Introduce yourself! #35
Replies: 25 comments 6 replies
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Hello, my name is Jeremy Ellis. You can find me on GitHub at https://github.com/hpssjellis and on LinkedIn at https://www.linkedin.com/in/jeremy-ellis-4237a9bb/ I've been creating neural networks for nearly 30 years and teaching high school machine learning for 8 years. Where I lack in book writing skills, I excel in simplifying ML concepts for teaching. In the cs249r_book, several chapters have discussed the security advantages of tinyML. You can explore these insights in the following links: embedded_sys.qmd Line 61 in d9c33af embedded_ml.qmd Lines 154 to 156 in d9c33af and also Line 204 in d9c33af However, it's worth noting that these chapters don't seem to mention that most tinyML models are created on cloud platforms. This inherently poses a security risk, as data is transferred during the training process, and there are questions about the handling of that data while it resides on the cloud. My particular area of interest revolves around streamlining the process of efficiently developing client-side machine learning models for tinyML. The question at hand is whether the construction of tinyML models is as secure as we assume, and if there are alternative approaches worth considering? |
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Hey @hpssjellis! Sorry that this message missed my inbox, I have been overwhelmed. Thanks for jotting down the points. I agree there is room for improvement. As we are working through the chapters, these points are covered in the later chapters, or at least I'd like to think so. Please continue to look through the other chapters, as they become available. Feel free to make suggestions or edits, as you see fit and issue a PR if you'd like to. We are open for contributions! Some of the specific things about security etc. will be covered in the on-device learning and security/privacy chapters 👍 |
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Hi all, Jared Ping here from South Africa. Group Technical Manager at Digital Matter, where we have a huge focus on the energy efficiency of battery-powered tracking devices in a range of applications (TinyML included!). Currently pursuing a part-time MSc(Eng) Electrical Engineering at the University of the Witwatersrand, specialising in operational optimisation of TinyML systems. Very excited to see the TinyML community grow, and this book is a great resource for introducing peers to it! |
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Hello everyone, My name is Ethan Wang, a Master's student in Software Engineering at North China Electric Power University. My journey in the tech world is driven by a profound passion for operating systems (OS) and the burgeoning field of Tiny Machine Learning (TinyML). The challenge of maximizing the capabilities of limited resources to achieve seemingly limitless tasks fascinates me. Currently, I am interning at Megatronix, where I am honing my skills in system stability, a crucial aspect of software engineering that aligns closely with my interests in OS and TinyML. My Main Interests:
Future Aspirations:My immediate goal is to deepen my understanding and expertise in OS and TinyML through practical experiences and academic research. However, looking ahead, I am also setting my sights on opportunities to study and work abroad. I believe that pursuing further education and professional experiences abroad will not only broaden my horizons but also provide me with a unique perspective on global technological challenges and innovations. Contributions to the Community:
Additional Links:
I am looking forward to engaging with this community, learning from you all, and contributing where I can. Thank you for welcoming me into this vibrant space! |
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Nice to see that this is an active repo and work in progress with commits as latest as yesterday! |
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Welcome Seshu, please feel free to share any thoughts and feedback you may
have. We have some exciting new updates coming through!
Vijay Janapa Reddi, Ph. D. |
John L. Loeb Associate Professor of Engineering and Applied Sciences |
John A. Paulson School of Engineering and Applied Sciences |
Science and Engineering Complex (SEC) | 150 Western Ave, Room #5.305 |
Boston, MA 02134 |
Harvard University | Email ***@***.***> | Website
<http://scholar.harvard.edu/vijay-janapa-reddi> | Google Scholar
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<https://scholar.harvard.edu/vijay-janapa-reddi/schedule> | Admin
<https://scholar.harvard.edu/vijay-janapa-reddi/contact> |
…On Fri, Mar 22, 2024 at 8:17 PM, Seshu-R ***@***.***> wrote:
Nice to this that this is an active repo and work in progress with commits
as latest as yesterday!
Just started reading.
Hope to see Exercises and Labs soon. Thanks in advance for all the efforts
and helping the broader community.
