Amazon AI Conclave




Customer Stories

Curated Content

for AI/ML Practitioners

 21 December, 2022

 10:00 AM – 5:00 PM IST | 2:30 PM - 9:30 PM AEST

Empowering businesses to make real impact with AI & ML

Watch the sessions from 6th edition of Amazon AI Conclave on-demand. The AI Conclave aims to help builders and businesses make smart, customer-centric, scalable solutions in the cloud and on the edge using Amazon’s broadest and deepest set of artificial intelligence and machine learning services (AI & ML).

The event features keynote sessions presented by industry leaders, deep-dive technical sessions and customer showcases of advanced AI & ML solutions.

Accelerate innovation, scale effortlessly, and create real impact for your business, with AI & ML services on AWS.

Who should attend?

Data Scientists, Developers and AI/ML Practitioners

 Get hands-on and step-by-step architectural and deployment best practices

 Building computer vision ML applications on AWS

 Explore ML Ops on AWS

 Earn a certificate of attendance

 Connect 1:1 with AWS experts for insights on building with AWS and cloud computing

Data CXOs and Business Leaders

 Learn how AI is change the way we do business today with the ‘AI state of the union’ keynote

 Hear how companies around the world and across all industries are leveraging AWS AI and ML

 Explore ways to boost revenue by creating new products with AI and ML

 Hear from AWS AI and ML experts and Industry leaders


  • Join this session to hear Vikram Anbazhagan, Director & General Manager, Amazon AI, Amazon Web Services, as he discusses the latest AWS innovations that can help you transform your organization's DNA, to gain meaningful and actionable insights for your business.

    In this technical keynote, he discusses the key components of a future-proof artificial intelligence and machine learning (AI & ML) strategy, and how you can empower your organization to drive the next wave of modern invention on AWS.

    Speaker: Vikram Anbazhagan, Director of Product Management, Amazon AI, Amazon Web Services

  • Artificial intelligence (AI) is a key enabler of digital transformation; and businesses must prepare for an AI-led future to solve real-world challenges.

    In this session, learn how to democratize and build AI for your organization at critical performance and impact on the Intel technologies powered Amazon Elastic Compute Cloud (Amazon EC2) platform. Reimagine the future with disruptive, fast, and efficient AI.

    Speaker: Akanksha Balani,  Global Alliance - AWS @ Intel

  • The machine learning (ML) journey requires continuous experimentation and rapid prototyping to be successful. In order to create highly accurate models, data scientists have to first experiment with feature engineering, model selection, and optimization techniques. These processes are traditionally time consuming and expensive.

    In this session, learn how low-code tools, including Amazon SageMaker Data Wrangler, Amazon SageMaker Autopilot, and Amazon SageMaker JumpStart make it easier to experiment faster and bring highly accurate models to production more quickly and efficiently.

    Speaker: Guru Bala, Head of Specialist Solutions Architect, AWS India

    • AI Developer track
    • Training ML models at scale with Amazon SageMaker

      Training machine learning (ML) models at scale often requires significant infrastructure investment.

      In this session, learn how Amazon SageMaker Model Training activates training and tuning of large-scale ML models with high performance, and without the need to manage infrastructure. Explore how Amazon SageMaker Model Training makes it easy to checkpoint models during training and automatically restarts the job on certain types of failures. Additionally, hear about how Amazon SageMaker Model Training offers tools for activating faster distributed training with libraries for data parallelism and model parallelism, model tuning to accelerate hyperparameter optimization, and more.

      Speaker: Vatsal Shah, Senior Solutions Architect, AWS India

      Accelerating your team with the next generation of Amazon SageMaker notebooks

      In this session, discover how to use quick-start, collaborative Amazon SageMaker Studio notebooks to increase developer productivity and support collaboration across all steps in your ML development.

      We showcase the newest built-in capabilities of SageMaker Studio notebooks during this deep-dive session, and discuss how Jupyter Notebook can reach their full potential with Amazon SageMaker.

      Speaker: Rahul Shringarpure, Principal Startup Solutions Architect, AWS India

      End-to-end data preparation and machine learning with Amazon SageMaker Studio

      In this session, you will learn through live demonstrations how you can build end-to-end data preparation and machine learning (ML) workflows using Amazon SageMaker Studio. First, we'll show you how you can use Amazon SageMaker Studio to access data in a data lake managed with AWS Lake Formation for fine-grained data access control.

