Enterprise Data Science Architecture Conference 2020

27th March 2020 at Melbourne Marriott Hotel, Melbourne Australia. All ticket holders will be able to attend virtually. This year, we are hybrid face-to-face and virtual.

Deploy Real Data Science Solutions at Enterprise Scale - Our industry moves fast. Don't get left behind.

Learn how leading enterprises deploy scalable data science solutions. Connect with leaders in this new field of data science architecture. Learn best practices and keep up with the latest innovations.

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The Enterprise Data Science Architecture Conference focuses on how to properly productionise data science solutions at scale. We have confirmed speakers from ANZ Bank, Coles, SEEK, ENGIE, Latitude Financial Services, Microsoft, AWS and Growing Data. The combination of presentations is intended to paint a complete picture of what it takes to productionise a profitable data science solution.

Our field flies fast. Keep your skills current.

As an industry, we are figuring out how to best build end-to-end machine learning solutions. The workflow starts with data sourcing, data preparation, feature engineering, building the machine learning model, testing the ML model, deploying to production, monitoring in production, getting feedback and then repeating the process with improved models and better features. Companies are currently learning how to build it all into one smooth pipeline. The presentations at our conference will show you what works today in leading enterprises. Reserve your place now, while tickets are still available.

Your toolkit for real projects.

Enterprise scale brings its own challenges and solutions. Automation, resilience, scalability and the appropriateness of the data science techniques used have significant impact on profit and loss. Governance is also important because of privacy laws and because large enterprises often operate in regulated industries. The presentations at our conference will focus on how enterprises are productionising machine learning solutions. We are still learning as an industry. Knowledge of best practices in end-to-end machine learning pipelines will become essential skills - demanded by clients and employers. Keep your skills current. Register today.

Where Data Science Architecture Fits

Our presentations will focus on how to properly deploy data science solutions in the enterprise. Enterprise solutions encompass the four broad categories below.

Infrastructure
Databases, Data Lakes, VMs, Kubernetes, Containers, Load Balancers, Virtual Networks, REST APIs, Dashboard Servers, Security, Availability, Resilience, Scalability
Data
Datasets, MLOps, Timeliness of Data, Feature Engineering, Transformations, Data Security, Encryption, Governance, Data Lineage, Data Pipelines
Methods
AutoML, Deep Learning, Reinforcement Learning, Recommender Systems, Supervised Learning, Unsupervised Learing, Charts, Dashboards, MLOps, AI, Statistics
Regulatory
Australian Privacy Principles, Responsible Gambling, Direct Marketing Code of Conduct, Medical Information, Security, Encryption, Governance

Speakers

Learn From Leaders

ANZ Bank

Jonathan Robinson

Principal Data Scientist @ ANZ Bank

Expand

Building a successful data science capability

Jonathan Robinson

Jonathan is a data scientist and engineer, leading analytics and data science for retail banking products and services for ANZ's Australian customers. He has previously worked at the Commonwealth Bank in the big data group, built data science platforms and capability at National Australia Bank and Suncorp and has several years experience researching and developing algorithmic systems for high frequency stock trading using machine learning for pattern recognition. He holds an MSc in physics from Otago University and a PhD in machine learning from Auckland university.

Coles

Richard Glew

Head of Data Engineering @ Coles

Expand

What does it take to build a great data platform?

Coles, like many organisations, has a data problem. We have some, we want a lot more, but it’s not easy. What can a large organisation with an established technology footprint do to radically transform the way it approaches data, making it available and good enough to use for all those fancy machine learning and analytics functions that businesses are demanding? Microsoft on the other hand, leverages data to continually transform its business, products and services and helps its customers undertake similar journeys. Richard and Ananth will talk about how Coles & Microsoft partnered to build Coles new data platform on Azure. They will explain the key problems that it is solving and the building blocks needed for successful delivery in an enterprise organisation. Hint - it’s not all about the technology!

Richard Glew

Richard has had a wide ranging career in technology, working in software engineering and data for more than 20 years throughout Australia, Europe and the United States. His background includes senior leadership roles at Disney, CTO of a digital education company, and prior to joining Coles, was a Principal Technologist at ThoughtWorks helping clients build cloud native platforms for systems and data. Now heading up Data Engineering and Operations at Coles, he is leading the retailer’s new data platform based on Azure. Richard holds a degree in Business Information Systems and is a certified Google Professional Cloud Architect.

