Vartika Sharma

Check out this highly recommended course for those interested in optimizing their models: - Introduction to Quantization - Symmetric and Asymmetric Quantization Modes - Hands-on Implementation from Scratch using PyTorch - covering Per Tensor, Per Channel (Axis), and Per Group techniques - Weights Packing: Exploring 2 or 4 Bits Quantization Methods https://lnkd.in/gMAuWXHm

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Attended an insightful session today at AWS re:Invent 2024 focused on using AI/ML services for accelerating cancer biomarker discovery. We explored how Amazon Bedrock Agents utilize agentic workflows to simplify and speed up complex oncology research processes. 💡 Key takeaways: 👉 How AI intelligently decomposes tasks, combines data sources, and visualizes insights for more effective cancer research. 👉 Leveraging Amazon Bedrock to reduce time spent on multi-step analyses and improve clinical trial outcomes. 👉 AI-driven tools have the potential to save weeks of work, enhancing drug development and cancer biomarker research. A promising session for anyone working in oncology and biopharma, showcasing how AWS is driving innovation in cancer research. #AWSreInvent #CancerResearch #BiomarkerDiscovery #GenerativeAI #AIinHealthcare #AWS #DrugDiscovery #Oncology #AWSPartners #zeb #AWS

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🚀 How the Einstein Visa Motivates Excellence! 🎯 The EB1A visa isn't just a pathway to residency—it's a powerful motivator for individuals to excel in their fields. It encourages professionals to aim higher, innovate, and achieve milestones that they might not have pursued otherwise. Here’s why the EB1A is a game-changer: 🔍 Sets a High Bar for Achievement: The stringent criteria for the EB1A visa push individuals to distinguish themselves through groundbreaking work, publications, awards, and leadership roles. 🌟 Fosters a Culture of Excellence: Striving for EB1A eligibility often leads to a deeper commitment to one’s profession. This drive can result in significant contributions to the field, benefiting both the individual and the industry. 🚀 Encourages Continuous Learning and Growth: The journey towards the EB1A visa often involves acquiring new skills, expanding knowledge, and staying updated with the latest advancements. This commitment to continuous improvement is invaluable. 💼 Builds a Strong Professional Network: Meeting the EB1A requirements often necessitates networking with other experts and thought leaders. This can open doors to new opportunities, collaborations, and career advancements. 🏆 Recognizes and Rewards Dedication: Achieving the EB1A visa is a recognition of one’s dedication, hard work, and significant impact in their field. It’s a testament to their extraordinary ability and contributions. In essence, the pursuit of the EB1A visa aligns closely with what professionals should inherently strive for: excellence, innovation, and leadership. It’s a journey that not only leads to personal growth and success but also contributes to the advancement of entire industries. #EB1A #CareerExcellence #ProfessionalGrowth #Innovation #Leadership #Motivation

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Very excited to share that I've completed an enriching Natural Language Processing(NLP) course at UC Berkeley this quarter(Part of my Masters degree)! Delving into the intricacies of language comprehension and analysis has been truly enlightening. As we explored concepts like information extraction, sentiment analysis, and machine translation, I couldn't help but reflect on the significance of attention in NLP. Indeed, "Attention is all you need"(Transformer based model) proved to be a guiding principle throughout our studies. Grateful for the opportunity to expand my knowledge in this field! #NLP #UC Berkeley School of Information I highly recommend diving into the research paper linked here (https://lnkd.in/ggA_SrUb), though it may take a few reads to fully grasp its depths, along with Jay Almmar's insightful blog post (https://lnkd.in/gbDJ3Ddj). Excited to delve into "Generative AI" as my focus for the upcoming quarter!

