AI is one of the most dynamic technical fields. It becomes demanding for data science/AI professionals to keep up with the pace of the technology. But more than keeping up with learning demands, it becomes hard for data scientists to differentiate themselves as access to learning resources is highly democratized in the last 5 years. In today’s market, the place where one studies or academic credentials are becoming less and less important. So how do data scientists prove their skills, showcase their quality and differentiate themselves, all while having a ton of fun? This session answers exactly that question. Join Abhijeet Katte, with:
1. Krishna Rastogi, Chief Technology Officer, MachineHack
2. Rahul Pednekar, Vice President – Advanced Analytics, SwissRe
3. Salil Gautam, Machine Learning Engineer, AirBnB
Audi 2
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.
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.
In an Omni channel marketing setup, Customer experience various ads via multiple channels before the final conversion. In a last-touch model one may miss the insights regarding the contribution of marketing channels that appeared in the customer journey prior to the conversion. A best-in-class Marketing Channel Attribution Model was developed using Markov’s chain to arrive at contribution of each channel and to identify cost optimum channel to be used for awareness and conversions.
This workshop will be a curtain raiser to the approach and best practices on operationalising AI/ML solutions from research to production .Borrowing best practices synergies from software development automation .We will walk through a Deep learning use case ,creating pipelines to operationalize the workflow thus created in an interactive workshop session .The attendees will get a first-hand understanding of creating a AI workflow , automating the pipeline and different approaches to training and a glimpse of the road ahead.
Prerequisites for the Workshop:
Docker Desktop (Preinstalled by attendees)
16 GB RAM local system
In this technical talk, we introduce a method for improving topic modelling by incorporating zero-shot classification with generative AI. Our approach considers the top words of a topic and the contextual meanings of the documents themselves to identify relevant documents within the topic. The use of this technique can have significant business impact, including the ability to more thoroughly analyse a topic and gain actionable insights relevant to each top-word.
The success of Transformer models in NLP led people to explore its application in computer vision tasks like image classification, object detection, segmentation, generative modelling and video processing. The workshop will discuss various Transformer based architectures used in Computer Vision and in particular, cover end-to-end use cases for Image Classification using various Transformer architectures.
1. Across industries, organisations realise the strategic importance of data to drive growth. There is a sense of urgency to invest in systems that can generate timely and relevant insights for gaining an edge in the marketplace
2. In today’s digital world, data generation and its use cases have grown exponentially. With different forms of data, the applications for machine learning are also ever-increasing in order to make data sensible and actionable
3. Despite challenges, there are multiple opportunists across industry verticals to further accelerate the use of machine learning and automation that, in turn enhances the RoI for data & analytics functions.
