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Amit Arora

Principal SA AI/ML @ AWS | Adjunct Faculty at Georgetown University | Author: "Blueberries In My Salad: My Forever Journey Towards Fitness & Strength".

Amit Arora is a Principal Solutions Architect specializing in AI/ML for Strategic Accounts at Amazon Web Services (AWS).1 Here are some key details about his background and experience:

Professional Experience

  • Currently works as Principal Solutions Architect AI/ML - Strategic Accounts at Amazon Web Services (AWS) since April 20221
  • Previously held roles at Hughes Network Systems, including:
    • Senior Principal Engineer (April 2015 - Present)
    • Principal Engineer - 2 SW (November 2012 - Present)
    • Scientist (October 2021 - April 2022)
  • Worked at Aricent in various engineering roles from 2000 to 2012, progressing from Software Engineer to Senior Engineering Project Manager1

Education

  • Master's Degree in Data Science from Georgetown University (2016-2018)1
  • B. Tech in Instrumentation and Control from Netaji Subhas Institute of Technology (1996-2000)1

Skills and Expertise

  • Specializes in AI/ML solutions for strategic AWS accounts1
  • Experienced in cloud architecture, particularly for financial services customers2
  • Knowledgeable in AWS security services, including identity and access management, network security, and threat modeling2
  • Skilled in deploying secure workloads in the cloud2

Additional Roles

  • Adjunct Lecturer at Georgetown University1
  • AI and ML Specialist Architect at AWS, helping enterprise customers utilize cloud-based machine learning services34

Amit has over 20 years of experience in the tech industry, with a strong background in security, cloud architecture, and AI/ML solutions. His expertise spans across various domains, including financial services, making him well-suited for his current role at AWS where he helps strategic accounts implement AI/ML solutions.

Highlights

Aug 18 · linkedin

Over the last 9 months, FMBench https://lnkd.in/esdVmW2G has become a passion project for me. Benchmarking foundation models and serving stacks is a key part of selecting the right model for a generative AI workload. As we kept on adding new features to FMBench while helping customers with their benchmarking needs, a key question remained “but what about model evaluations?”, after all, both evaluations and performance benchmarking are needed for selecting a model and a serving stack for a generative AI workload.

Today, am excited to share news about a major FMBench release, we now have release 2.0 for FMBench that supports 🎉model evaluations through a panel of LLM evaluators🎉. With the recent feature additions to FMBench we are already seeing increased interest from customers and hope to reach more customers and have an even greater impact. Check out all the latest and greatest features from FMBench on the FMBench website https://lnkd.in/eddZ7tRc.

With FMBench you get the following:

✔️Support for Model Evaluations: FMBench now adds support for evaluating candidate models using Majority Voting with a Panel of LLM Evaluators. Customers can now use FMBench to evaluate model accuracy across open-source and custom datasets, thus FMBench now enables customers to not only measure performance (inference latency, cost, throughput) but also model accuracy.

✔️Native support for LLM compilation and deployment on AWS Silicon: FMBench now supports end-to-end compilation and model deployment on AWS Silicon. Simply put the relevant configuration options in the FMBench config file and it will compile and deploy the model on Amazon SageMaker or Amazon EC2.

✔️Website for better user experience: FMBench has a website now along with an introduction video. The website is fully searchable to ease common tasks such as installation, finding the right config file, benchmarking on various hosting platforms. model evaluation, etc.

✔️Native support for all AWS generative AI services: FMBench now benchmarks and evaluates any Foundation Model (FM) deployed on any AWS Generative AI service, be it Amazon SageMaker, Amazon Bedrock, Amazon EKS, or Amazon EC2. We initially built FMBench for Amazon SageMaker, and later extended it to Bedrock and then based on customer requests extended it to support models on EKS and EC2 as well.

As for what next, model evaluations have evolved into RAG evaluations, AI Agent evaluations so stay-tuned for some exciting updates in this area, along with continued work on better user-experience and new features based on customer feedback.

Website: https://lnkd.in/eJ2n8tt7 GitHub: https://lnkd.in/esdVmW2G

We look forward to partner with you on your FM benchmarking and evaluation journey!

Cc: Madhur Prashant

Dec 30 · linkedin

As the year 2023 winds down to a close, I thought I'd make a list of the books I have read this year. While there is never enough time to read all the books you want, but am happy to have read across a broad spectrum of topics from psychology to theoretical physics (sometimes you get the feeling if there even is a difference between the two). Like many people (everyone?), I also realized this year that you can allow yourself the liberty to leave a book midway if reading it becomes too onerous (there are just too many good books out there waiting to be read!).

Not that anyone asked 😊, but one recommendation from each of the three categories of books I read would be:

1/ Useful delusions (psychology, neuroscience) - it made me question how I think about the world, it is a short book and I would absolutely recommend it.

2/ The girl on the train (murder mystery, fiction) - incidentally, I bought this book at a train station and I read a lot of this book on trains and airplanes. If you have not seen the movie then do read this (to my mind at least) written to be made into a movie kind of book.

3/ Parallel Worlds (physics, cosmology) - a very enlightening account of our understanding about the theories of the universe over the past 200 years, it is mind numbing to realize what we do know today about the universe and even more mind numbing to understand how much we do not know.

If there is just one book from the ones I read this year that I am thankful for having stumbled upon and read then that would be "Useful delusions: The Power & Paradox of the Self-Deceiving Brain". Cannot recommend it highly enough.

Here is to more reading in 2024! #books #reading #psychology #physics #fictionbooks #readinglist

Related Questions

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How did Amit Arora transition from Hughes Network Systems to AWS?
What specific AI/ML solutions does Amit Arora specialize in at AWS?
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Amit Arora
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Location

Clarksburg, Maryland, United States