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Longhorn Electric Racing Is Building the Future of Electric Vehicles with Cadence Tools. Longhorn Electric Racing is a student-run racing team at the University of Texas, Austin. Not only are they building the best race cars, but they’re also building the future generation of engineers. Their electric vehicle team is one of their most recent teams, founded in 2016. Some of the challenges they face in designing an electric car are making sure it can go as fast as possible, making sure it has sufficient cooling, and making models effectively. With the help of Cadence CFD and Cadence OnCloud, they’re reaching their goals much more efficiently. https://ow.ly/phqj50RuqK4

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🚨🚨 Important Update on EB-1 Visa Eligibility! 🚨🚨 📢 On October 2, 2024, USCIS issued new guidance on the EB-1 extraordinary ability (E11) immigrant visa criteria! 📢 In this latest policy update, USCIS has provided clarifications on the types of evidence they consider when determining EB-1 eligibility, making it easier for applicants and petitioners to understand what's required. 📜 Key Highlights: 1️⃣ Team Awards: USCIS will now recognize team awards as valid evidence under the “nationally or internationally recognized prizes or awards” criterion. 🏆 2️⃣ Memberships: Past memberships can be evaluated as part of the membership criterion for extraordinary ability. 👥 3️⃣ Published Material: Language implying that published material must demonstrate the value of a person’s work has been removed, providing more flexibility. 📝 4️⃣ Exhibitions: While "exhibition" is broadly defined, USCIS clarifies that only artistic exhibitions will count unless evidence properly supports claims of comparable achievements. 🎨 This update brings clarity and transparency, helping applicants better prepare their evidence and strengthening their petitions. 💼 🔗 Read the full update here: https://lnkd.in/dUTQWz-4 This policy is effective immediately and overrides any previous guidance on these topics. 📚 For more details, check out the Policy Manual, Volume 6, Part F, Chapter 2. ✨ We feel this is great news! Want to stay ahead of the curve and get that green card? Email your resume to [email protected] today! 🚀 Visit us at: www.jineegreencard.com #USImmigration #GreenCard #EB1Visa #ImmigrationGuidance

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❝ How do you taste your Wine? With the right Glass, served from the right Bottle. How do you utilize your LLM? With the right Task, served from the right Compute environment. ❞ If the quote above caught your interest, check out my blog post that was featured on the Toyota Connected India Medium channel last week: Leveraging LLMs in Cloud - 🔗 https://lnkd.in/gfHsdevH --- I hope this blog adds a bit of amusement for the seasoned AI/ML engineers 🧠👨‍💻 with the carefully crafted images and perhaps even the silly wine analogies 😉. Whether you're a cloud engineer ☁️ 👨‍💻, a product owner of cloud-based apps, or someone new to Generative AI in the cloud, this blog offers a detailed, beginner-friendly look into the selection and deployment of LLMs in cloud environments. --- A heartfelt thanks 🙏 to my colleagues at Toyota Connected India for taking the time to review, shape this piece and even share a creative writeup like below in the original post 🫡 👇 🤔 Curious to dive in? Click the link below to explore the blog on Medium! 🌐 🔗 https://lnkd.in/gfHsdevH

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Do LLMs have any immediate applications in the field of Robotics? The above question was loosely based on a query asked by a startup founder in an event, based on my colleague’s work on a recent LLMs integration. (Siddharth) Come to think of it, robotics about 10 years ago wouldn’t have been able to accommodate such computational expenses. Imagine running a LLM model on a Raspberry Pi zero and not expecting it to blow up! :) While implementing a LLMs could be a fun side project, what question warrants a deeper insight would be whether it is of absolute necessity? With Nvidia doubling down on the highly powerful Jetson devices, and Blackwell chips, the hardware is expansive and available, but it still begs to ask the same question again and again - is it necessary? Like every other industrial trend, once must deeply analyse where a novel idea would fit in, and calculate against its effort and yield. Let’s explore a few options wherein it could be absolute importance to use LLMs in context of Robotics: 1. Support agents - Social robotics has a huge potential of closing down on the weary technical support based on an exhaustive list of solved and interpreted problems. Distilling technical documents and datasheets into a message parsing model is an important win to do effective knowledge transfer. 2. Assistive bots - In the field of soft robotics and prosthetic arms/exoskeleton, a voice controlled NLP agent would be great help in helping administer orchestrated actions for differently abled individuals who have clear motive/task list. 3. Surveillance/Monitoring bots - Given the nature of preordained message intents, it is a perfect candidate for having a lightweight message-action model for surveying specific sites. Looking at the magnanimity of the manufacturing plants, this would be a great boom. 4. Surgical bots - Expert assisted robotic surgeries are a natural extension for aligning non-conventional anomalies that are very unique to the nature of the surgery. Since precision and targeted algorithms are of utmost importance, one cannot make actions based on learning based approaches effectively from general archetypes. Are there more applications that cross your mind marrying LLMs and Robotics?

