and monitoring production in the long run.


Speaker Profile:

Amitanshu heads the New Products and Business Initiatives team at Bharti Axa General Insurance and has a diverse experience in speaking at various data science keynotes for corporates and student groups alike.

After completing his bachelors in Mathematics and Scientific Computing from Indian Institute of Technology, Kanpur (IITK), Amitanshu has extensive experience in both academia and corporates – where he worked across verticals such as Telecommunications, Retail, E-Commerce and Insurance.

In his current role, Amitanshu leads the development of innovative micro-insurance product portfolios encompassing delays (flight delay insurance, cab trip delay insurance etc.), consumer durables & electronics such as gadget insurance, bicycle insurance etc. offering seamless customer claims experience with minimal customer involvement or paperwork.

Prior to this, Amitanshu has lead the end-to-end development & deployment of various Data Products leveraging sophisticated Machine Learning Algorithms on various fronts viz. Chatbots, Voicebots, Recommender Systems etc.

On a typical work day, you can find Amitanshu busy in strategizing optimal insurance cross-sell/up-sell strategies along with hands-on problem solving with Data Science & Product teams while implementing robust data architectures for optimum digital-first insurance product offerings. Apart from work, Amitanshu is a professional guitarist, with a passion for classic rock bands such as Pink Floyd, Led Zeppelin, Radiohead, etc. and an avid adventure sports enthusiast.

X Topic Abstract

Over the past few years, we have seen increased activity in the development of software powered applications in a range of sectors, such as autonomous vehicles, security, and data mining. As the level of investment and research effort in these fields intensifies, companies are looking to protect the rights to the Intellectual Property of their innovative solutions and algorithms to protect their technology roadmap and gain an edge over their competitors. But what are the different ways we can protect these innovations? In this presentation, we will discuss the protection of software - based innovations, discuss how to deal with A.I “inventors”, and provide examples of IP considerations that need to be considered during the product development lifecycle.


schedule 08:40AM – 09:00AM Login/Conference Overview
Consultant 1.21GWS
schedule
09:00AM – 09:45AM Applications of Artificial intelligence In healthcare: A miracle or menace - Click Here for More Info
Vineet Shukla, Sr. Director - Data Science & Machine Learning

schedule 09:45AM – 10:30AM Content discovery and recommendations for time sensitive content - Click Here for More Info
Henrik Petander, Head of Artificial Intelligence and Machine Learning, Sourse

schedule 10:30AM - 11:00AM Tea Break
schedule
11:00AM – 11:45AM Machine Learning and Real-Time Analytics with Apache Kafka KStream Applications - Click Here for More Info
Abhijit Kumar,Data Architect Deltatre

schedule
11:45AM – 12:30PM Machine Learning with Insights to Action - Click Here for More Info
Srivatsan Santhanam,Senior Director SAP

schedule 12:30PM - 01:30PM Break
schedule
01:30PM - 02:15PM “Enabling Meaningful Man-Machine Dialogue: Conversational AI”
Abhimanyu Dasgupta, Senior Manager, Applied AI, Deloitte Consulting

schedule
02:15PM - 03:00PM Intro to Azure Machine Learning (demo) - Click Here for More Info
Thavash Govender, Solutions Architect , Microsoft

schedule 03:00PM - 03:30PM Break
schedule
03:30PM - 04:15PM Reimagining Insurance using ML & AI - Click Here for More Info
Amitanshu Gupta, Head, New Products & Strategic Initiatives, Bharti AXA General Insurance

schedule
04:15PM - 05:00PM Strategies for protecting software-based innovations - Click Here for More Info
Dr Nikos Minas, Patent Attorney, Hanna Moore + Curley

Day 2: 21st May, 2020

Theme : Trending & Deep Learning

X Topic Abstract

1. I'll talk about different types of Computer Vision,
2.. Dive a little deeper into how 'Deep Learning' CNNs work,
3. Compare some of the 'state of the art' Computer Vision algorithms, give a rough guide on how to choose which algorithm to use and finally
4. Consider the ethics of Computer Vision.

Speaker Profile:  https://www.linkedin.com/in/christo-rademan-6141a579/

My life is machine learning R&D, currently being applied in CIB banking innovation and disruptive technologies research.

X Topic Abstract

Adoption of Data Science and Machine Learning is growing at a rapid rate. But success metrics of such models in production and eventual and sustained business value is still low. Can adoption of some of the latest technologies including Micro services, Containerization and Cloud native applications and architecture help improve the overall success metrics? Learn in this session.


Speaker Profile:

Vinod Khader has around 20 years experience in Software Development and is an Associate Director at IBM Software Labs in the Data and AI division. In the current role, he leads the development of Watson Machine Learning Platform on IBM's public and private cloud platforms with teams across the globe. He enjoys talking at technical forums and conferences. Watson Machine Learning platform help enterprises in managing the end to end Machine Learning and Model management life cycle including from Model Training, Evaluation and Serving.

Linkedin profile - https://www.linkedin.com/in/vinodkhader/

X Topic Abstract

As the entire business world is heading toward a revolutionary transformation from an era of “Digitisation” to an era of “Cognification”, we must confess that our banks and financial institutions worldwide are the ones that have recognised the potentials of Artificial Intelligence at a very early stage and adopted it in their transformation journey.

Using Artificial Intelligence to redefine their products, processes and the strategies is the main consideration for most of the forefront banks and financial institutions today.

This includes predictive and cognitive capabilities enabled through cutting-edge technologies like Machine Learning, Deep Learning and Natural Language Processing.