      Next, you'll learn how you can use Amazon SageMaker Studio to prepare data for ML at scale using Apache Spark on Amazon EMR and AWS Glue interactive sessions. Finally, you'll learn how you can use the prepared data to train and deploy ML models, and easily scale your interactive notebook based workflow to an automated scheduled job.

      Speaker: Dhiraj Thakur, Senior Solutions Architect, AWS India

      Implementing MLOps practices using Amazon SageMaker

      Machine learning operations (MLOps) tools help you automate, standardize, and govern processes across the ML lifecycle to productionize ML models faster and maintain model quality in production. Amazon SageMaker provides an enterprise level platform with a breadth of MLOps tools to train, test, troubleshoot, deploy, and govern ML models at scale

      In this session, learn how to quickly setup and standardize data science environments as well as dive deep into Amazon SageMaker MLOps features, including Amazon SageMaker Feature Store, Amazon SageMaker Pipelines, Amazon SageMaker Projects, Amazon SageMaker Experiments, Amazon SageMaker Model Registry, and Amazon SageMaker Model Monitor. Find out how you can increase efficiency and improve the quality of your ML workflows.

      Speaker: Gaurav Singh, Solutions Architect, AWS India

    • AI Engineers and Scientist track
    • Building more inclusive and responsible AI

      Artificial intelligence (AI) and machine learning aid society in tackling some of its most challenging problems, and help businesses improve customer experience and spur innovation. With the growing use and scale of AI comes the recognition that it should be used responsibly, respecting human rights, human dignity, and fundamental values such as equity, privacy, and fairness.

      In this session, hear about the challenges and issues faced by organizations that are working to put responsible AI principles AI theories into practice.

      Speaker: Rahul Sureka, Enterprise Solutions Architect, AWS India

      Finding accurate answers quickly, with ML-powered intelligent search

      Data fuels innovation, and innovation helps organizations stay ahead of the curve. Finding the right answers accurately and quickly is critical to solving business problems, improving employee productivity, and ensuring customer success for businesses in any industry.

      Attend this session to learn about Amazon Kendra, an intelligent search service powered by machine learning. Learn how you can bring intelligent search to where your employees and customers are working, while transforming the way they can get accurate answers to their questions quickly, from content across the enterprise. Hear about how AWS customers were able to create a fully functional intelligent search application for better employee productivity and exceptional customer experiences with low total cost of ownership.

      Speaker: Vijai Gandikota, Senior Product Manager, AWS Kendra, Amazon Web Services

      Improving uptime with predictive maintenance for industrial equipment

      Predictive maintenance is an effective way to avoid industrial machinery failures and expensive downtime, by proactively monitoring the condition of your equipment. It ensures that you are alerted to any anomalies before equipment failures occur. However, this technology has been difficult to implement.

      In this session, learn how industrial customers are activating predictive maintenance and avoiding downtime with AI services from AWS such as Amazon Monitron Lookout for equipment, which requires no machine learning experience.

      Speaker: Sudhanshu Hate, Principal AIML Specialist Solutions Architect, AWS India

      Expert talk on natural language processing

      In this session, we discuss a question-answer based approach for medical entity relationship extraction using sequence-to-sequence transformers. Relationship extraction from different kinds of documents across business domains can become non-trivial due to complex linguistic relations like distance between entities, overlapping entities within a relationship span, and of prohibitive annotation requirements. We present one of our solutions that addresses these issues and produces state-of-the-art results for extracting relation between adverse drug events (ADE) and suspected drug, from various unstructured data sources including clinical study reports, patient health records, and social media posts.

      We also talk about a comparative study of post-hoc explanation methods for human understandable text classification. Transformer-based models have been successfully applied to many real-world text classification problems with state-of-the-art performance. However, the opaque nature of these models impedes deployment in highly regularized and sensitive domains such as finance and health care where decision transparency is required. It is therefore crucial to explain model decisions to users in such cases. Though many explanation methods have been proposed in the past few years, there lacks a thorough understanding of their performances and trade-offs for the productionization of these methods.

