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  • Liked Simon Kaplan
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    Simon Kaplan - Lessons from Building a Data Platform for Smart Cities

    Simon Kaplan
    Simon Kaplan
    CEO
    [ui!] the urban institute
    schedule 4 months ago
    Sold Out!
    45 Mins
    Invited Talk
    Intermediate

    We've built a data platform for smart cities. This has been deployed in over a dozen cities, and we've learned a lot in the process, about:

    • why data ingestion from IoT networks can range from trivial to very painful, and how to cope;
    • how to architect the system to easily handle many different 'data domains';
    • getting the architecture to work well including making additions of new data sources as simple as we can;
    • approaches to analytics and visualisations that have been useful;
    • why end-user analytics and visualisations are critical;
    • how user permissions for smart city applications can be different to more 'normal' applications.
    • and lots more

    In the talk, I'll walk through the lessons learned and show off examples of the system in action.

    The goal is to use the platform as an exemplar of the design principles, this is not a sales pitch for the tool itself.

  • Liked Juliet Hougland
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    Juliet Hougland - How to Experiment Quickly

    Juliet Hougland
    Juliet Hougland
    Data Vagabond
    Bagged & Boosted
    schedule 5 months ago
    Sold Out!
    45 Mins
    Invited Talk
    Intermediate

    The ‘science’ in data science refers to the underlying philosophy that you don’t know what works for your business until you make changes and rigorously measure impact. Rapid experimentation is a fundamental characteristic of high functioning data science teams. They experiment with models, business processes, user interfaces, marketing strategies, and anything else they can get their hands on. In this talk I will discuss what data platform tooling and organizational designs support rapid experimentation in data science teams.

  • Liked Brendan Hosking
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    Brendan Hosking - Custom Continuous Deployment to Uncover the Secrets in the Genome

    Brendan Hosking
    Brendan Hosking
    Solutions Engineer
    CSIRO
    schedule 4 months ago
    Sold Out!
    30 Mins
    Talk
    Intermediate

    Reading the genome to search for the cause of a disease has improved the lives of many children enrolled in clinical trials. However, to convert research into clinical practice requires the ability to query large volumes of data and find the needle in the haystack efficiently. This is hampered by traditional server- and database-based approaches being too expensive and unable to scale with accumulating medical information.

    We hence developed a serverless approach to exchange human genomic information between organisations. The framework was architected to provide instantaneous analysis of non-local data on demand, with zero downtime costs and minimal running costs.

    We used Terraform to write the infrastructure, enabling rapid iteration and version control at the architecture level. In order to maintain governance over our infrastructure created in this way, we developed a custom Continuous Deployment service that built and securely maintained each project, providing visibility and security over the entire organisation’s cloud infrastructure.

  • Liked Mat Kelcey
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    Mat Kelcey - Practical Learning To Learn

    30 Mins
    Talk
    Advanced

    Gradient descent continues to be our main work horse for training neural networks. One recurring problem though is the large amount of data required. Meta learning frames the problem not as learning from a single large dataset, but learning how to learn from multiple related smaller datasets. In this talk we'll first discuss some key concepts around gradient descent; fine-tuning, transfer learning, joint training and catastrophic forgetting and compare them to how simple meta learning techniques can make optimisation feasible for much smaller datasets.

  • Liked Noon van der Silk
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    Noon van der Silk - How Much Data do you _really_ need for Deep Learning?

    Noon van der Silk
    Noon van der Silk
    Director
    Braneshop
    schedule 4 months ago
    Sold Out!
    30 Mins
    Talk
    Intermediate

    A common assumption is that we need significant amounts of data in order to do deep learning. Many companies wanting to adopt AI find themselves stuck in the “data gathering” phase and as a result delaying the use of AI to gain competitive advantage in their business. But how much data is enough? Can we get by with less?

    In this talk we will explore the impact on our results when we use different amounts of data to train a classification model. It is actually possible to get by with much less data than we might expect. We will discuss why this might be so, in which particular areas this applies, and how we can use these ideas to improve how we train, deploy and engage end-users in our models.

  • Liked Simon T. O'Callaghan
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    Simon T. O'Callaghan / Alistair Reid / Finn Lattimore - Engineering an Ethical AI System

    30 Mins
    Talk
    Intermediate

    To improve people’s well-being, we must improve the decisions made about them. Consequential decisions are increasingly being made by AI, like selecting who to recruit, who receives a home-loan or credit card, and how much someone pays for goods or services. AI systems have the potential to to make these decisions more accurately and at a far greater scale than humans. However, if AI decision-making is improperly designed it runs the risk of doing unintentional harm, especially to already disadvantaged members of society. Only by building AI systems that accurately estimate the real impact of possible outcomes on a variety of ethically relevant measures, rather than just accuracy or profit, can we ensure this powerful technology improves the lives of everyone.