Microsoft

Ananth Prakash

Lead Architect - Data, AI & IoT @ Microsoft

Expand

Presenting Two Talks

Operationalising ML model release using Azure DevOps

For the longest time data science was often performed in silos, using large scale compute operating across isolated copies of production data. This process was not repeatable, explainable or scalable and often introduced business and security risk. With modern enterprises now adopting a DevOps engineering culture, no longer can machine learning development practise operate in isolation of the business or existing applications teams. This demo heavy session introduces the new Azure ML Services capabilities and how this can assist to bring the practice of data science into the age of modern DevOps.

What does it take to build a great data platform?

Coles, like many organisations, has a data problem. We have some, we want a lot more, but it’s not easy. What can a large organisation with an established technology footprint do to radically transform the way it approaches data, making it available and good enough to use for all those fancy machine learning and analytics functions that businesses are demanding? Microsoft on the other hand, leverages data to continually transform its business, products and services and helps its customers undertake similar journeys. Richard and Ananth will talk about how Coles & Microsoft partnered to build Coles new data platform on Azure. They will explain the key problems that it is solving and the building blocks needed for successful delivery in an enterprise organisation. Hint - it’s not all about the technology!

Ananth Prakash

Ananth has a successful track record in delivery of strategic projects, products and platforms in the Data & AI domain for 16 years across North America, Australia and Asia. Some of his roles include leading product engineering groups at Oracle, Managing Consultant at IBM and Senior Technical Architect - Data & AI at Infosys. As a Lead Architect at Microsoft, Ananth helps enterprise customers architect & deploy strategic solutions on Azure to accelerate their business transformation journey.

Microsoft

Eugene Dubossarsky

Chief Data Scientist @ AlphaZetta

Expand

Human Infrastructure for Data Science and Analytics

Human Infrastructure is the most ignored, and most key component of any organisation’s analytics infrastructure initiative. Building a data literate infrastructure, with the correct knowledge, skills and incentives is key to ensuring that the value of analytics is realised and electronic analytics infrastructure does not go to waste. Time permitting, this presentation will also consider the changing role of human infrastructure in difficult times, such as we are experiencing now.

Eugene Dubossarsky

Dr Eugene Dubossarsky is a leader in the analytics field in Australia, with over 20 years’ commercial data science experience. He is a the Managing Partner of the AlphaZetta Global Analytics Training Academy as well as AlphaZetta's Chief Data Scientist. Eugene has worked as a data scientist, software developer, entrepreneur, trainer, consultant, financial trader and speculative sports punter, applying his data science and analytics skills in all these fields. He is also the founder of the Australia–New Zealand Data Analytics Network, with over 19,000 members, and the head of the Data Science Sydney group (6,000+ members), and Big Data Analytics Sydney (7000+ members). Eugene is the Chief Scientist of reask, a global climate and catastrophe modelling company. He is regularly invited as a conference presenter, consultant and advisor, and appears in print and on television to discuss data science and analytics, and advisory board member for listed companies, advising on AI and Data Science. Eugene also applies data science in an entrepreneurial setting, to financial trading and online startups, and is the creator of ggraptR, an interactive visualisation package.

NAB

Dmitri Markman

General Manager, Analytics, CX Division @ National Australia Bank

Expand

Unleashing the Power of Analytics to Create a Better Bank for Our Customers

Dmitri Markman

Dmitri has 20+ years financial services experience in various Credit Risk and Analytics roles spanning first line and group support functions. In 2019 he was appointed as General Manager, Analytics for Customer Experience Division at National Australia Bank, where he is responsible for development of specialised ‘deep’ customer insights that are used as inputs into pricing, proposition development, and marketing across all channels and segments. In this key strategic role, Dmitri is entrusted with delivering an analytics roadmap to empower NAB and its customers to achieve their goals by helping them with banking decisions. Prior to this role Dmitri was an integral part of the team that developed the market leading small business digital origination capabilities, Quickbiz, that utilises internal and external data to dramatically improve customer experience and significantly reduce efforts in assessing new lending requests. Known across the enterprise for his track record of consistent delivery, Dmitri’s current focus is on lifting analytics capability within the division and collaborating with Enterprise Data to ensure alignment on data architecture

Dmitri joined NAB in 2014 from ANZ where over the span of 11 years he held several senior roles across the group including Head Credit Risk, Small Business and Esanda; Head of Credit, Unsecured Consumer Lending; and being responsible for development of all decisioning models for Australia and Asian markets. Prior to ANZ, Dmitri spent 4 years at Ford Credit Asia Pacific being involved in retail credit management for 11 markets in the region.