      Join this session, as we share our insights from a wide-range of experiments to answer which algorithm to use for productionization, considering practical issues such as robustness across models and datasets, computational cost, and understandability for users.


      • Atanu Roy, Senior Manager, ML Solutions Lab, AWS India
      • Tomal Deb, Data Scientist, ML Solutions Lab, AWS India
    • AI and ML Stories track
    • Accelerated RoBERTa development leveraging distributed training

      RoBERTa is currently a pioneer in advancing self-supervised systems by robustly optimizing the method for pre-training natural language processing (NLP) systems that improve on BERT. Amazon SageMaker enables customers to train, fine-tune, and run inference.

      Join this session to learn how CACTUS Communications leveraged Amazon SageMaker distributed training to train RoBERTa models.


      • Praveen Jayakumar, AI/ML Solutions Architect Leader, India, AWS India
      • Jalaj Thanaki, Principal Data Scientist, CACTUS Communication

      Observe.AI - Best way to host cost effective-high throughput language models

      Observe.AI offers a conversational AI-driven software as a service (SaaS) product that transforms contact centers; by using AI to analyze customer interactions across channels, boost agent performance, and automate workflows that accelerate sales and retention. Language models like BERT are core to the intelligence offered by Observe.AI. As part of their journey to build a scalable, highly performant, and low-cost inferencing platform, Observe.AI explored various solutions across AWS, open source, and third party.

      Join this session to learn how Observe.AI evaluated various solutions and ported their model to AWS Inferentia for a cost-effective solution.


      • Venkatramana Ameth Achar, Solutions Architect, AWS India
      • Sai Guruju, Senior ML Engineer, Observe.AI

      Meesho leveraging AI & ML to solve e-commerce problems for next billion users of India

      Join this session to find out how Meesho is using AI and ML across the e-commerce platform. Deep dive into representation learning based retrieval to drive multi-staged ranking systems for recommendations across real-estate. Get an overview of the deep learning models and the architecture which powers these retrieval systems.


      • Rajesh Kumar SA, Director of Data Science, Meesho
      • Abhay Shukla, Data Scientist, Meesho

      Fidelity Investments AI & ML platform journey

      In this session, get to know how Fidelity Investments built an AI & ML platform using Amazon SageMaker focusing on serverless MLOps to provide a seamless customer experience and achieve operational success, without building additional tooling for model monitoring. Learn about their use case on Document processing and how they streamlined, optimized cost and maintenance efforts by running the inference from Amazon SageMaker platform.


      • Avishek Pradhan, Director of Workplace Investing business, Fidelity Investments
      • Ramanathan Natarajan, Director Architecture, MLOps & Advanced Analytics, Fidelity Investments
    • AI/ML for Business Applications track
    • AI Impacts @ Yubi

      Yubi (formerly known as CredAvenue) is a fully integrated debt financing platform that helps discover, trade, execute, and fulfil debt solutions for investors. They have over 2,300 corporates and 750 lenders on the platform. They recently became a unicorn and have built their complete tech stack on AWS. Data plays a major role in Yubi’s mission to change the debt landscape in India, as they use it to solve complex problems.

      Yubi has multiple varieties of data, ingested from both internal and external sources. The rich public data that is available on enterprise borrowers plays a fundamental role in their recommendation, match making, and scoring engines. They also have collections applications that deal with analysing user experience and compliance monitoring. They use MLflow as a model repository and use Amazon SageMaker for model development and deployment. They also use Amazon Textract to extract data from external documents and use Amazon SageMaker Ground Truth for labelling. Through the use of Amazon SageMaker, they have cut down by 50% the time spent by data scientists on productionizing models.

      Join this session to hear more about how Yubi leverages services provided by AWS.


      • Mathangi Sri, Chief Data Officer, Yubi (CredAvenue)
      •  Vivek Murugesan, Vice President, ML Engineering, Yubi (CredAvenue)

      Contact Center Intelligence on legacy on-premises telephony

      Digital Risk, a mortgage BPM (business process management) subsidiary of Mphasis Limited, India, uses on premises based legacy contact center platforms. One of them is from Mitel Networks Corporation, which handles thousands of calls per day. Through Contact Center Intelligence (CCI) powered by AWS AI & ML stack, Digital Risk sought to transform their agent experience and also gain insights for supervisors, analysts, and business leaders. They were particular about having a voice platform-agnostic intelligence layer which can be placed on top of any existing or new contact center infrastructure. On-the-call knowledge management by agent is simplified using NLP-powered search index with Amazon Textract and Amazon Comprehend. Join this session to hear about Digital Risk's use of AI & ML services from AWS.


