Srishti Bhargava

Hello, colleagues, this Yahoo article is interesting and relatable for professionals. Here is a teaser: "Amazon has an incredibly granular list of criteria that need to be met before you can be considered for promotion, so I worked with my manager to identify the gaps in my performance, ... and I created a project for each gap that, if completed, could serve as evidence of my ability. I worked backward to create a strict timeline to finish my projects ... that helped me get systematic about my approach moving forward." https://lnkd.in/dsfvPeAk Take a lesson from this person. I seems Amazon gets very analytical when it comes to promotions. And in a company that size, it is impossible to not promote without rigid check-off type policies or whatever XYZ job promotion software. But, hey, there is a lot I will do for a $500,000 salary. Please take note of this last sentence, if anyone works for Amazon and wants a Quality Engineering Manager/Director anywhere in the world.

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Ok, so I get a lot of people asking how to setup custom, secure, safe, “ChatGPT Like” tools with access to proprietary information. In the procurement world this could be Spending and Transaction Policies, Delegations of authority, spend thresholds or any other or a myriad SOP’s. It can even be highly detailed category specific knowledge that you you want to surface to specific individuals. Now you can with #AmazonQ. Whether it’s straight for q&a or more advanced clause extraxtion, it’s all available right now. It’s easy to setup, super safe and secure and empowers procurement teams like nothing else! If you want to know how to get the most out of Generative AI tools in procurement (whether it’s a RAG like solution like this or something else) ping me and I will be glad to share my experiences! The world is changing procurement folk… let’s make sure we change with it! #aws, #amazon, #llm, #chatgpt, #innovation, #procurement, #generativeAI

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https://lnkd.in/gqUkq3BM is an excellent overview of major topics in LLMs. If you are getting started in this space, this video which is also generated by another LLM 😂 , is a great place to begin your journey in this exciting area of AI. It covers topics like pre-training, evolution of GPTs and other popular LLMs, fine tuning (SFT, PEFT like LORA, RLHF), inference optimizations (quantization, speculative decoding, distillation, flash attention), output sampling techniques, multimodal capabilities and finally some real world applications like math, coding, medicine etc. Loved their analogies even for core complex concepts like flash attention - "it's like finetuning the engine of a car, to make it more fuel efficient, without changing how the car drives"

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LLMs and LangChain: The Dynamic Duo 🦸‍♀️🤖 Just finished a fun project where we built a Q&A bot using these two powerhouses. It's like having a personal AI assistant that's always ready to answer your burning questions! 🔥 Here's what I learned: LLMs are wizards: They can conjure up answers from thin air (or, uh, the internet). 🧙‍♂️ LangChain is the glue: It holds everything together and makes it easy to use. 🔗 Endless possibilities: This tech can be used for anything from customer support to writing catchy social media posts. 📝 Want to see it live (obvio a test server link 😛 ): https://lnkd.in/dZNAVCSV Note: it's a render link, they will take down the instance because of no use, you can hit the link and wait for two minutes, they will spin up the instance and you can try it out. Want to see the code? Check it out here: https://lnkd.in/dzjh4r47 #AI #MachineLearning #NLP #LangChain #LLM #Python #Developer #FunProject"

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Vijay Kulkarni Harish Nagu Sana and I wrote a Medium blog titled "Stateful Feature Backfill on Apache Beam." We have learned a lot about Apache Beam, including checkpointing, savepointing, state management, and much more during the project. Check it out! #apachebeam #intuit #intuitengineering #featuremanagement #intuitAi https://lnkd.in/gVwxaeby

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Excited to share our blog on a stateful historical backfill feature using Apache Beam. Our team developed a unified pipeline for processing historical data and then to real-time. #apacheBeam #streamProcessing #featureManagement #intuit #intuitAI #apacheFlink

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Last week, I read a paper by Amazon on AWS Lambda and how they spin up 15,000 containers per second with a cold-start time of 50ms; and the three things I found most amusing are - block-level demand loading - deduplication of blocks with convergent encryption - use of erasure coding in caching for optimizing tail latencies If you want to know the details, I have a quick write-up explaining the paper along with my notes, and a quick TLDR. give it a read - https://lnkd.in/gWNx3Ek8 I have a habit of taking notes while reading papers. It has always helped me build a better understanding. To date, all of them were private in my obsidian. I have now started posting them as blogs on my website. Slowly, I will do it for all the papers in my arpitbhayani.me/papershelf and the new ones that I am reading. I absolutely love to jot down my thoughts, why not just make them public :) easy for everyone who wants to follow. #AsliEngineering #SystemDesign