Dmitri holds a Bachelor Degree in Commerce (Finance) from Melbourne University, as well as various professional qualifications gained throughout his career.

Dmitri is married and has a 9-year old son, Lucas. Outside of work Dmitri enjoys spending time with his family, going to the theatre and attending sporting events.

SEEK

Pramudi Suraweera

Principal Data Scientist @ SEEK

Expand

Deployment of real-time predictive models in Kubernetes

Pramudi Suraweera

Pramudi is a Principal Data Scientist at SEEK. He has been leading the development of AI services to help hirers post jobs and increase transparency between candidates and hirers. He has also been leading the modelling for SEEK’s new pricing initiative. His varied background includes leading an analytics team at Telstra within the direct marketing team, working at Deloitte as Senior Data Scientist, developing new machine learning algorithms as a Researcher at Monash University and developing models for teaching mathematics as a Cognitive Scientist at Carnegie Learning (USA). Pramudi holds an MSc in Computer Science and a PhD in AI for Education from the University of Canterbury.

IAG

Dimitri Semenovich

Analytics Director @ IAG

SEEK

Manuel Weiss

Tech Lead Personalisation Squad, AI Platform Services @ SEEK

Expand

Building global recommendation systems for local markets - The data challenges

Dr. Manuel Weiss

Manuel has been playing with computers since the early 90s, starting on MS-DOS and 640kB of RAM. He has studied a bit of Physics and has a degree in Computational Linguistics, as well as a PhD in Bioinformatics. He has worked in Zurich, London and Melbourne in a number of different companies and industries. Ranging from a media management system for police agencies to ad tech. He is currently the tech lead for the recommendations team at SEEK’s AI Platform Services department.

AWS

Julian Bright

Machine Learning Specialist @ AWS

Expand

From A to AI: Operationalising Machine Learning in 1 day

Companies are recognising the importance of cross-functional expertise in bringing AI-driven products to market, supported by efficient tooling. Just like DevOps helped organisations accelerate their software delivery schedules, in this session you will learn how to build an end-to-end pipeline for continuous delivery of Machine Learning models. By automating ML ops with Amazon SageMaker and serverless workflows you will be able to build, deploy & monitor models at scale to maximise the business value to your organisation.

Julian Bright

Julian has over 20 years’ experience building software and data science solutions in Australia and Europe from small start-ups to large multi-nationals. As a Machine Learning Specialist at AWS, Julian helps customers solve AI and ML problems by developing and deploying cloud native solutions at scale. Prior to AWS, Julian held senior positions as a Data Scientist and Architect as SEEK, where he applied machine learning and data engineering to develop recommendation and search algorithms to match candidates to hirers. Julian has won a number of hackathon events, is active in the Melbourne start-up community and has spoken at events including AWS Sydney Summit, Health 2.0, NDC and IJCAI.

SEEK

Tim Barber

Senior Engineer @ SEEK

Expand

Deployment of real-time predictive models in Kubernetes

Tim has been working in the Melbourne IT sector for nearly 20 years across a range of roles and different tech stacks. Currently a team leader at SEEK working alongside data scientists to help them deliver their models to production via Java microservices and Kubernetes.

Latitude Financial Services

Shan Moorthy

GM of Architecture, Data & Engineering (CTO) (Acting) @ Latitude Financial Services

ENGIE

Maya Muthuswamy

General Manager Analytics @ ENGIE

Expand

Real analytics in the wholesale electricity market

Maya Muthuswamy is the General Manager of Analytics at ENGIE Australia and New Zealand. She leads a team of problem-solvers to help the business solve complex and ambiguous problems, typically related to Australian wholesale electricity and gas markets. Maya has been leading this team since early 2017, and prior to this was the senior analyst in the team. Before joining ENGIE, Maya worked at AGL in load forecasting and commercial analysis. Maya's experience at AGL provided her with a solid grounding in the electricity and gas markets after finishing her PhD in 2008 at the University of Melbourne.

