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One of the most important skills for those who work with Machine Learning (ML) is to know which method is the right choice for a given problem. Some choices are trivial however, there is usually a myriad of algorithms that can be used. In addition, productionising a trained model also comes into the production-level pipeline. Modern end-to-end (E2E) learning refers to the entire pipeline of a production-level ML application. This usually consists of infrastructure architecture, data processing, statistical modelling, ML frameworks, distributed computing, deployment as well as user experience (UX). In this talk, we are explaining the journey of a Data Scientist with their required skills/knowledge. What does a full stack hands-on mean? How can Data Scientists or a ML Engineers improve their skill sets to make themselves a contributor in the modern E2E ML product development? These are some of the most important questions data scientists have. We are going to make a clear picture of this journey by demonstrating a real-life customer pattern recognition use case.,

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Preserving privacy when processing data from multiple sources with machine learning is always a challenge. Organizations may want to perform collaborative data analytics while guaranteeing the privacy of their individual datasets. Combining multiple data sources to support a better algorithmic outcome improves accuracy of prediction, but it may come at cost of confidentiality, if sensitive information is not accurately protected.
Azure Confidential Computing adds new data security capabilities to the cloud and specifically to machine learning processing. By using trusted execution environments (TEEs) to protect your data while in use, with confidential computing, you can use machine learning algorithms across different organizations to better train models, without revealing the processed data.
This session presents the benefits of Azure Confidential Computing in an ML scenario, where two separate health institutes collaborate on data analysis and prediction using Azure Machine Learning, and still mask any sensitive information to protect the privacy of their patients.
A session about protecting data in the cloud with confidential computing.
The use case is for organisations to share data with full confidentiality protection.

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I will explain how the modern digital company is aproaching digitalization, will give examples and tips how to achive fast growth and retain customers. I will then demo a brain storming session with real time planning interactive board on how to lunch digital product and calculate budget and model process of customers uptake.

Speaker Profile

Nikola is the SVP, Chief Data Scientist at PT Smartfren Telecom. He leads data analytics and data science projects from strategy development, execution to delivery with regard to the collection, manipulation and AI based analysis of data for various business functions and departments including customer experience, customer on-boarding, marketing, sales, network operations etc.

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The talk introduces how AI and big data analysis can be used in the aged care sector. We have always been committed to using AI to help improve the welfare system and the standard of living of residents - particularly senior citizens. We invented CaptureLive, a smart care solution for the elderly with innovative patented fall prevention and detection system driven by ML&AI, for families of elderly people who can monitor seniors' status and send alerts to carers when a fall is predicted to occur.

Speaker Profile

Taicheng is the CTO of AIBUILD PTY LTD, an innovative Australian startup company that develop solutions using state-of-the-art technology from AI, VR to IoT with Australian Innovation Patent granted.

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The physical infrastructure underpinning large scale machine learning workloads is moving from traditional architectures to innovative new designs in the data centre. These new, and often radical, physical designs reduce energy consumption and increase density, which allows machine learning workloads to scale at lower cost and with little or no environmental impact.

Speaker Profile

Peter is a project leader and systems architect who specialises in cloud computing, machine learning and software engineering. He's particularly passionate about building scalable and robust technology with high usability. Peter began his career in financial services where he designed and developed institutional banking systems for major Australian and international banks including Westpac, the Commonweath Bank, NAB, HSBC, and the Bank of Tokyo. Peter later moved into the e-research sector where he initiated and led big-data and cloud computing projects at the Tasmanian Partnership for Advanced Computing (TPAC), the Integrated Marine Observing System (IMOS), and the Australian Ocean Data Network (AODN). Peter has worked in the startup space where he designed machine learning systems for fin-tech and other technology businesses including Biteable. Peter holds a PhD in artificial intelligence along with a Masters degree in accounting and a Bachelors degree in computer systems engineering. Peter lives in Hobart. He likes dogs, although he doesn't have one, and is warming to cats.

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Deep learning is driving advances in artificial intelligence that are changing our world. Computer vision gives machines those eyes to detect, track, classify and analyse objects while feeding with images and videos.
In this presentation, we'll talk about some ground breaking applications implemented worldwide using computer vision and have a small demo on how to detect a face in a video.

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Speaker Profile

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Computer vision applications are expected to grow at an estimated market size of $28bn by 2030. Many companies have started to realize the potential benefit AI can bring to their organization.
In this session, we will have a brief introduction to the applications of Visual inspection and other use cases in video analytics and action recognition.

Speaker Profile

Gomathy shankar is a Senior Data scientist in IBM, Singapore. He has worked on multiple AI/ML products, working closely with the clients and making them successful in their digitization journey.

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HyperAutomation will become the "Next Normal" for addressing all the "Digitization" and "Digital Automation" need where organizations are shifting to consume more impactful integrated HyperAutomation technologies to have an array of tools such as RPA, AI, ML, Analytics, Process Mining, iBPMS and associated skills to situationally apply as needed. It’s important for organizations, business and IT stakeholders to understand how "HyperAutomation" suite products act as powerful catalyst for product standardization, product differentiation and paving the way towards becoming "Digital First Organization" which is the highly pervasive to sustain, grow and prevail in the current Digital Era. Also, all such “Digital First” organizations will have to adopt Digital product management to build scalable, responsive, and resilient products that can sustain and prevail in all socio-economic conditions.

Speaker Profile

Raghav Nyapati is the Senior Director – Product Head of Digital Automation (RPA, AI, and ML) at ASG Technologies. Founder and Creator (Head of Automation) of Robotics Automation and Cognitive Automation Product (ASG-Zenith) which provides best-in class HyperAutomation platform for customers. He is creator of “Digital Product Life Cycle (DPLC)” framework and approach that can be used to build new “HyperAutomation” products, scale and grow existing digital products which has been adopted in various customers and industries such as Banking, Insurance, Healthcare, Retail across various the globe.
Raghav is recognized as an Industry Ambassador and Digital Automation expert in Robotic Process Automation, Cognitive Automation (complementing AI skills such as NLP, CV with RPA) providing mentoring, coaching, and evangelizing digital automation products to various premier B Schools, Product Schools and Engineering institutes across the globe.

Raghav has authored, published, and presented many articles, journals, podcasts on HyperAutomation, Robotics, AI, and Cognitive Automation in various platforms such as Medium, CIO Magazine, TechTarget publication etc. Raghav has filed Patent on “Unified Digital Automation Platform- RPA + BPM” (9804US) which is registered in US Patents Office in 2021.
Raghav is a Keynote Speaker and Industry Delegate presenting in various Webinars, conferences, summits, and educational forums/institutes on HyperAutomation products such as RPA, AI, and ML.

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Speaker Profile

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Speaker Profile