X Topic Abstract

In this talk we will explore the evolution of NLP technology, core building blocks and computing backend with some example use cases. We will also talk about few uses cases we have implemented in Tata Elxsi - for example , Virtual sales assistant and Virtual Car Assistant.


Speaker Profile:

Biswajit Biswas is Chief Data Scientist @ Tata Elxsi heading the AI Center of Excellence responsible for driving innovation in Digital systems for Communication, Media and Transportation business verticals . He has over 22+ years of Industry experience.

He is very well versed in AI and Deep learning , especially, in the area of computer vision, language and text processing . Before heading Data Science team, he was responsible for Wireless Communication, Multimedia Systems, Digital signal processing , Image, Video and Audio coding, Machine Vision System. He has hands on with developing algorithms in C/C++, Python, TensorFlow, R and devotes significant time in coding, coaching team in various development activities. He also holds a patent for innovation in V2X related technology and has filed Patents for few other innovations in the area of Predictive Machine learning.

He is a Certified Data Scientist from Massachusetts Institute of Technology (MIT, US) Media Lab , Masters from BITS and BE from Jadavpur University Calcutta.

X
X Topic Abstract

With the rapid pace of connectivity and product innovations that we've seen in the past decade across the online marketplace, simple A/B testing for deciding the efficacy of an action is not enough. In the era of intense competition, it is essential that we take that leap to learn not just population summary from A/B tests, but to make inferences on personalised levels - and spend on marketing only where it's necessary.
The applications are open to all industries - especially the ones where actions need to be determined and learned from big data anlaytics - for instance, who are the people whom this product feature should be targeted to - and that “who” can be answered on an individual level by building models on test-control testing and can be applied to the overall population.
Machine Learning Takeaways: Uplift Forest Models to determine the population fit for targeting. The idea of uplift, and how it relates and compares to traditional statistical models


X Topic Abstract

Sparse data has always been a problem with major classification task in NLP. With the advent of Deep Learning technology for NLP, extensive research on language model which can scale to different classification task has been researched. Recently powerful language models have shown that there is scope to apply such model into different classification task in parallel and transfer learn between different domain. In this talk we will cover different ways to transfer learn in NLP and how recent advances in language model can help multi task and transfer learn to improve performance.


Speaker Profile:

I am a deep learning researcher, after graduating from Indian Statistical Institute with Master's in Computer Science, with specialization in NLP and IR, I have worked with Bing Ads, Microsoft R&D and HSBC Bank as decision scientist to build risk models for bank. I am currently working in Amazon as a data scientist to build models to cater to amazon internal network.

X Topic Abstract

Behavioral Science seeks to understand how humans process, absorb, and react to information. Machine learning is all set to revolutionize the application of behavioral science. With the application of both, we are able to test, deliver and evaluate behaviorally informed programs in the most effective and efficient way. This in term likely improves human decision-making. My talk will consider pulling out examples on this context and show the power of hyper-personalization, nudge theory, etc where machine learning and behavioral science plays pivotal parts.


X Topic Abstract

Artificial intelligence (AI), machine learning and data have brought enormous opportunity to healthcare. While much of the attention has been centered on use in improving treatments and enabling physicians and providers to make decisions, these technologies have exciting potential to impact patient engagement. It ranges from improving patient outcomes to reducing costs, and driving better care. It also has the power to support providers for more efficient, effective processes. This presentation will provide attendees with a comprehensive look at the use AI and its subset technologies in patient engagement, share case study examples, and empower organizations to engage patients at a powerful new level.


schedule 09:00AM – 09:10AM Conference Overview
Consultant 1.21GWS
schedule 09:10AM – 09:50AM Deep learning for computer vision - Click Here for More Info
Christo Rademan, Data Scientist, Rand Merchant Bank

schedule
09:50AM – 10:30AM How Microservices Containers and Serverless architecture can accelerate time to value of your Machine Learning & AI projects - Click Here for More Info
Vinod Khader, Associate Director, Watson Machine Learning Platform Development, IBM

schedule 10:30AM - 11:00AM Break
schedule
11:00AM - 11:40AM How Artificial Intelligence is Transforming Banking and Finance - Click Here for More Info
Utpal Chakraborty, Head AI, Yes bank

schedule
11:40AM – 12:20PM Deep NLP - Helping to break the Language barrier - Click Here for More Info
Biswajit Biswas, Chief Data Scientist, Tata Elxsi

schedule
12:20PM – 01:00PM Let's make AI answerable !
Bharathi A., Principal , AT&T

schedule 01:00PM – 02:00PM Break
schedule
02:00PM – 02:40PM Personalised A/B Testing Framework - Click Here for More Info
Chandra Bhanu Jha, Data Scientist Goldman Sachs

schedule
02:40PM – 03:20PM Multi Tasking Deep Learning for Natural Language Processing – Transfer Learning - Click Here for More Info
Debjyoti Paul, Data Scientist, Amazon

schedule 03:20PM – 03:50PM Break
schedule
03:50PM – 04:30PM Behavioral Science & Machine Learning - Click Here for More Info
Arghya Mandal, Director, Digital and Analytics Incedo Inc

schedule
04:30PM – 05:10PM Applying AI And Machine Learning To Increase Patient Engagement in Healthcare - Click Here for More Info
Ahmad Jubran, Chief Technology Officer, ConsjeoSano

Register Your Attendance At Conference 2020

Any Question? Call: +919810667556

Ticket Price & Plan

Any One day

ZAR 4500

Till 21st May, 2020

Conference ticket

Both Days

ZAR 8000

Till 21st May, 2020

Conference ticket

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Contact Us


Australia - +61416893630 / +61416576383

USA - +1 908 444 0221

India - +919810667556

naveen@1point21gws.info

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