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Hello everyone, You can find me on:- |
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If you're interested in robotics, I strongly encourage you to engage in practical deployment scenarios of AI. Robotics and AI require embedded machine learning technologies and capabilities. Both these domains are computationally very intensive and they're also severely power constrained, therefore HAI technologies are going to be quite critical for them. |
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Hello Professor Reddi and everyone! 👋. I'm Hardik Agarwal, an undergraduate student at the Indian Institute of Technology, Delhi. My interest in ML ignited when I saw how effective it can be in tackling real-world problems with surprising efficiency and precision. Over time, I began to explore many aspects of machine learning, and I am presently most interested in TinyML. The idea of deploying machine learning models on resource-constrained devices and optimizing them for edge applications is both daunting and interesting to me. In the future, I hope to contribute by developing more efficient AI models and robotic systems that can operate seamlessly in real time and with minimal energy consumption. I’m really passionate about the intersection of machine learning, robotics, and optimization, and I see this as the key to unlocking new possibilities in various industries. I've been reading your Machine Learning Systems book, and I must say that it's quite informative and useful. Your focus on the practical aspects of ML deployment and system optimization, which are frequently disregarded in other resources, fills a critical need. Thank you for creating such a useful resource! I'm looking forward to networking with everyone and learning more about ML systems. I’m eager to learn from your expertise and insights as well as from my fellow students who share my passion for machine learning and robotics. I will be happy to connect here: |
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Thank you for reaching out! I'm always excited to see students engaging with this resource. You are the reason I feel inspired to keep developing it. I remember being deeply motivated to study computer architecture as a student because of Hennessy and Patterson's textbook on computer organization and advanced computer architecture. If you are interested in TinyML -- checkout tinyML on HarvardX. There are many other resources I can recommend, but this is a great starting point! You can take it for free and if you need a kit for Course 3 let me know. |
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Hello Everyone, 👋 I'm Poojak Patel, a student at Illinois Mathematics and Science Academy. Since a young age, I have wondered how cars operate, which has grown into a broader interest in how machine learning could be used to enhance autonomous systems. Currently, I am interested in how we can create autonomous systems using machine learning that can help robots learn in real time. The idea that machines can both react to what's happening around them while learning from their experiences just like how a person would is fascinating. Also, after reading more about TinyML in Professor Reddi’s Machine Learning Systems book, I have been intrigued about its potential as it allows machine learning models to run on small devices with low power. I think, just like machine learning, TinyML can lead to more innovative solutions in the future. I am excited about the possibilities that this kind of technology could bring and how it would be able to handle current existing challenges, making a difference in people's lives. I would love to connect with others who are also interested in machine learning, robotics, or any related topics. Feel free to reach out to me by email ([email protected]) and see my past projects on GitHub (github.com/PatelPoojak). Looking forward to engaging with all of you in this community! Best Regards, |
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Hi Poojak 👋 Nice interests. Embodied AI is expected to be a significant topic in the coming years. I believe the next major challenge will involve how language models, like ChatGPT, can begin interacting with physical sensors in the real world. This interaction will provide them with a wealth of new information and data to process. Therefore, I genuinely think we are only at the tip of the iceberg. |
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Hi everyone! I’m Mimi, and I currently work in helpdesk but am aiming to transition into software development. I'm starting with front-end development through a Meta course and am really enjoying learning the basics. I'm eager to expand my skills and learn from this community, so any tips or advice would be greatly appreciated as I make this career shift! I would love to connect! |
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Hello and welcome! A general word of advice -- it's important to complement your knowledge with AI tools, as they will play a major role as a copilot in assisting us with programming tasks. Therefore, becoming proficient with these tools while still learning the fundamentals (which is equally important) will be essential for all of us moving forward. |
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Thank you for the warm welcome and the advice! I completely agree that AI tools are becoming an essential part of programming. I'll definitely look into learning more about them while continuing to strengthen my fundamentals. It’s exciting to see how they can complement our skills and enhance our efficiency! Any suggestions or resources I can reference to start learning about AI tools? |
