In this session, you will learn how to quickly build real time recommendations for your customers with Amazon Personalize.
- To help you Personalize every touchpoint along the customer journey
- Help customers discover products faster
- Highlight new content offerings and Increase content consumption
In this session hear from AWS APN Partner Lumiq about their product Drishti and how Amazon Textract makes digital document processing a breeze.
- Learn how customer experience is impacted by multiple, often repetitive, touchpoints during a journey cycle: from onboarding to claims.
- Learn how the Drishti - Lumiq's Document AI product can reduce manual interface, improve productivity and TAT, reduce cost AND improve CX.
- Walk through a customer success story as the CTO of Canara HSBC Life Insurance talks about implementation of Drishti, and its impact on productivity and CX.
In this session, we will cover how to use Amazon SageMaker and Deep Graph Library (DGL) to train GNN models and detect malicious users or fraudulent transactions.
Data preparation is critical for effective ML model training to ensure higher accuracy.
In this session we will cover various options available to prepare data including Amazon SageMaker Processing and SageMaker Data Wrangler.
We will also cover how you can use SageMaker Feature Store, purpose-built repository to store, update, retrieve, and share machine learning (ML) features.
In this session, you will learn from AWS APN partner Quantiphi about running ML operations on AWS
Brief overview of Quantiphi’s technologies and capabilities in the field of data, platform engineering, ML & AI.
Overview of their ML operational excellence and understand the types of ML production operation solutions viz. ML Automation, ML Platform, ML Ops platform.
Through the course of this session Quantiphi will also showcase their capabilities in the field of ML operational efficiency and use cases relevant for customers.
In this session we will look at how to quickly annotate, train and deploy deep learning based Image classification and object detection models using Amazon Rekognition.
In this session learn how AWS AI services are applied to applications and used in real-life use cases. We will focus on how AI services can tackle multiple real world challenges to extract , process, and bring value out of your data by using AWS AI Services - Textract, Transcribe, Translate , Comprehend.
In this session, we’ll see how you can put your AI/ML project on the right track from the get-go. Applying common sense and proven best practices, we’ll discuss skills, tools, methods, and more. We’ll also look at several real-life projects built by AWS customers in different industries and startups.
Medical patient data is untapped, undervalued, or unused and this is true for 80% of the healthcare organizations around the world. Forward-thinking medical executives are now looking for ways to effectively extract value from medical data. Insights from this data can prove to be especially useful in these times where tele medicine and virtual doctor consultations have become the norm.
Join us for this ML Fridays session where we look at extracting insights from healthcare data and how applying AI/ML techniques can drive improved patient outcomes with early detection, improved diagnosis accuracy and efficiencies.
The transformer is one of the most popular state-of-the-art deep (SOTA) learning architectures that is mostly used for natural language processing (NLP) tasks. Ever since the advent of the transformer, it has replaced RNN and LSTM for various tasks. The transformer also created a major breakthrough in the field of NLP and also paved the way for new revolutionary architectures such as BERT.
Join this session
- To dive deep into Transformers, and understand how it uses the encoder-decoder architecture for a language translation task.
- We will learn how transformers overcome one of the major challenges which we had in recurrent models like RNN/LSTM in capturing the long-term dependency. Later we will learn about BERT and see how it differs from other embedding models and build a Disaster Tweet Analysis system.
With more than 4.6 billion internet users today across the globe, providing real time interaction capability between users is critical to the success of any social platform be it gaming, UGC, social networking etc. And adding a multi-language real time chat system will increase the platform adoption and user interactivity across regions.
During this session, we will demonstrate how to build a real time multi language chat system which can be integrated to any existing application by consuming a websocket API. Session will give you tips to use Amazon Translate, a neural machine translation service that delivers fast, high-quality, and affordable language translation and natural language processing service to create the system.
In this session you will learn how to harness the power of social media to capture your customer's/ target demographics experiences, expectations, desires and aversions. We will discuss how to use Amazon Comprehend and other AWS services to build a dashboard for social media sentiment analysis and use the insights to make data-driven decisions.
This will help you to:
- Listen to the voice of your customers and move with more agility
- Build a deeper engagement with customers using real-time engagement during events and promotions
- Track sentiment of your customer base with respect to your products/brand
- Build social media campaigns and track trends
- Proactively handle support issues
Organizations across all industries have a large number of documents that require processing of some kind. These documents, such as invoices, patient forms, loan applications, and contracts, contain data like applicant names, entities (places or brands), or patient health history, which is essential to their business processes. All of this data needs to be extracted from digital documents to perform tasks like process loan applications, analyze customer sentiment, determine patient treatments, or filter out non-compliant purchases from invoices.
Today, organizations spend millions each year on manual efforts to do this, which are time-consuming, error-prone, expensive, and do not scale easily. To help overcome these challenges, AWS offers Amazon Textract, Amazon Comprehend and Amazon Rekognition services, powered by machine learning which can be used to extract data from millions of documents, understand the sentiment of or relationships between those documents, and even help identify fraud in certain scenarios.
We will also have Abhay Singh, Manager, Data Science, Biz2Credit to talk about how they have leveraged Amazon Textract and Amazon Rekognition AI services for their use case.