Microsoft

Julian Lee

Senior AI Technical Specialist @ Microsoft

Databricks

Jonathan Choi

Solutions Architect @ Databricks

Expand

Standardising the ML lifecycle with Databricks

The ML lifecycle is ever-evolving and it presents a number of challenges for data scientists and data engineers. Databricks believes there should be a better way to manage the ML lifecycle from preparing data to building models to deploying them to production. In this session, you will learn how to use Delta Lake and MLFlow to simplify ML workflows, improve model reproducibility and encourage efficient collaboration.

Jonathan Choi

Jonathan is a Solutions Architect at Databricks. He focuses on helping customers solve their problems and realise the business value in data. Prior to joining Databricks, Jonathan has built big data analytics solutions for companies across different countries and industries, from banks and advertisers in Australia to retailer in the U.S and health insurer in China. He has also built and led the data & analytics team at a fintech start-up in Berlin.

Cloud Security Podcast

Ashish Rajan

Technical Director, Cybersecurity @ Cloud Security Podcast

Expand

There is no Data Strategy without Data Security

If you are building Data Strategy roadmap for your organisation and want to know what capabilities you should consider to manage security risks and compliance, then this talk is for you. The talk goes through capabilities and where applicable tools/process that can be used to manage these risks on a platform that is going to be dealing with a lot of data at a scale that no one else in your organisation would have seen. If you have already built your strategy roadmap, you might still find something to take home. I would love to talk to you if you feel if I have missed something. The talk is going to be as interactive as possible with the LIVE session.

Ashish is a Technical Director for Cybersecurity with 10+ yrs of helping organisations release products and analytics build on cloud to customers more frequently securely. My previous talks and podcast can be found on www.ashishrajan.com

Conference Program

Schedule of Presentations

Time Main Track Senior Leaders Track MLOps Track The Dataiku Experience
8:30AM Doors Open! Take your name badge and grab a coffee!
9:00AM Welcome Address
9:10AM What it takes to build a great data platform
Richard Glew (Coles) + Ananth Prakash (Microsoft)
10:00AM Building a successful data science capability
Dr. Jonathan Robinson (ANZ Bank)
10:40AM Morning Tea, Networking & Sponsors!
11:10AM Shan Moorthy (Latitude Financial Services) From A to AI: Operationalising Machine Learning in 1 day
Julian Bright (AWS)
11:35AM Human Infrastructure for Data Science and Analytics
Dr. Eugene Dubossarsky (AlphaZetta)
12:00PM Unleashing the Power of Analytics to Create a Better Bank for Our Customers
Dmitri Markman (NAB)
Operationalising ML model release using Azure DevOps
Ananth Prakash (Microsoft) & Julian Lee (Microsoft)
12:25PM Real analytics in the wholesale electricity market
Maya Muthuswamy (ENGIE)
12:50PM Lunch, Networking & Sponsors! Thinking about model lifecycle in the business.
Yannis Ghazouani (Dataiku) & Grant Case (Dataiku)
1:50PM Standardising the ML lifecycle with Databricks
Jonathan Choi (Databricks)
2:40PM Afternoon Tea, Networking & Sponsors!
3:10PM Building global recommendation systems for local markets - The data challenges
Dr. Manuel Weiss (SEEK)
3:50PM There is no Data Strategy without Data Security
Ashish Rajan (Cloud Security Podcast)
4:30PM Artificial Intelligence in Financial Services
Dimitri Semenovich (IAG)
4:50PM Deployment of real-time predictive models in Kubernetes
Dr. Pramudi Suraweera (SEEK) & Tim Barber (SEEK)
5:30PM Closing Address
5:40PM Evening Drinks, Canapes & Sponsors! Training deep learning algorithms on GPUs and deploying on elastic resources using Kubernetes.
Yannis Ghazouani (Dataiku) & Grant Case (Dataiku)

Presentations

What you can learn at the conference

What does it take to build a great data platform?