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Join me and Guruprasad Raghavan at Yurts today where we discuss a high level overview of one method that we're utilizing to improve model efficiency and efficacy for our Enterprise customers. There's currently an arms race amongst companies to develop the most capable LLMs, but this often comes at the expense of computational costs. In our blog below, we'll discuss quantization, how it works conceptually, how it can enable cost reductions of over 80%, and how we improve on state of the art methods to minimize performance degradation. We evaluate performance degradation qualitatively and quantitatively by comparing a Yurts tuned model with popular commercially available off the shelf quantized models. All the generated data for this article is shared within the post! Come join the discussion here https://lnkd.in/g2WbMVhY

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Ever wondered how random numbers work in NumPy and why setting a random seed is important? 🌟 Check out my latest blog to learn more about true vs. pseudo-randomness and the role of random seeds. #Randomness #PseudoRandomness #NumPy #PythonProgramming #Algorithms #Computing #Cryptography #ScientificComputing

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🌟 **Decoding Vector vs. Graph Databases for RAG & RAFT – Which One Wins?** When it comes to enhancing large language models through Retrieval Augmented Generation (RAG) or Retrieval Augmented Fine-tuning (RAFT), the choice of database—vector or graph—plays a pivotal role. 🔹**Vector Databases:** Data is partitioned into chunks and each chunk is transformed into a vector using a language model. These vectors are then indexed in a vector database, facilitating quick nearest neighbor searches to find the most relevant information chunks in response to a query. 🔸**Graph Database:** This database shines in storing relationships between entities. By parsing and indexing these connections, graph databases excel at constructing detailed subgraphs for specific queries, ensuring that only the most relevant, contextually connected information is retrieved. It’s about understanding the network of relationships.An LLM is good at extracting the below kind of triplet information. [Entity A] -> [Relationship/Context] -> [Entity B] Both have their strengths—vector databases are great for fuzzy matching large data sets quickly, while graph databases excel in precise context and relationship extraction. 🚀 **The cutting edge? Combining both!** : This hybrid model takes advantage of the quick searching capability of vector databases with the deep relational insights of graph databases. For RAG or RAFT, this means not only retrieving the most relevant information quickly but also understanding its context within a broader knowledge base. For example, if a model is answering a query about "healthy eating," a vector database can quickly fetch documents mentioning health and nutrition. A graph database then elaborates on how these concepts connect with diet plans, medical advice, and recipes, providing a comprehensive, context-rich response. #GraphRAG #RAG #RAFT #GenerativeAI #GraphDB #VectorDB

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🚨 Struggling with Low-Frequency Products? Here’s the Playbook I Wish I Had 🚨 For 15+ years, I’ve been managing products that people don’t use every day and only on episodic basis. And let me tell you—it’s tough. No guide. No clear answers to questions like: 👉 How do you measure product-market fit? 👉 How do you optimize acquisition costs when usage is infrequent? 👉 How do you actually drive retention? The frustration led me to create ICED Theory—a framework so intuitive and logical, it became part of Reforge’s course material. Now, for the first time ever, I’m hosting LIVE virtual classes to teach the ICED framework and help you grow and manage low-frequency products. 📅 Two intense days of interactive discussions. 💡 Deep dives into strategies that work. 🎉 50% launch discount for my first cohort. 👉 Ready to transform your approach to low-frequency products? Enroll now and secure your spot: https://lnkd.in/gHwUsGiJ Spots are limited, so don’t wait! Let’s crack the code together. 🚀 #lowfrequencyproducts #workshop #productmanagement #training #cohort

