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August 16, 2019, Auckland
Venue : Karstens Auckland

World Machine Learning Summit

August 16, 2019, Auckland
Venue : Karstens Auckland

World Machine Learning Summit

August 16, 2019, Auckland
Venue : Karstens Auckland

World Machine Learning Summit

Ways to convince Your Boss Ways to Save

Briefly Know About This Event

We are very excited to announce our 1st edition of World Machine Learning Summit-2019, Auckland being organized by 1point21GWs, stay ahead with us!

Our CEO Says - "1.21GWS (1point21gws), the name is inspired by the all-time hit time-travelling movie "Back to the Future". As in the movie, the protagonists required 1.21 GWs of power to move in time, this company provides the 'right' knowledge and content needed to build the world of the future."

World Machine Learning Summit is a 1 day conference in Auckland on August 16, 2019. This is a Program being curated based on guidelines from industry experts, with a target of about 75+ delegates.







Who Should Attend :
• 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

  • 8+

    Global Speakers

  • 8+

    Topics

  • 75

    Tickets

  • 1

    Day

Conference Schedule

X Topic Abstract

Terms such as "Machine Learning", "Artificial Intelligence" and "Data Science" have become buzzwords that represent sets of useful tools that almost every industry is trying to make use of in the current era, and the academia/scientific community is no exception. While different disciplines may have their unique definitions for each of these buzzwords, there are common services that attempts to collectively satisfy all of these distinct requirements. These common services often fall short of the needs of technical disciplines, which is not uncommon in the scientific community. This presentation is an attempt to summarize the current views on related technologies from the scientific community, and hopefully provide useful insights of possibilities to other disciplines/industries.

X Topic Abstract

With the rapid vehicle volume growth on roads in New Zealand and specifically Auckland, the performance of transport systems becomes an important topic for all members of the urban community. With the availability of advanced data collecting techniques, which offers detailed insight into the movements of the vehicles, we are now able to use state-of-the-art Machine Learning (ML) techniques to overcome traditional challenges in transportation engineering. Through my presentation I will go through some practical problems which were addressed using ML techniques in transportation engineering.

X Topic Abstract

The success of a machine learning project depends highly on the impact it will bring and how well various levels of audience receive the scientific insights. This presentation shows how visualisation has been adopted in machine learning to illustrate patterns and tell stories using data collected. A common approach in data exploration is to first visualise the distribution of the data, spot anomalies and cyclical patterns, then compare and observe the contrast of those patterns. Recently, with the development of computational power, there has been a change in the landscape of how data can be visualised by taking advantage of practical tools and libraries. In this talk, I will share the visualisations used throughout the machine learning process: training data, assessing model and communicating results.

X Topic Abstract

Artificial Intelligence has become a critical practice by achieving business transformation and creating unique opportunities for companies worldwide. As a simulation of human intelligence processes by machines, this specialised technique is a crucial factor of success by providing a range of new functionalities from automation to machine learning for businesses such as finance, healthcare, education, law, and manufacturing.

X Topic Abstract

Software testing is a complex task that often requires days to complete. With releasing new products and features being more important than ever companies need to cut the time it takes to test their software. However, users now have more choice, are more technically savvy and won't put up with bugs. We will explore a case study of how a company in the USA utilized AI to predict which tests would fail and which commits were most risky to reduce their automated testing by 90% and their manual testing by 60%.

