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The talk will elaborate on building an end-to-end AI deployable pipeline for various industries (Health, Smart Spaces, Autonomous Driving, Industry 4.0, Conversational AI, Retail, Telecommunication, Energy) that can be brought to live-production in optimal cost and efficient timeframe for businesses.

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.

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.

Advancements in technology, data and analytics are enabling predictive modeling and forecasting to help improve health outcomes. Similar to how weather forecasting proves to be very useful to prepare for uncongenial weather conditions, disease forecasting can be used to mobilize, allocate and manage resources as well as inform the general population of the impending onset of a seasonal disease, such as the flu, in a timely manner. In the coming years, disease forecasting is positioned to become more accurate and meaningful, with richer and better sources of data from wearable devices, improved ambient computing, more readily available gene data etc. Forecasting methodologies and data analytics models are also advancing, with ongoing innovations around recurrent neural networks, compared to traditional statistical techniques. All of this is powered by the awareness of populations for more accurate information on seasonal and chronic diseases.

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.

SOTA computer vision algorithms play a large role in addressing business problems across verticals such as Manufacturing, Retail, Insurance, etc. In this talk by Praveen Jesudhas, learn how advancements related to active learning, synthetic data generation, few-shot learning, etc., can be consolidated efficiently to enable rapid evaluation and deployment of Computer vision-based solutions.

An intelligent combination of Data-Centric and Model-Centric approaches that not just focus on procuring the relevant data and on algorithmic tools and techniques but also focuses on generating information by emphasising on business relevance, responsible AI, sustainable AI and explainable AI, which collectively help to solve the business problem.