Coles, like many organisations, has a data problem. We have some, we want a lot more, but it’s not easy. What can a large organisation with an established technology footprint do to radically transform the way it approaches data, making it available and good enough to use for all those fancy machine learning and analytics functions that businesses are demanding? Microsoft on the other hand, leverages data to continually transform its business, products and services and helps its customers undertake similar journeys. Richard and Ananth will talk about how Coles & Microsoft partnered to build Coles new data platform on Azure. They will explain the key problems that it is solving and the building blocks needed for successful delivery in an enterprise organisation. Hint - it’s not all about the technology!

Richard Glew

Richard Glew - Head of Data Engineering @ Coles

Richard has had a wide ranging career in technology, working in software engineering and data for more than 20 years throughout Australia, Europe and the United States. His background includes senior leadership roles at Disney, CTO of a digital education company, and prior to joining Coles, was a Principal Technologist at ThoughtWorks helping clients build cloud native platforms for systems and data. Now heading up Data Engineering and Operations at Coles, he is leading the retailer’s new data platform based on Azure. Richard holds a degree in Business Information Systems and is a certified Google Professional Cloud Architect.

Ananth Prakash

Ananth Prakash - Lead Architect - Data, AI & IoT @ Microsoft

Ananth has a successful track record in delivery of strategic projects, products and platforms in the Data & AI domain for 16 years across North America, Australia and Asia. Some of his roles include leading product engineering groups at Oracle, Managing Consultant at IBM and Senior Technical Architect - Data & AI at Infosys. As a Lead Architect at Microsoft, Ananth helps enterprise customers architect & deploy strategic solutions on Azure to accelerate their business transformation journey.

Deployment of real-time predictive models in Kubernetes

Pramudi Suraweera

Dr. Pramudi Suraweera - Principal Data Scientist @ SEEK

Pramudi is a Principal Data Scientist at SEEK. He has been leading the development of AI services to help hirers post jobs and increase transparency between candidates and hirers. He has also been leading the modelling for SEEK’s new pricing initiative. His varied background includes leading an analytics team at Telstra within the direct marketing team, working at Deloitte as Senior Data Scientist, developing new machine learning algorithms as a Researcher at Monash University and developing models for teaching mathematics as a Cognitive Scientist at Carnegie Learning (USA). Pramudi holds an MSc in Computer Science and a PhD in AI for Education from the University of Canterbury.

SEEK Logo

Tim Barber - Senior Engineer @ SEEK

Tim has been working in the Melbourne IT sector for nearly 20 years across a range of roles and different tech stacks. Currently a team leader at SEEK working alongside data scientists to help them deliver their models to production via Java microservices and Kubernetes.

Building global recommendation systems for local markets - The data challenges

Dr. Manuel Weiss

Dr. Manuel Weiss - Tech Lead Personalisation Squad, AI Platform Services @ SEEK

Manuel has been playing with computers since the early 90s, starting on MS-DOS and 640kB of RAM. He has studied a bit of Physics and has a degree in Computational Linguistics, as well as a PhD in Bioinformatics. He has worked in Zurich, London and Melbourne in a number of different companies and industries. Ranging from a media management system for police agencies to ad tech. He is currently the tech lead for the recommendations team at SEEK’s AI Platform Services department.

Real analytics in the wholesale electricity market

Maya Muthuswamy

Maya Muthuswamy - General Manager Analytics @ ENGIE

Maya Muthuswamy is the General Manager of Analytics at ENGIE Australia and New Zealand. She leads a team of problem-solvers to help the business solve complex and ambiguous problems, typically related to Australian wholesale electricity and gas markets. Maya has been leading this team since early 2017, and prior to this was the senior analyst in the team. Before joining ENGIE, Maya worked at AGL in load forecasting and commercial analysis. Maya's experience at AGL provided her with a solid grounding in the electricity and gas markets after finishing her PhD in 2008 at the University of Melbourne.

Building a successful data science capability

Jonathan Robinson

Jonathan Robinson - Principal Data Scientist @ ANZ Bank

Jonathan is a data scientist and engineer, leading analytics and data science for retail banking products and services for ANZ's Australian customers. He has previously worked at the Commonwealth Bank in the big data group, built data science platforms and capability at National Australia Bank and Suncorp and has several years experience researching and developing algorithmic systems for high frequency stock trading using machine learning for pattern recognition. He holds an MSc in physics from Otago University and a PhD in machine learning from Auckland university.