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Re-posting one of my less popular / unorthodox views. Weekly 1:1s are hacks to get around the lack of proper team communication setups, and they weaken individual resilience. Adhoc 1:1s are fine for providing spot feedback, but they should be rare. Career growth 1:1s are great, but they should happen once in 3-6 months. Project check-ins / questions should be discussed in 5-10 minutes on slack. A design whiteboarding meeting between two engineers is fantastic, but it's not a 1:1. Weekly 30 minute 1:1s are unhealthy and an unnecessary time-sink, stop using them to communicate with your reports. https://lnkd.in/ehr_e2Xv

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Our trajectory with Google Cloud epitomizes a transformative strategic pivot. What began as a challenging market positioning has evolved into a compelling narrative of operational resilience and technological leadership. Over seven pivotal years, we've fundamentally reimagined our enterprise value proposition—transitioning from a peripheral cloud solution to a mission-critical AI innovation partner. The financial metrics tell a powerful story: we've systematically converted recurring quarterly deficits into a robust framework for sustainable profitability. Buckle up the next 7 years are going to be epic @ Google Cloud! 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 🚀 #google #googlecloud #GCP #gcpcloud

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Highly recommend course for intro to fine tuning! #AI #DeepLearning #LLM #FineTuning

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Last November the news was on fire, with speculation that an OpenAI model called Q* was getting close to AGI. Today Adam Selipsky announced their new product Amazon Q, coincidence? I think not. This used to be a product called CodeWhisperer. But it looks like it's merged into a product suite under the name Q. The website for CodeWhisperer had an announcement listed: "Amazon CodeWhisperer is now Amazon Q Developer, which is generally available. All of the functionality that CodeWhisperer provides is part of Amazon Q Developer – in-line code suggestions, security scanning, open-source license tracking, and more. This website will remain live until mid May. For the latest information and to learn more about Amazon Q Developer, visit the website." Any how, it looks like the play is to connect to internal data sources and employees to make generative AI apps based by their own apps. They note in their FAQs "When you sign up for Amazon Q Developer Pro or Amazon Q Business plans, we don't use content to improve underlying models for others. Your data is your differentiator." But it doesn't look like there are separate instances of the model but rather a super-model (yeah I made that pun). So if you trust that your data won't get integrated than you should be fine. Just like the trainwreck you couldn't look away from on the news. 🙄 P.S. 👍 If you found these insights useful, please like this post to support more content like this! 🔁 Feel free to share with your network. P.P.S. Subscribe to my newsletter, The Artificially Intelligent Enterprise, for weekly insights into how to succeed with AI in Business - https://lnkd.in/eqJNArYx

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For anyone involved in large-scale data processing, particularly those considering Apache Flink or Google Cloud Dataflow. The article gives guideline on how to setup a fair benchmark consideration as well as potential optimization that can be done on the pipeline: Real-world Benchmark: It offers a practical, real-world benchmark study conducted by Yahoo, providing valuable insights into the cost and performance of these platforms under specific use cases. This can help organizations make informed decisions about their technology stack. Fair Comparison Methodology: The blog post outlines the methodology used to ensure a fair comparison between Flink and Dataflow, emphasizing the importance of controlling variables and establishing a consistent testing environment. This level of rigor adds credibility to the findings. Cost Optimization Focus: It focuses on cost-effectiveness, a critical factor for businesses. The post helps readers understand how to minimize costs on both platforms.