X Topic Abstract

The Rise of the Machines
Artificial Intelligence (AI) algorithms are everywhere. From scoring your credit score to recommending movies for your NetFlix, from detecting fraudulent transactions on your debit card to diagnosing diseases. AI is slowly but surely dominating the critical decision-making functions of our lives. [Here I will provide a brief explanation of how is AI is dominating our daily lives]

The Crack in the Diamond
Current AI algorithms have an inherent flaw. The lack of an explanation. Humans can explain their daily decisions. If a clinician recommends a particular diagnoses, he can explain his decision. Human can explain the “Why” of their decisions while AI algorithms are unable to do so currently. AI algorithms are treated as black boxes that only provide magical answers. Most experts in AI have ignored this flaw but new legislation being enacted around the world is forcing them to rethink. [Here I will elaborate on the problems with having an AI which does not explain its decisions]

The Empire Strikes Back
On 14th April 2016, a landmark legislation was passed by the European Union (EU). EU General Data Protection Regulation (GDPR) is a wide-ranging and complex regulation designed to strengthen and unify data proptection for all individuals within the EU. It also drafted to address the risk of companies making unfair decisions about individuals using AI algorithms. In other words, come the May 2018 deadline when the GDPR kicks in, if you are in the EU, AI owes you an explanation every time it gives a particular recommendation or executes an action that can affect individuals. So what will this regulation mean for AI around the world. And how long before this kind of legislation reaches our shores? [Here I will explain what does the GDPR regulation mean for organizations and its impact on AI rollout]

Life can be unfair and so can AI
AI is only as good as the data that is used to train it. If your data is biased, your AI will be biased. AI programs can exhibit racial and gender biases if the data used to train it has those biases. A recent example was the Microsoft Tay Twitter chatbot which started worshipping Hitler after receiving input from racists. AI is like a child that needs to be taught the difference between good and bad. Without proper safeguards, these AI tools risk eroding the rule of law and diminishing individual rights.
[In this section I will explain, How AI can be biased and its impact on individual rights]

Hitchhikers guide to Explainable AI
Explaining what goes inside a neural network is a complicated task even for experts. Communicating the reason for the decision to an individual without any knowledge of machine learning is only going to make this task harder. Again it is not impossible but it is complicated.

There are two ways to work around this problem
Human-in-the-loop: Give human decision makers rights to override or intervene automated algorithms. This already happens in many services for example in medicine where is AI is never the only or final decision maker.AI should be used as an assistant rather than a master.
The second options is Explainable AI. It requires users to go beyond just implementing algorithms and trying to increase accuracy of models. This can be divided into three parts :

Explaining AI for structured information
Explainable AI for text information
Explainable AI for image recognition

X Topic Abstract

Overcoming the loop holes in Machine Learning to enable the leading Telecom and Digital Service Provider in New Zealand by developing a ML based churn model in saving would be churners from leaving.

- Built an End to End Machine Learning Churn predictions model and Tracking Methodology that automatically tracks which Campaigns have been successful in saving Customers at risk of leaving.
- The solution generates predictions every day without failing due to drifts and new patterns observed in the customers data.
- Tweaking traditional data science methods conceptually to deliver an automated solution.
- This fully automated solution runs every day with zero human touch through

data preparation, model update, prediction generation, data quality monitoring, campaign creation and distribution, campaign tracking and measuring customer success rates

Speaker profile

With 8+ years of industry experience of working as a Data Scientist in various domains and verticals, I hold a Masters in Statistics and specialize in Automating Machine Learning leveraging state-of-art data science concepts. I help my client's solve their business problems by leveraging Automated Machine Learning.
A variety of ML solutions have been automated. To name a few, MMM, Customer Churn Prediction, Campaign Optimization, Segmentation, Master Data Management, Purchase Preference Analysis, UpSell and Cross Sell, Market Basket Analysis, etc.
The automation mechanism is an E2E fully automated which runs on a day to day basis without errors and generates report for data shifts and changing data patterns.
The Automation mechanism is supported by a Fail-Safe mechanism which is developed on top of each and every solution which understands the underlying ML and Data Science concepts and drawbacks and thereby overcomes them and shares a report for the new behaviors noticed in the data which could be then utilized to update model to learn from.
I am at present working on Contract from Infosys for Spark NZ, the leading telecom and digital service provider in NZ as a Data Scientist.