    This talk focuses on the anatomy of these ethically-aware decision-making systems, and some design principles to help the data scientists, engineers and decision-makers collaborating to build them. We motivate the discussion with a high-level simulation of the "selection" problem where individuals are targeted, based on relevant features, for an opportunity or an intervention. We detail the necessary considerations and the potential pitfalls when engineering an ethically-aware automated solution, from initial conception through to causal analysis, deployment and on-going monitoring.

  • Liked Huon Wilson
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    Huon Wilson - Entity Resolution at Scale

    Huon Wilson
    Huon Wilson
    Sr. Software Engineer
    CSIRO's Data61
    schedule 4 months ago
    Sold Out!
    30 Mins
    Talk
    Intermediate

    Real world data is rarely clean: there are often corrupted and duplicate records, and even corrupted records that are duplicates! One step in data cleaning is entity resolution: connecting all of the duplicate records into the single underlying entity that they represent.

    This talk will describe how we approach entity resolution, and look at some of the challenges, solutions and lessons learnt when doing entity resolution on top of Apache Spark, and scaling it to process billions of records.

  • Liked Dana Ma
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    Dana Ma - Building Rome Every Day - Scaling ML Model Building Infrastructure

    Dana Ma
    Dana Ma
    Sr. Software Engineer
    Zendesk
    schedule 4 months ago
    Sold Out!
    30 Mins
    Case Study
    Intermediate

    "I want to reset my password". "I ordered the wrong size". "These are not the droids I was looking for". Every day, a support agent fields thousands of these queries. Multiply that by the thousands of agents a company might have, and the sheer vastness of data being generated becomes hard to imagine. How can we make sense of it all? It seems a formidable task, but we have a formidable weapon in our arsenalwe have machine learning.

    By combining deep learning, natural language processing and clustering techniques, we built a machine learning model that can take 100,000 tickets and efficiently cluster and summarise them into digestible topics. But that's only part of the challenge; we also had to scale it to build for 30,000 customers, in production, every day.

    In this talk I'll share the story of Content Cues - Zendesk's latest Machine Learning product. It's the story of how we leveraged the power of AWS Batch to scale a model building platform. Of how we tackled challenges such as measuring how well an unsupervised model performs when it's not even clear what "well" means. Of how our team combined our pool of skills across data engineering, data science and product management to deliver a pipeline capable of building a thousand models for the price of a cup of coffee.

  • Liked Lex Toumbourou
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    Lex Toumbourou - Emerging Best Practices for Machine Learning Engineering

    Lex Toumbourou
    Lex Toumbourou
    Sr. Consultant
    Thoughtworks
    schedule 4 months ago
    Sold Out!
    30 Mins
    Talk
    Intermediate

    In this talk, I'lll walk through some of the emerging best practices for Machine Learning engineering and contrast them to those of traditional software development. I will be covering topics including Product Management; Research and Development; Deployment; QA and Lifecycle Management of Machine Learning projects.

  • Liked Pantelis Elinas
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    Pantelis Elinas - Practical Geometric Deep Learning in Python

    30 Mins
    Talk
    Intermediate

    Geometric Deep Learning (GDL) is a fast developing machine learning specialisation that uses the network structure underlying the data to improve learning outcomes. GDL has been successfully applied to problems in various domains with network-structured data, such as social science, medicine, media, finance, etc.

    Inspired by the success of neural networks in domains such as computer vision and natural language processing, the core component driving GDL is the graph convolution operator. This operator is used as the building block for deep learning models applied to networks. This approach takes advantage of many algorithmic and computational developments from modern neural network research and practice – such as composability, optimisation, and end-to-end training – to improve predictive performance.

    However, there is a lack of tools for geometric deep learning targeting data scientists and machine learning practitioners.

    In response, CSIRO’s Data61 has developed StellarGraph, an open source Python library. StellarGraph implements a number of state-of-the-art methods for GDL with a clean and consistent API. Furthermore, StellarGraph is designed to make the application of GDL algorithms to network-structured data easy to integrate with existing machine learning workflows.

    In this talk, we will start with an overview of GDL and its real-world applications. Then we will introduce StellarGraph with a focus on its design philosophy, API and analytics workflow. Finally, we will demonstrate StellarGraph’s flexibility and ease-of-use for developing solutions targeting important applications such as product recommendation and social network moderation. Lastly, we will touch on the challenges of designing and implementing a library for a fast evolving machine learning field.

  • Liked Kevin Jung
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    Kevin Jung - Bitcoin Ransomware Detection with Scalable Graph Machine Learning

    Kevin Jung
    Kevin Jung
    Software Engineer
    CSIRO's Data61
    schedule 5 months ago
    Sold Out!
    30 Mins
    Talk
    Intermediate

    Ransomware is a type of malware that has become a major threat, rising to 600 million attacks per year, and this cyber-crime is very often facilitated via cryptocurrency. While ransomware relies on pseudonymity to send and receive payments that are difficult to trace, the fact that all transactions on the bitcoin blockchain are written publicly presents an opportunity to develop an analytics pipeline to detect such activities.