Operationalising ML model release using Azure DevOps

For the longest time data science was often performed in silos, using large scale compute operating across isolated copies of production data. This process was not repeatable, explainable or scalable and often introduced business and security risk. With modern enterprises now adopting a DevOps engineering culture, no longer can machine learning development practise operate in isolation of the business or existing applications teams. This demo heavy session introduces the new Azure ML Services capabilities and how this can assist to bring the practice of data science into the age of modern DevOps.

Ananth Prakash

Ananth Prakash - Lead Architect - Data, AI & IoT @ Microsoft

Ananth has a successful track record in delivery of strategic projects, products and platforms in the Data & AI domain for 16 years across North America, Australia and Asia. Some of his roles include leading product engineering groups at Oracle, Managing Consultant at IBM and Senior Technical Architect - Data & AI at Infosys. As a Lead Architect at Microsoft, Ananth helps enterprise customers architect & deploy strategic solutions on Azure to accelerate their business transformation journey.

From A to AI: Operationalising Machine Learning in 1 day

Companies are recognising the importance of cross-functional expertise in bringing AI-driven products to market, supported by efficient tooling. Just like DevOps helped organisations accelerate their software delivery schedules, in this session you will learn how to build an end-to-end pipeline for continuous delivery of Machine Learning models. By automating ML ops with Amazon SageMaker and serverless workflows you will be able to build, deploy & monitor models at scale to maximise the business value to your organisation.

Julian Bright

Julian Bright - Machine Learning Specialist @ AWS

Julian has over 20 years’ experience building software and data science solutions in Australia and Europe from small start-ups to large multi-nationals. As a Machine Learning Specialist at AWS, Julian helps customers solve AI and ML problems by developing and deploying cloud native solutions at scale. Prior to AWS, Julian held senior positions as a Data Scientist and Architect as SEEK, where he applied machine learning and data engineering to develop recommendation and search algorithms to match candidates to hirers. Julian has won a number of hackathon events, is active in the Melbourne start-up community and has spoken at events including AWS Sydney Summit, Health 2.0, NDC and IJCAI.

Standardising the ML lifecycle with Databricks

The ML lifecycle is ever-evolving and it presents a number of challenges for data scientists and data engineers. Databricks believes there should be a better way to manage the ML lifecycle from preparing data to building models to deploying them to production. In this session, you will learn how to use Delta Lake and MLFlow to simplify ML workflows, improve model reproducibility and encourage efficient collaboration.

Jonathan Choi

Jonathan Choi - Solutions Architect @ Databricks

Jonathan is a Solutions Architect at Databricks. He focuses on helping customers solve their problems and realise the business value in data. Prior to joining Databricks, Jonathan has built big data analytics solutions for companies across different countries and industries, from banks and advertisers in Australia to retailer in the U.S and health insurer in China. He has also built and led the data & analytics team at a fintech start-up in Berlin.

Unleashing the Power of Analytics to Create a Better Bank for Our Customers

Dmitri Markman

Dmitri Markman - General Manager, Analytics, CX Division @ National Australia Bank

Dmitri has 20+ years financial services experience in various Credit Risk and Analytics roles spanning first line and group support functions. In 2019 he was appointed as General Manager, Analytics for Customer Experience Division at National Australia Bank, where he is responsible for development of specialised ‘deep’ customer insights that are used as inputs into pricing, proposition development, and marketing across all channels and segments. In this key strategic role, Dmitri is entrusted with delivering an analytics roadmap to empower NAB and its customers to achieve their goals by helping them with banking decisions. Prior to this role Dmitri was an integral part of the team that developed the market leading small business digital origination capabilities, Quickbiz, that utilises internal and external data to dramatically improve customer experience and significantly reduce efforts in assessing new lending requests. Known across the enterprise for his track record of consistent delivery, Dmitri’s current focus is on lifting analytics capability within the division and collaborating with Enterprise Data to ensure alignment on data architecture

Dmitri joined NAB in 2014 from ANZ where over the span of 11 years he held several senior roles across the group including Head Credit Risk, Small Business and Esanda; Head of Credit, Unsecured Consumer Lending; and being responsible for development of all decisioning models for Australia and Asian markets. Prior to ANZ, Dmitri spent 4 years at Ford Credit Asia Pacific being involved in retail credit management for 11 markets in the region.