X Topic Abstract

ESP is working with more than 72 organisations, over 500 sites across New Zealand. ESP’s data migration to AWS has been transformational for ESP and our clients which include industrial, retail, and commercial sites. ML/AI has been key in transforming not only how utility efficiency is managed, but also asset utilisation, predictive maintenance, and process optimisation. Industrial sites have been able to reduce their electricity, gas, and water consumption by up to 40% with ESP’s phantom analytics platform. Mitigating global warming to < 1.5℃ is going to require a quantum shift in the way we manage energy and carbon emissions. With the Carbon Net Zero bill proposing reduction of all greenhouse gases to net zero by 2050, it has become ever so important for organisations to reduce their carbon footprint. Energy makes up a significant portion of the carbon footprint of industrial organisations. ESP is now able to apply advance data analytics, machine learning and artificial intelligence to the utility sub-metering data. This talk will outline ESP’s journey to, and application of ML/AI in decarbonisation, energy management and efficiency.

Speaker profile

Mannat specialises in identifying and implementing energy saving measures for commercial buildings, SMEs, and large industrial sites. With specific experience in compressed air, steam and hot water systems, lighting, HVAC and other process improvements. He is an accredited Industrial Energy Systems Optimisation Specialist.
With degrees in; Master’s Degree in Energy from the University of Auckland, Bachelor of Engineering (BEng), Mechanical Engineering - Fachhochschule Aachen (FH Aachen).
Mannat’s work has helped ESP’s NZ clients achieve energy savings in excess of $23 million dollars, 171 Giga Watt Hours of energy or 22,154,591 kg CO2-e avoided.

schedule 08:45AM - 09:15AM Registration / Conference Overview
Nitesh Naveen, Partner & Managing Consultants - Digital Transformation, 1.21GWS
schedule 09:15AM - 10:00AM Machine Learning/Artificial Intelligence/Data Science - Scientific and Technical Applications - Click Here for More Info
Jason Tam, Data Scientist, enviPath
schedule 10:00AM - 10:30AM Tea Break
schedule 10:30AM - 11:15AM Application of machine learning techniques in transportation engineering - Click Here for More Info
Soroush Rashidi, Data Scientist, WSP Opus
schedule 11:15AM - 12:00PM Visualisation for Machine Learning: How industries use it to help their decision making - Click Here for More Info
Pei-Luk Low, Data Scientist, BNZ
schedule 12:00PM - 01:00PM Lunch Break
schedule 01:00PM - 01:30PM Round Table Discussion
schedule 01:30PM - 02:15PM How will Artificial Intelligence transform business and the very nature of work? - Click Here for More Info
Dayrell Lana, Senior Consultant, Fulcrum Decision (FDL)
schedule 02:15PM - 03:00PM How companies are reducing their testing time by up to 80% using Machine Learning - Click Here for More Info
James Farrier, Founder, Appsurify
schedule 03:00PM - 03:30PM Tea Break
schedule 03:30PM - 04:15PM Explainable AI - Click Here for More Info
Habib Baluwala, Senior Data Scientist, Spark New Zealand
schedule 04:15PM - 05:00PM Machine Learning Methods and Analysis - Click Here for More Info
Smita Agarwal, Data Scientist, Infosys
schedule 05:00PM - 05:45PM How Machine Learning and Artificial intelligence has improved energy efficiency at industrial manufacturing sites - Click Here for More Info
Mannat Choksi, Energy Consultant, ESP

Conference Ticket Price & Plan

Group of three or more(Early Bird)

NZD 364

Till 16th June, 2019

Conference ticket

Tea break

Group of three or more(Standard)

NZD 414

Till 16th August, 2019

Conference ticket

Tea break

Individual(Early Bird)

NZD 553

Till 16th July, 2019

Conference ticket

Tea break

Individual (Standard)

NZD 692

Till 16th August, 2019

Conference ticket

Tea break



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Register Your Attendance At Conference 2019

Any Question? Call: +91 97390 49970