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In 2022, I returned to Amazon after a brief stint at Zwift. Now, as a Principal Engineer taking on my biggest challenge yet, here's what I've experienced and learned: 🚀 Key reflections: ‣ Amazon's Leadership Principles remain the backbone of how we work. They continue to guide our decisions and actions daily, proving their enduring value. ‣ In Operations and Robotics, I dove deep into large-scale federated simulation and witnessed groundbreaking logistics technology. The innovation in this space is truly pushing boundaries. ‣ Despite turbulence in the tech industry, I've experienced stability at Amazon. However, I recognize this as a privilege earned through decades of hard work - a reality not everyone in tech has enjoyed recently. ‣ Lateral mobility at Amazon is powerful. I recently moved from Operations and Robotics to the US Prime and Marketing Tech team, reporting to a VP with an SVP as my skip-level. It's the biggest scope and responsibility of my career - both daunting and invigorating. ‣ This mobility keeps managers accountable: you must work hard to keep employees challenged and fulfilled, or they'll find a manager who will. ‣ My work-life balance and high level of autonomy have remained consistent throughout, unaffected by return-to-office mandates (a benefit of the demand for Principal Engineers). ‣ Taking on this new role in US Prime and Marketing Tech is the biggest challenge of my career. It's a testament to the growth opportunities available at Amazon for those willing to step out of their comfort zone. Returning to Amazon was the right choice for me. The chance to work on cutting-edge technology, move between teams, and maintain autonomy in my work has been incredibly rewarding. While I'm excited about my personal journey, I'm also mindful of the complex challenges facing the broader tech industry. As I look ahead, I'm energized by the scope of my new role and the impact I hope to make. Here's to continuous learning and growth, wherever your career takes you. What's your experience navigating career moves in today's sometimes crazy tech landscape? How do you balance stability with new challenges? And, how can I help you??? Curious about the full story? Check out the link in the first comment for the blog post. #CareerGrowth #TechIndustry #PrincipalEngineer #Amazon #AmazonCulture

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Test time training of LLM. For easier problems providing context along with the prompt is enough. For harder problems like math, coding, law etc. taking a sample of similar problems and training few batches on it improves accuracy many X times (3-14 times or so). https://lnkd.in/gwp97RFr

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Formula 1 has extensively integrated AWS cloud technology into its operations and fan engagement efforts in recent years. It's a game-changer for speed and efficiency. Here are some key stats and insights about F1's use of AWS: Data Processing and Analytics - F1 cars generate over 1.1 million data points per second from 300 sensors on each car. - This massive amount of data is transmitted from the cars to the pits and then processed using AWS cloud infrastructure. - F1 stores 65 years of historical race data in Amazon S3 storage. Simulation and Design - Using AWS high-performance computing, F1 was able to run aerodynamic simulations to develop its next-generation car 70% faster than before. - The F1 engineering team increased their weekly simulations from 20-30 to 80-90 tests using AWS cloud infrastructure. - Simulation run times were reduced from 60 hours to 12 hours by using AWS instead of a supercomputer. Fan Engagement - AWS powers F1 Insights, which provide real-time statistics and graphics during race broadcasts. - By the end of the 2020 season, F1 had introduced 10 different AWS-powered Insights graphics. - These insights use machine learning models trained on historical and live data to provide predictions and performance metrics. Sustainability - Moving operations to the AWS cloud has reduced the amount of IT equipment F1 needs to transport globally for races. - Independent research shows running on AWS provides an 80% lower carbon footprint compared to F1 operating its own data centers. Production and Operations - F1 processes around 500TB of media data captured during a 90-minute race using AWS. - Remote production capabilities enabled by AWS have helped reduce freight and personnel travel. This extensive use of cloud computing has allowed F1 to innovate faster, improve car designs, enhance the viewing experience for fans, and make progress on sustainability goals. If this resonates, and need some support, reach out for a chat!

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Trying to reduce costs of your LLMs by 2x? Use a small highly optimized "router" LLM to decide if you need a heavy or a light LLM based on the context. Simple questions go to the light LLM, tougher ones to the heavy LLM. Most surprising finding: the router LLM generalizes across different heavy and light LLMs -- no fine tuning required! (Also this is my first academic paper in 20 years -- it was a lot of fun working with Amjad Almahairi on this)

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I am excited to share the latest blog, co-authored by myself, Junhwi Kim and Vyas Sekar, detailing how we managed to successfully localized and improved LLM by combining Fine-Tuning and RAG. https://lnkd.in/gKDkgGPq

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This past weekend, I spoke at the July meetup of CNCF Hyderabad. My talk was on "Running Generative AI workloads on Kubernetes", as the name suggests, I provided a glimpse of how organizations can scale their Generative AI projects sitting atop of the accelerated compute infrastructure. I sincerely thank Atulpriya Sharma for providing me with such an opportunity. I hope to conduct such talks/workshops/webinars online so there is no barrier when I leave to the US this Fall :) I had already provided the GitHub repo to the demo I presented during the talk, but if you missed it, here's the link: https://lnkd.in/gwRufAw6 Another important note is that I converted the demo into an article which will soon be published on the InfraCloud Technologies blog, so keep an eye out for that!

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