    Graph Machine Learning is a rapidly developing research area which combines entity attributes and network structure to improve machine learning outcomes. These techniques are becoming increasingly popular, often outperforming traditional approaches when the underlying data can be naturally represented as a graph.

    This talk will highlight two main outcomes: 1) how a graph machine learning pipeline is formulated to detect bitcoin addresses that are suspected to be associated with ransomware, and 2) how this algorithm is scaled out to process over 1 billion transactions using Apache Spark.

  • Liked Hercules Konstantopoulos
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    Hercules Konstantopoulos - Is Agile Data Science a thing now?

    30 Mins
    Talk
    Intermediate

    How come there’s no standard text on how to operate a Data Science team? At its current scale this is a young practice without a widely accepted mode of operation. Because so many practitioners are housed in technology shops, we tend to align our delivery cycles with developers… and hence with the Agile framework.

    I will argue that if a data team fits within Agile it is probably not performing data science but operational analytics—a separate and venerable practice, and a requisite for data science. To ‘do’ science we need a fair bit of leeway, although not a complete lack of boundaries. It’s a tricky balance.

    In this talk I will share my experience as a data scientist in a variety of circumstances: in foundational, service, and advisory roles. I will also bring some parallels from my past life in scientific research to discuss how I think data science should be performed at scale. And I will share my current Agile-ish process at Atlassian.

  • Liked Ananth Gundabattula
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    Ananth Gundabattula - Auto feature engineering - Rapid feature harvesting using DFS and data engineering techniques

    30 Mins
    Talk
    Intermediate

    As machine learning adoption permeates across many business models, so is the need to deliver models at a much faster rate. Feature engineering arguably is one of the core foundations of model development cycle. While approaches like deep learning tend to take a different approach to feature engineering, it might not be exaggerating to say that feature engineering is the core construct which can make or break a classical machine learning model. Automating feature engineering would immensely shorten the time to market classical machine learning models.

    Deep Feature Synthesis (DFS) is an algorithm that is implemented in the FeatureTools python package. DFS helps in rapid harvesting of new features by taking a stacking approach on top of a relational data model. DFS also has first class support for time dimensions as a fundamental construct. Some of these factors make the feature tools package a compelling tool/library for data practitioners. However the base algorithm itself can be enriched in multiple ways to make it truly appealing for many other use cases. This session will present a high level summary of DFS algorithmic constructs followed by enhancements that can be done on featuretools library to enable it for many other use cases

  • Liked Itzik Feldman
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    Itzik Feldman - Scaling Analytics as your Company Grows

    30 Mins
    Case Study
    Intermediate

    Analysts are a bottleneck, they can't answer all the questions that are coming from their business users

    Business users are heavily dependant on their analysts, as a result, when the analyst is not available they either wait for a long time or act according to their gut feeling

    Analysts are feeling frustrated because they are underutilized. Most of their tasks require simple querying and dashboarding while they want to do data science

    Does any of these sounds familiar? then you should join this talk.

    Back in the days, when Atlassian had only a few hundreds of employees, we used to hire analysts to help the business teams with insights generation. As we grew, we hired more of them, but we came across these problems and we realized that this approach is not scalable.

    During this talk, I will show how we solved these problems. We will see the Atlassian journey towards self serve analytics and data-driven culture.

  • Liked Agustinus Nalwan
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    Agustinus Nalwan - The Magic of Unsupervised Learning: Teaching an AI to Understand Our World

    30 Mins
    Talk
    Intermediate

    No doubt that AI is the new kids on the block. From as simple as classifying hot-dog vs not hot-dog, recognising flower species and going towards science fiction realm in generating fake videos.

    This talk will cover the problem with supervised learning which is what most of current AI technologies are based on and what is the promising trend towards the future of AI with unsupervised learning. As a use case, we will cover how image generation techniques such as Variational Auto-encoder extract knowledge from images in an unsupervised manner.

  • Liked Antoine Desmet
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    Antoine Desmet - From Zero to Tensorflow: Building an Analytics Dept.

    Antoine Desmet
    Antoine Desmet
    Analytics Manager
    Komatsu
    schedule 6 months ago
    Sold Out!
    30 Mins
    Case Study
    Intermediate

    Day 1: one engineer vs. a heap of time-series data on a 1990s-era database
    Four years on, there's 8 of us, we run TensorFlow analytics on a Hadoop cluster to detect subtle signs of a potential breakdown on earthmoving equipment. We've prevented million-dollar component failures, and reduced a lot of "parasite" stoppages.