      • Ramanuj Vidyanta, AI & Machine Learning Strategy & Business Development Specialist, AWS India
      • Sanjiv Kumar Shrivastava, Vice President and Head of Technology, Automation, Digital Transformation, Mphasis Limited

      Building an AI & ML solution for fraud and risk management

       A client had their machine learning workload divided between local and AWS, with a lack of automation at every flow. The client wanted to fully utilize AWS MLOps capabilities and automate their existing system with minimal human intervention. The business impact – reducing manual intervention in auto detection of fraud.              

      Join this session to learn how we incorporated entire Amazon SageMaker pipelines to pre-process the large variety of their datasets, and build the optimal model. We used in-built Amazon SageMaker algorithms to build the model and save it into model registry along with CI/CD pipelines. Gain insights on how we did this using multiple offerings from Amazon SageMaker, including Amazon SageMaker processing jobs, Amazon SageMaker Data Wrangler, and Amazon SageMaker Clarify.


      • Bhavesh Goswami, CEO & Founder, CloudThat
      • Dev P, CTO, Kisetsu Saison finance India Pvt. Ltd

      Indecomm Global Services uses Amazon Textract for document processing automation

      In the mortgage industry there is a huge amount of data and documents to be collected. Physically managing and extracting the right information from these documents is a challenging and time-consuming process. To solve this problem, Indecomm Global services uses Amazon Textract and Amazon SageMaker to automate documenting processing. They are able to process thousands of documents in a very short time frame, extract relevant information with a high level of accuracy, and initiate the next steps in the process using this information.     

      Attend this session to learn more about how AWS services are leveraged by the mortgage industry for document processing.


      • Praveen Hosur Narayanagupta, Solutions Architect, AWS India
      • Dr. Harish B Kamath, SVP, Engineering, Indecomm Global Services

Break (1:15pm - 2:00pm IST | 5:45pm - 6:30pm AEST)

Virtual booth showcase (3:30pm - 5:00pm IST | 8:00pm - 9:30pm AEST)

Vikram Anbazhagan

Vikram Anbazhagan

Director of Product Management, Amazon AI, Amazon Web Services


Guru Bala

Guru Bala

Head of Specialist Solutions Architect, AWS India

Akanksha Balani

Akanksha Balani

Global Alliance - AWS @ Intel

Vaishali Kasture

Vaishali Kasture

Director Enterprise Segment, AISPL

Harjyot Soni

Harjyot Soni

Director - Enterprise Support, AISPL

Sundar VG

Sundar VG

Director, Business Development, AISPL

Kumara Raghavan

Kumara Raghavan

India Head of Startup Business, AISPL

Meet our sponsors

Anker Cloud

Session levels designed for you

Level 100: Introductory

Sessions are focused on providing an overview of AWS services and features, with the
assumption that attendees are new to the topic.

Level 200: Intermediate

Sessions are focused on providing best practices, details of service features and demos with the assumption that attendees have introductory knowledge of the topics.

Level 300: Advanced

Sessions dive deeper into the selected topic. Presenters assume that the audience has
some familiarity with the topic, but may or may not have direct experience .

Level 400: Expert

Sessions are for attendees who are deeply familiar with the topic, have implemented a
solution on their own already, and are comfortable with how the technology works.

Learn more about AI & machine learning on AWS


customers use AWS for machine learning

Leader in IDC MarketScape: Vision Artificial Intelligence Software Platform 2021 Vendor Assessment


increase in team productivity using Amazon SageMaker


reduction in data labeling costs using Amazon SageMaker


of all deep learning projects in the cloud run on AWS

Frequently Asked Questions

  • Amazon AI Conclave Online is an online conference.

  • Whether you are getting started with AI/ML, an advanced user, a business executive, or curious about AI/ML, we have a specific track for your level of experience and job role.

  • All content will be in English.

  • Amazon AI Conclave Online is a free online conference.

  • If you have questions that have not been answered in the FAQs above, please email us.

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