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Hi all! My name's Ryan. I'm currently working at Apple doing cloud/backend engineering with a focus now on testing and system integration. No practical ML experience at this time. Working at Apple has inspired a data-privacy-centric view in me. That, combined with an increasing personal desire to spend less time on socially connected, networked devices has spurred an idea in me which involves using federated machine learning on a dis-connected personal device. My desire is to create a simple AI assistant that is vocally interacted with (no screen) and dis-connected from the internet (on-device learning with data privacy). Soon after I had this thought, I came across this book and thought it would be a great way to dive into ML, especially after hearing about TinyML and its relevance to the on-device learning I want to do. I've never worked with hardware, so this will be a fun and new challenge. Fun fact: Another passion project of mine is electronic music production. |
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Hi everyone, I’m Danjie, an undergraduate at University of Toronto. My first exposure to ML systems came when I was trying to integrate Apple’s Neural Engine with a personal project. I managed to get BERT running on my M1 laptop (with some reshaping and some other steps that I don’t really remember). The results were incredible, the Neural Engine achieved seven times the throughput of the M1’s GPU while consuming just 5 watts of power! On top of that, I have a close friend working at Annapurna Labs in Toronto on AWS’s Inferentia service. He told me they were working on getting Llama3 405b to run on Inferentia chips. It’s exciting to see how ASICs are rapidly gaining traction in the industry and generating millions in revenue for AWS. I’m particularly passionate about the co-design of hardware and software for AI acceleration. My background includes experience in computer architecture, assembly language, and high-level machine learning with PyTorch. This background allows me to see that there’s immense potential to improve efficiency across all kinds of applications, though I’ve often struggled to find resources on this topic. Thanks so much for the course! All the best, |
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Hi my name Is Tabina Hussain, I am a current high school student at Nikola Tesla STEM High School with a focus pursuing Mechanical & AI engineering. My current specialized focus is making cars more efficient and environmentally friendly. To get closer to this goal I’ve started researching energy efficiency aerodynamics and materials, like for example improved sensors that use AI to calculate the most optimizing solutions. This could push automotive technology forward. Making solutions to these types of issues encourages me to participate in the future of transportation. Deeper in that topic, something that intrigues me that I found in the MLSYS Book was the section on AI training due to its incorporation in EV sensors. I hope to be a part of the evolution of this principle. So excited to be an apart of this community, feel free to reach out to me at [email protected] for anything Mechanical or AI related. Always ready for involvement! Many Thanks, |
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Hi everyone! I'm Avinash Krishna, in the final year of my Master of Technology in Electrical and Computer Engineering at IIT Jodhpur, India. My journey in technology has taken me through diverse areas, from machine learning and AI, cloud computing and network security. I've worked on research projects at IIT Delhi, IIT Dharwad, and IIT Jodhpur, applying AI/ML, 6G communication, 5G network slicing, and security systems. My interest in ML systems began when I saw how AI can revolutionize real-world applications—AI, ML, and CV for autonomus system, Industry 4.0, improving the reliability of 6G networks, and enhancing security with fewsshot learning based concealed object detection. The ability of ML models to find patterns, make predictions, and automate complex decisions fascinates me. Few-shot learning in AI/ML is something that truly excites me! I've worked on few-shot learning-based concealed object detection using YOLO, ResNet, and GANs in sub-terahertz security imaging. The potential of ML systems to learn from minimal data and adapt quickly to new tasks is incredibly powerful, especially for security, healthcare, and automation applications. I'm looking forward to talking with others. |
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Hi everyone! I’m Tadiwos, an undergrad at KAIST, double majoring in EE and CS. I’m interested in ML systems, particularly in efficient AI—accelerating inference and hardware-software co-design. My research focuses on systems for ML and AI accelerator architectures, with a focus on optimizing inference performance. My interest in ML systems was sparked by the realization that while AI models continue to grow in complexity, their impact hinges on the efficiency of the underlying hardware and system optimizations. I've worked on inference acceleration, GPU performance analysis, and algorithm-hardware co-design strategies, particularly in areas like tensor computation efficiency and distributed inference. |