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I had the pleasure of attending a great presentation titled "Sculpting Data for ML" by Jigyasa Grover at Dev<Talks>. Her insights on data curation, extracting meaningful datasets, and the importance of high-quality data in machine learning were highly informative. Jigyasa's expertise and engaging delivery made complex topics accessible and actionable. Kudos to her for an excellent session! #DevTalks #MachineLearning #DataScience #GenAI

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It’s been a while since I have shared my experiences of working on AI projects using LLM’s that I have worked on in last 2 years. Hope these experiences will help folks. 😊 so here is the first one   Language Classification with GPT – We were working on a problem scenario where 2 languages to be classified in separate classes were very much similar in terms of words available in them and up to a good extent semantically also, but the languages were actually different. As the languages were domain specific, it was expected that we have to empower GPT. We used different models starting with GPT 3.5, GPT4 Turbo 0125, and then GPT 4 Omni and at last GPT-4 Turbo 2024-04-09. Of all the models we got the best accuracy with the last one. The last 2 are the latest ones available and as per Microsoft recommendation the go-to GPT models. However with GPT-4 Turbo 2024-04-09 also we didn’t got the desired result by the same pipeline we were using (words filter + vector search + RAG). While searching for the method to resolve the issue, I came across a research paper around ‘Feedback loop’ and how it can improve the results. (Sorry didn’t remember the paper name now , will try to find and post that).  We applied the same with some experimentations and it worked. So the focus here is – if you have a use cases where GPT/LLM’s is getting confused in language classification after application of latest techniques like vector search and RAG, feedback loop can be introduced after the final output as a last step. It can enhance the accuracy in very confusing but important cases. Hope this will help community. 😊 #NLP #LLM #GPT #RAG #DataScience #AI #OpenAI #Chatgpt OpenAI

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Munir Al - Dajani and I wrote about our overall approach for aligning GPT to a healthcare task - check it out! We're also planning some more in-depth & more technical blogs for the near future so stay tuned.

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RAG is one of the basic techniques to leverage large language models in combination with the custom knowledge base. In my recent blog post, I explore what exactly happens inside RAG-based solution, even before the first byte of the prompt is sent to LLM. #LLM #AI #ML #RAG #PromptEngineering

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Excited to share insights from my recent conversation with Shaan Mukhtar and Scott Cecil about the open-source library RAY (https://lnkd.in/gE6sShCe) ⚡️ This discussion opened my eyes to the potential of Ray for accelerating data science workflows. Here's what I learned: - Ray simplifies data ingestion, feature engineering, and deployment, especially on unstructured data. - Ray Serve makes deployment a breeze with just one line of code. - Scaling up and down is effortless, utilizing both CPUs and GPUs. - KubeRay enables seamless integration with Kubernetes for scaling. - Ray is a game-changer for LLMs, providing efficiency and lower latency. - While SQL remains essential, Ray can complement and even replace Spark in certain scenarios like unstructured data. I'm eager to explore Ray further and put these learnings into practice. Thanks to Shaan and Scott for the insightful conversation and personalized connection! 🙏 #Ray #DataScience #MachineLearning #LLMs #Deployment #Scaling

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Lyft ML Engineer Rachita Naik shares a podcast that deep-dives into #machinelearning use cases in Lyft Rideshare and ETA reliability prediction. In addition, Rachita also shares some challenges she sees in deploying ML models, and offers tips on how to stay updated with #ML/#AI trends. It's packed full of invaluable insights. Have a listen here! https://lnkd.in/e62dYaeU

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🎉 Excited to share a snippet from my very first podcast as a host! 🎙️ It’s just the beginning, and there’s a long way to go. #MyFirstPodcast #CareerGrowth #ProfessionalDevelopment #Inspiration #LongWayToGo

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