Mackenzie is the Global Startup Evangelist at AWS. His days are spent traveling the globe to meet startups, share their stories, and connect engineering teams together. Every day there are a large number of startups launching on AWS across every imaginable industry. It’s Mackenzie’s mission to find stories of startups that are helping to improve the world and share these stories with a wide audience.
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Prior to joining AWS, Mackenzie was the Head of Technical Operations at Betterment, the world’s largest independent robo-advisor based in NYC which manages over $8B in assets. Mackenzie was a founding engineer and Head of Technical Operations at Oscar Health, an insurance startup also based in NYC, helping to grow the company to over 400+ employees.
The AWS NLP Summit 2022 will host over 25 sessions focusing on latest trends, hottest research, and innovative applications leveraging Natural Language Processing (NLP) capabilities on AWS. You will have the chance to learn about the latest trends in NLP from our keynote speakers. AWS experts will share how to leverage NLP for your use cases and you can participate in hands-on workshops that translate theory directly into applicable knowledge. You can also connect with Solutions Architects to discuss your use cases, and network with peers directly. By the end of this summit you will have a clear view on how to use AI Language Services and state-of-the-art open source models (e.g. BERT, GPT, etc) on Amazon SageMaker to tackle your NLP challenges.
Natural Language Processing (NLP) is one of the most disruptive technologies within the field of AI & Machine Learning and continues to fuel cutting-edge innovation even though it has significant mainstream applications. With the potential to unlock key business potential and new revenue streams, enterprises are using NLP for diverse use cases including:
* Transforming and modernising contact centre operations with revenue generating insights
* Automating document processing workflows across industries including claims adjudication, mortgage processing, clinical notes management and more
* Building differentiated search experiences with semantic search, content enrichment, metadata tagging and intent recognition improved spelling, grammar, diction and phrasing in real-time as users write
* Monetising media content with context based ad-serving
* Improving Know Your Customer (KYC) processes with voice of the customer analytics
* Reducing expansion and localisation costs with neural machine translation
* Boosting average revenue of sales representatives by optimising the amount of time the spend on certain topics in sales calls
* Engaging with customers in real-time wherever those customer interactions occur— fielding questions on websites, automating routine customer support requests, giving customers updates on their orders, or supporting sales efforts.
* Automated recommendations for
* Content moderation to detect profanity, misogyny and toxicity in text
* Uncover insights from customer calls — manage script compliance, find new opportunities, expand your services to address gaps, elevate the customer experience delivered by your contact center agents
* Automated summarisation
* Legal Contract Review — automatically extract and identify key clauses from contracts, saving hundreds of hours of manual labor
Track 1: Workshop - Build a hotel reservation agent
Track 2: Workshop - Easily run Transformers notebooks with Studio Lab
Track 3: Visually build conversation flows with Amazon Lex Visual Conversation Builder(10:30-11:00)
Track 3: How to deploy and use HuggingFace in an enterprise environment(11:00-12:00)
Track 4: Generating art from text with distributed training on Amazon SageMaker(10:30-11:30)
Track 4: Deploy large NLP models on SageMaker using DJL DeepSpeed inference(11:30-12:00)
Track 1: Workshop - Augment your HuggingFace model with Human-in-the-Loop
Track 2: Workshop - Distributed Training and Inference for Large Language Models on Amazon SageMaker
Track 3: NLPOps - Operationalise and automate your NLP pipeline using AWS (1:00PM-1:30PM)
Track 3: Build highly accurate NLP solutions faster with the open-source AutoGluon and Amazon SageMaker (1:45PM-2:30PM)
Track 4: Responsible AI: NLP Explainability
Track 1: Hugging Face Model Explainability for customer sentiment text analytics
Track 2: Accelerate your NLP workloads with Trainium & Inferentia
Track 3: Automated mining of emergent catchphrases for advertising content moderation
Track 4: Knowledge Representation and Extraction at Scale (4:45PM-5:15PM)
Suman Addanki is head of the ML Engineering for AI Services in JPMorgan AI Platform. He is currently responsible for delivering scalable distributed systems for NLP, speech and large language models. He has about 20 years of Software Engineering experience. Prior to this, Suman worked at Sanofi and successfully delivered many of their key Analytics, Data Integration and Big Data systems. Working with IBM, Suman also provided Technology consulting services and successfully delivered many projects to AIG, American Express, Ameriprise, Caterpillar, Occidental chemicals, Zimmer.
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Igor Carron is the co-founder and president of LightOn where he thinks that making the most powerful Large Language Models available to the Enterprise is key to their transformation. Igor has a background in Engineering (space and nuclear) as well as Machine Learning and has organized one of the largest Machine Learning meetup series for the past ten years in Paris. Â
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Timo is a Co-Founder of deepset and responsible for bringing deepset's products, the open source framework Haystack and our commercial offering deepset Cloud, to clients. He has a background in Data Science and is a passionate NLP engineer and open source fan. Currently, he thinks a lot about business applications of NLP, bringing NLP into production, or growing a scalable customer success organization.
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