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I am Aryaa Gargatti, a Bachelor of Engineering student in Electrical and Electronics Engineering at KLS Gogte Institute of Technology. My academic interests lie at the intersection of machine learning (ML) and electronics, where AI-driven technologies can revolutionize embedded systems, automation, power electronics, and robotics. I am particularly fascinated by how ML enhances predictive maintenance in power systems, optimizes energy efficiency, and improves decision-making in control systems. The ability of AI to analyze vast datasets, detect patterns, and enable smart automation aligns perfectly with my passion for innovation in electronics and intelligent systems. I aim to integrate ML into embedded systems to create self-learning devices that adapt to their environments, making industries smarter and more efficient. Beyond academics, I hold leadership positions as the Chair of IEEE PES and Treasurer of IEEE WIE, where I organize research-driven initiatives. I am also an avid basketball player with aspirations of leading my team. My drive to bridge the gap between hardware and AI-powered solutions motivates me to pursue research and hands-on projects that push the boundaries of smart electronics and automation. |
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Hello everyone! My name is Renan Michel, and I am a last-year undergraduate student in Control and Automation Engineering at the Federal Institute of Pará (IFPA-Belém). My passion lies in applied robotics, particularly in integrating Machine Learning into robotic systems to enhance autonomy, adaptability, and real-time decision-making. I am deeply fascinated by how intelligent algorithms can empower robots to solve complex real-world challenges, from industrial automation to assistive technologies. What sparked my interest in ML systems:My interest in ML systems was sparked by my fascination with how intelligent algorithms can transform the way machines interact with the world. Machine Learning is revolutionizing countless industries, from healthcare to finance, by enabling data-driven decision-making and automation. ML systems topics that really gets me excited:
The intersection of robotics and ML captivates me. I am particularly interested in developing and optimizing algorithms that enable robots to perceive, learn, and interact more effectively with their environment. Whether through reinforcement learning for robotic control, sensor fusion for real-time decision-making, or TinyML for efficient edge computing in autonomous systems, I am eager to explore ways to push the boundaries of intelligent robotics.
I am also highly interested in evaluating and optimizing ML models for resource-constrained devices. The challenge of balancing accuracy, efficiency, and real-time performance in TinyML models excites me, particularly in robotic applications where computational constraints are a key concern. Developing systematic benchmarking methodologies to assess model performance across different hardware platforms is another area I am keen to explore.
Beyond robotics, I am enthusiastic about deploying AI models on edge devices for autonomous applications. From industrial process automation to smart IoT solutions, I see immense potential in leveraging efficient ML techniques to enable intelligent, self-adaptive systems that operate reliably in dynamic environments. I am looking forward to great discussions and collaborations ahead in the community Find me on LinkedIn: www.linkedin.com/in/renanmichel |
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Hi everyone! My name is Wei-Chung (Wells) Lu, and I’m currently a master's student in Electrical and Computer Engineering at UCSB. I was an AI Engineer for 2 years. During that time, I did a lot of programming models and training work using TensorFlow and PyTorch. Then, I transferred these models through CoreML or Snapdragon SDK to implement them on mobile device chips. I was also responsible for developing data pipelines and recommendation systems on the Google Cloud Platform and innovatively introduced MLOps systems for our company and clients. Now, I am fascinated by ML systems, especially those that strengthen model deployment to enhance real-world problem-solving by accelerating and operating AI inference. My research interests include AIoT, MLOps, AI runtime systems, and memory strategies. I welcome everyone to discuss with me in this community or connect to my LinkedIn. |
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Hi everyone! I’m Li Tz Lung, a Mechanical Engineering undergraduate from Nanyang Technological University, Singapore. My journey into ML systems started through robotics — I serve as the Deputy Team Lead of NTU’s RobotX Team, where we build Autonomous Maritime Systems (ASVs + UASs) for real-world deployment and international competitions. Working on embedded autonomy, perception, and sensor fusion under hardware constraints made me realise how critical system-level thinking is when bringing ML into the physical world. What excites me most about ML systems is the challenge of enabling efficient, real-time learning on edge devices — especially in multi-agent settings. The idea that a fleet of autonomous robots can learn from their own experiences and share insights with one another, all while operating under bandwidth, power, and compute limits, is both fascinating and deeply practical. It combines my passion for robotics, systems engineering, and the potential of machine learning to reshape how autonomous agents adapt and collaborate in uncertain environments. LinkedIn: https://www.linkedin.com/in/litzlung/ |
