The third edition of the Machine Learning Developers Summit (MLDS 2021), one of the biggest gatherings of machine learning developers in India, is scheduled to be held virtually from February 11-13 in Bengaluru.
With more than 1,500 machine learning developers, 60 speakers from around 200 organisations, the conference corrals India’s leading Machine Learning innovators and practitioners to share their ideas about machine learning tools, advanced development and more.
Here, we have put together a list of nine keynotes, tech talks and workshops you can look forward to at MLDS 2021.
1| The Interplay between Quantum Theory and Artificial Intelligence
When: 11 Feb, 13:30 – 14:10
By: Anish Agarwal, Director – Data & Analytics, India at NatWest Group
About: Quantum theory is the theoretical framework of modern physics. After more than five decades since its inception, quantum theory bonded with computer science, and quantum computation was born. One of the fertile areas for quantum computing is Artificial Intelligence that hinges on processing large volumes of complex datasets. The field also anticipates the necessity to develop algorithms to allow for better learning, reasoning and understanding of AI.
2| Building Smart and resilient operations by leveraging data-driven operating model to sustain business growth
When: 12 Feb, 09:45 – 10:25
By: Alok Kumar, Head of Data & Analytics at DBS Bank Singapore
About: The banking industry is bullish on digitalisation and is churning out unprecedented levels of data. The million-dollar question is how the banks could translate these huge datasets into meaningful insights to further their agenda in areas such as customer servicing, productivity to drive operational efficiency. Kumar will share his perspectives based on his experience in harvesting the data for crafting smart operations utilising advanced analytics for the data-driven operating model.
3| Elementary, my dear ML
When: 11 Feb, 12:00 – 12:40
By: Sayanti Bhattacharya, Senior Manager at Ugam, and Ashwin Pai, Manager at Ugam, a Merkle company
4| Building a product using ML and AI
When: 12 Feb, 16:00 – 16:40
By: Bragadeesh S, Head Of Analytics at Daimler AG
About: Building a product calls for expertise in creating a framework and then applying the model to produce results and later visualising the outcome. To build a web application or on a Kotlin/ Flutter application requires the skill of a full stack developer. The session will dilate on the specific skills needed to develop this product.
5| Deploy Machine Learning Models on Streamlit – Web-Based Application like a pro
When: 13 Feb, 11:00 – 12:55
By: Shirish Gupta, Team Lead/Data Scientist-1 at Novartis
About: Streamlit is a popular open-source app framework for ML and Data Science to generate impressive dashboards as well as web apps in a short span of time. The workshop will teach attendees how to deploy a machine learning model on streamlit and explore functionalities, such as uploading datasets, doing Exploratory Data Analysis (EDA), running a machine learning algorithm, and visualising the outcome. The attendees will also learn how to “beautify” the apps by adding buttons, checkboxes, and more. Prerequisites for this workshop include- Python (Basic to Intermediate), Streamlit installed in the system.
6| Fintech as a customer and recruiter for DS
When: 12 Feb, 16:45 – 17:25
By: Sachin Garg, Head of Data Science at PayU
About: Application of DS in fintech and PayU, customer acquisition and retention, fraud detection, risk analysis etc. Industry status quo, career opportunities, skills companies such as PayU look for in a candidate and also careers in DS at PayU.
7| ML Fairness & How to mitigate biases using TensorFlow
When: 11 Feb, 14:15 – 15:55
By: Luca Massaron, Senior Data Scientist, Kaggle Master; Google Developer Expert on ML
8| TensorFlow2 Crash Course / Essentials
When: 13 Feb, 16:00 – 17:55
By: Dipanjan Sarkar, Data Science Lead at Applied Materials. Google Developer Expert – ML
About: The workshop will cover: Tensor Layers, Tensor Ops; Model types – sequential, functional, custom; Build custom layers; Activation functions and optimisers; Wide, deep and residual models; Comparison of DNN models; Brief intro on CNN \ RNN.
9| Effective EDA & data visualisation
When: 12 Feb, 09:00 – 09:40
By: Martin Henze, Data Scientist at Edison Software | Kaggle Grandmaster | PhD Astrophysicist
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A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.