    This talk details the strategy and lessons learned from building an analytics department from scratch, in particular:

    • Many analytics depts. were created as a "Flavour of the month". How do you approach this perception, survive and go beyond?
    • Choosing the right projects to create a credible and sellable offering as quickly as possible to build your reputation.
    • Expectation management, and choosing projects: Dealing with those who think "it won't work", and those who think you can solve all problems,
    • Growing from a "start-up in a large company" to a more mature group. Change management, scaling, velocity, etc.
    • Approach to R&D and launching new projects, dealing with the "shiny toys"
  • Liked Anthony I Joseph
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    Anthony I Joseph - Techniques Used to Analyse the Affordability, Commutability and Demographics of Real Estate in School Catchment Areas

    30 Mins
    Case Study
    Intermediate
    School catchments, otherwise known as priority placement areas or intake zones are a zone where children are entitled to enrol in a public school. Recent media coverage has drawn attention to the increased demands for residential real estate within high performing school catchments. While school catchment areas remain a controversial and influential factor in determining student enrolments, the impacts of school catchment areas on its local community is only recently being studied. This presentation will describe some of the analytical techniques used to analyse school catchment areas, as expressed as geospatial concepts as well as some of the results obtained from analysing school catchments across Australia. This analysis involved combining different spatial and non-spatial datasets across various jurisdictions. These geospatial analytical techniques were used to draw insights on the affordability, commutability and demographic changes that school catchments may have on urban environments. Urban environments and school catchments across Australia have been analysed. The insights obtained from this analysis could be used to influence property investment decisions for individuals, and policy decisions on:
    - public housing locations,
    - public transport infrastructure,
    - school catchment area designs, and
    - future school locations
    for government agencies.
    This presentation will cover analytical techniques including geoprocessing, vector data analysis and isochrones. The slides for this presentation are available at:
  • Liked Paola Oliva-Altamirano
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    Paola Oliva-Altamirano - CLASSIEfier: Using Machine Learning to Paint a Picture of Social Sector Trends

    30 Mins
    Case Study
    Intermediate

    Tracking the flow of funding and other support to social sector organisations in Australia has historically been difficult because of inconsistencies in categorisation, or the absence of categorisation entirely. Our Community (Melbourne based social enterprise) developed CLASSIE to serve as a universal classification system for Australian social sector initiatives and entities. We are now developing a Machine learning algorithm to reduce or remove the need for manual (human) classification. Once released, CLASSIEfier will allow us to classify historical records on behalf of grantmakers and other social sector supporters, and reduce the need for human intervention in classification of current and future records. In a long term will allow us to answer fundamental questions such as: Where is the money going? Are we helping the areas in most need?

    I will present the project scope and development of CLASSIEfier, highlighting my experiences using Machine Learning in the social sector. I will also list the difficulties of working with text and sensitive data, and the methodologies to identify and mitigate algorithmic biases.

  • Liked Brad Urani
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    Brad Urani - My 5 Biggest Database Blunders

    Brad Urani
    Brad Urani
    Staff Engineer
    Procore
    schedule 7 months ago
    Sold Out!
    30 Mins
    Case Study
    Intermediate

    We've all made mistakes. With databases, mistakes are particularly costly because they lead to performance bottlenecks, deployment disasters, lost data and intractable technical debt. Join us and learn from my mistakes. You'll hear harrowing tales of schema design blunders that were never rectified, and where recursive SQL is a path to a dark place. You'll learn why databases make lousy queues, and what to use instead. You'll learn the perils of table locking and botched migrations that can cause downtime and data loss. You'll laugh at my futile attempt to tune queries after choosing the wrong database, and why certain workloads work well on some databases, but not on others. Whether you're new to database engineering, or have made all the same errors, hearing about my missteps will help you avoid mistakes in your own data engineering challenges.

  • Liked Gareth Seneque
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    Gareth Seneque - Search at Scale: Using Machine Learning to Automate Content Metadata

    30 Mins
    Case Study
    Intermediate

    For media organisations, reach is everything. Getting eyeballs and ears in front of content is their raison d'être.

    Search plays a critical role in connecting audiences with t-1 content (yesterday's news, last week's podcast). However, with audience expectations conditioned by Google and others, it is challenging to deliver robust, scalable search that people actually want to use.

    The relevance of your results is everything, and to produce relevant results you need good metadata for every object in your search index. With hundreds of thousands of content objects and an audience of millions, the ABC has unique challenges in this regard.

    This talk will explore the ABC's use of Machine Learning (ML) to automatically generate meaningful metadata for pieces of content (audio/video/text), including AWS MLaaS for full transcripts of audio podcasts and a platform developed in-house for NLP tasks such as entity recognition and automated document summarisation, and image-related tasks such as segmentation and tagging.

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