Google has been widely recognized as the industry leader in AI/ML. Apart from building Google’s own planet-scale consumer applications like Gmail, Photots etc. which are powered by AI/ML, Google has also been a strong supporter of and contributor to the open source community. Open source is indeed at the heart of Google—it’s what we’re built on and at the core of everything we do. From sharing datasets to creating platforms like Tensorflow for building AI/ML models and container orchestration systems like Kubernetes that help scale ML, Google’s contribution to the open source AI/ML community has been immense. Join this session to learn more about Google’s AI/ML contributions to the open source community, that you as ML Developers can take advantage of, to innovate, build and scale, just like we at Google do.
Day 1
Rajat tells the story off google’s journey into an AI-first world! The story talks about chance interaction, curiosity about the human mind and above all the googleyness spirit that helped Google create differentiated product offerings by being amongst the very first to leverage AI and ML in their products. He also talks about how you can leverage Google’s learning in your own organisation to re-create the same magic!
Data entered into QuickBooks has real financial implications – a single data entry mistake can be expensive for the business requiring significant effort in undoing the impact. Customer research highlighted the following top concerns: Error in entering invoice data, Error in paying employees, Fat finger a bill payment to a new vendor..
We needed a system which will flag errors on key fields while entering transaction data, and nudge the user towards the correct range. AI solutions are now norm for delivering smart product features to enhance customer experience but most solutions are focused on tailored use-cases that need resources from ideation to deployment and subsequent integration, every time we adopt a new use case.
Given the scope of multiple use-cases across data input fields in Intuit products, the error-detection system had to be extensible so that it allows developers to build tailored AI models for specific error detection by providing information about the fields and data sources only.
FEDS employs novel methodologies for error detection based on Empirical Bayesian Test, Likelihood Ratio Test and Deep Learning approaches. It decouples the efforts of Data Scientists from the underlying implementation layer, which facilitates the product developers to easily adopt across multiple use cases.
According to a study by Mckinsey, 71% consumers expect personalisation and 76% get frustrated when they don’t find it. As we see a surge in digital behaviour, personalisation has become a necessity for most businesses. However, while most people lay stronger emphasis on the recommendation engines powering personalisation, design and engineering play an equally critical role in delivering maximum value through personalisation, along with data science.
In this talk, I will cover the use case of storing and analyzing images of Yoga Poses with BigQuery and creating a classification model using BigQuery ML to label the postures using only SQL and no other form of code.
In this talk, we will learn Google’s way of putting thousands of models in production and see how our MLOps framework can help you achieve more clarity and faster models in production. We also explore Google’s Vertex AI platform, which is being built to handle MLOps at google’s scale. Finally, the talk will introduce the Google Cloud MLOps ecosystem and products like Vertex AutoML, workbench, Pipelines, Feature Store, Explainable AI, and Model Monitoring.
A talk on how to deal with data imbalance in machine learning/deep learning classification problems. We will discuss classical machine learning techniques of Oversampling, Undersamping, hybrid techniques, and cost-sensitive learning, as well as some advanced deep learning techniques like Dynamic sampling using PyTorch. If time permits, we will discuss how data imbalance affects the calibration of models.
Businesses increasingly focus on brand loyalty and customer engagement as they move towards more customer-centric strategies. Besides end consumers, sellers also form a key customer base for eCommerce marketplaces. Bad customer experience can lead to negative brand perception and drive down NPS (Net Promoter Score). This study was to help an eCommerce Marketplace scientifically identify key experience areas that were leading to a drastic decline in Seller NPS scores across regions over eight months. This would help businesses focus on the most important levers, prioritize budgets and improve NPS. In this study, we modelled customer survey data on brand perceptions and experiences, with historical transactional data and company policy information, to determine drivers of NPS. It was important to understand the inter-relations among various aspects of customer data, such as brand perceptions, issue resolution, customer service and macro factors, to provide holistic and actionable recommendations. We used a path model approach with a Bayesian network to determine causality between the key factors driving NPS and the interrelation among each factor. Structural Equation Modeling followed this to quantify the relative impact of each factor on NPS. The study found that sudden policy changes without effective communication, poor customer service and lack of protection for sellers from false buyer claims led to a poor selling experience. Sellers perceived the site as unfair to them and unsafe to sell. These insights were actioned upon by the company in an organization-wide cross-team initiative which helped address the seller’s dissatisfaction and eventually brought NPS back on an upward path.
Semiconductor and data storage are highly complicated and capital-intensive manufacturing. It often requires Industry 4.0 solutions to target key business metrics such as reducing manufacturing cost, improving capital efficiency, reduce time-to-market to develop new products. The solutions span around AI/ML, Digital Twin, and Operations Research which originates as first-of-a-kind with an intent to take it scale. As India commits to catalyzing the semiconductor ecosystem in the country and many companies invested, join me in learning the challenges or gaps in skills and techniques on complex use cases in Advanced Analytics to propel the vision forward.
Computer vision AI systems trained on medical images such as X-rays, CT scans etc. can be used to identify, classify and locate specific health conditions, like severity of knee-joint-space, location of vertebrae slip etc., with increased speed and accuracy. Leveraging medical imaging AI tools can provide clinical reviewers with images annotated with relevant clinical parameters, which can aid decision-making across the utilization management value chain, driving improved operational efficiency, accuracy and consistency in decision-making and delivery of health care.