Dmitri holds a Bachelor Degree in Commerce (Finance) from Melbourne University, as well as various professional qualifications gained throughout his career.

Dmitri is married and has a 9-year old son, Lucas. Outside of work Dmitri enjoys spending time with his family, going to the theatre and attending sporting events.

Human Infrastructure for Data Science and Analytics

Human Infrastructure is the most ignored, and most key component of any organisation’s analytics infrastructure initiative. Building a data literate infrastructure, with the correct knowledge, skills and incentives is key to ensuring that the value of analytics is realised and electronic analytics infrastructure does not go to waste. Time permitting, this presentation will also consider the changing role of human infrastructure in difficult times, such as we are experiencing now.

Eugene Dubossarsky

Eugene Dubossarsky - Chief Data Scientist @ AlphaZetta

Dr Eugene Dubossarsky is a leader in the analytics field in Australia, with over 20 years’ commercial data science experience. He is a the Managing Partner of the AlphaZetta Global Analytics Training Academy as well as AlphaZetta's Chief Data Scientist. Eugene has worked as a data scientist, software developer, entrepreneur, trainer, consultant, financial trader and speculative sports punter, applying his data science and analytics skills in all these fields. He is also the founder of the Australia–New Zealand Data Analytics Network, with over 19,000 members, and the head of the Data Science Sydney group (6,000+ members), and Big Data Analytics Sydney (7000+ members). Eugene is the Chief Scientist of reask, a global climate and catastrophe modelling company. He is regularly invited as a conference presenter, consultant and advisor, and appears in print and on television to discuss data science and analytics, and advisory board member for listed companies, advising on AI and Data Science. Eugene also applies data science in an entrepreneurial setting, to financial trading and online startups, and is the creator of ggraptR, an interactive visualisation package.

More topics & speaker bios will be announced as we get closer to the date of the EDSA Conference...

Sponsors

Deep Dive with Dataiku

Small Group Sessions

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI. Dataiku believes that those companies who succeed in deploying and scaling AI will do so by ingraining a culture of working with data throughout the enterprise instead of siloing it into a specific team or role. To make this a reality, Dataiku provides one simple UI for data wrangling, mining, visualization, machine learning, and deployment based on a collaborative and team-based user interface accessible to anyone on a data team, from data scientist to beginner analyst. At the Enterprise Data Science Architecture Conference, you will be able to participate in a small-group sessions on the practical deployment of machine learning solutions with Dataiku.

Are we the right audience for you? Become a sponsor. Email winning@edsaconf.io

Sponsors - Correct Audience

Reach the right audience. Our attendees work with architecture, data infrastructure, data security, artificial intelligence, data science, software engineering and everything in between. If this is the audience that you want to reach, then email us at winning@edsaconf.io


Our Venue is the Melbourne Marriott Hotel

Conference Venue

Our venue is the Melbourne Marriott Hotel. This luxury hotel provides unparalleled elegance and world-class service. It is conveniently located in the Melbourne CBD at the Corner of Exhibition and Lonsdale Streets. Conference delegates will enjoy morning tea, lunch, afternoon tea and evening canapes. We will be there on 27th March 2020. We also have a virtual option. See below.


Virtual Option

Your Choice: Face-To-Face or Virtual

This year's conference will be a hybrid of face-to-face and virtual attendance. Both attendees and speakers have the option of attending either virtually or face-to-face. This is our response to new company policies and any potential new government regulations. Some of the presenters will be presenting virtually. We are finalising the virtual platform. Ticket holders will be notified by email. If you have purchased a ticket for someone else, make sure that you assign that ticket to them and their email address.

Register

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See you at the Enterprise Data Science Architecture Conference!

When


Friday 27th March 2020

Where


Melbourne Marriott Hotel
Corner of Exhibition St. and Lonsdale St.
Melbourne VIC 3000
Australia