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Hello sir, My name is M.Rohit, and I am a research student at the Indian Institute of Technology Madras,working on embedded machine learning. When I first started approaching on TinyML, I was captivated by the idea of running intelligent models directly on microcontrollers—seeing how much could be achieved within such tight memory(constrains) and power budgets really sparked my passion for ML systems.To date, I have implemented ML applications on NVIDIA Jetson Nano, nRF52840, and ESP32 platforms. I have been studying through the MLSysBook and found it valuable—Chapter 2’s deep dive into deployment paradigms (cloud, edge, mobile, tiny) helped me understanding of latency, privacy, and resource constraints more systematically. In particular, I’m keen to explore research questions around hybrid cloud–edge partitioning strategies: for example, how to dynamically decide which parts of a model to run locally versus in the cloud based on real-time network conditions, and usage of ML based upon the device and its constraints. I would be grateful for any guidance on exploring and working around these ideas, especially drawing on the insights . I look forward to learning from all of you and contributing my experiences as I intend to design robust, adaptive deployment pipelines for resource-constrained devices. Warm regards, |
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Hi everyone, My name is Rafa Aranda, I'm 45 years old and I'm from Spain. My approach now is that of a learner, because I can clearly see the potential of ML in diverse fields and how important it is to find a solid position regarding ML and AI. It's not my first encounter with ML, though. Although my background is on Industrial Engineering, or perhaps because of that, I've been very interested in the possibilities that ML and AI in general can bring to the industrial and the business fronts. I find this ML systems approach to be innovative, and most importantly necessary, since I consider that in order to make a truly creative use of the Al technologies of today one must fully understand not only the conceptual layer but also the hardware and software ones, which are what make up the material substrate upon which ML can be deployed. I'm very excited for having found this valuable "book", which is far more than that, as we are seeing here. Let me thank the author and the many contributors, and express a deep thank you to everyone here for sharing their objectives and vision for learning this material. All the best. |
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Hello everyone, I have a deep interest in AI for real-world impact, especially in areas like computer vision, robotics, and assistive technologies. I enjoy building intelligent systems that bridge perception and action, combining machine learning with embedded systems and robotics. My work on projects like real-time arrow detection for autonomous navigation (as part of the IRC 2025 team), and the biodegradable material classifier using Raspberry Pi and CNNs, has helped me apply theoretical concepts to physical systems. One of my most impactful experiences was developing an Indian Sign Language Translator during the IBM Z Datathon 2024, which combined NLP, computer vision, and 3D animation to enable communication between hearing and deaf communities. I am also passionate about financial forecasting, demonstrated through my work on a Predictive Market Analysis platform using ML algorithms. I'm always eager to explore new research directions, collaborate with like-minded peers, and contribute to applied AI innovations that address real-world challenges in areas like accessibility, sustainability, and automation. You can find me here: |
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Hi everyone! I’m Shubham Kumar Mandal, a student at IIT Kharagpur, currently pursuing my Dual Degree in Mechanical Engineering. While my core background is in engineering, I’ve always been drawn to the intersection of hardware and intelligent systems—especially how machine learning can be optimized and deployed on real-world devices. My interest in ML systems sparked when I started working on a wearable sound-detection watch for the hearing impaired. I realized that building an ML model is only half the challenge—the real complexity lies in making it run efficiently on a low-power, resource-constrained device. That experience opened my eyes to the world of ML systems. One topic that really excites me is TinyML , I love the idea of pushing intelligence to the edge and making devices smarter without needing cloud access. It’s amazing how much can be achieved with so little hardware, and I’m eager to explore more in this space! Looking forward to learning with all of you and building something impactful together |
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Dear Prof. @profvjreddi and the community, "Intelligence is the log of compute." – Satya Nadella, CEO, Microsoft With a profound passion for exploring the dynamic intersection between computer architecture, hardware-software co-design, chiplets, AI, and reinforcement learning, I am Arnav Shukla Currently pursuing Bachelor’s in Electronics and Communications Engineering at IIIT-Delhi. I aim to achieve the student mindset that is both inventive and deeply analytical. In my research engagements, I am tackling critical AI workload bottlenecks by designing a novel Chiplet-architecture, emphasizing inter-chiplet communication efficiency. I have experience with analog layout optimization, fine-tuning large & RL for post-training language models (LLMs), Conditional GAN-based text-to-image synthesis and a deep reinforcement learning framework for VLSI chip placement and routing through my internship and projects. In my academic journey, I've aimed to move beyond traditional disciplinary boundaries. By engaging with both hardware and software perspectives and staying open to new paradigms and real-world systems, I've tried to build a more holistic understanding of ML systems. I desire innovative mindset, practical experience and never refrain from learning and new things. Looking forward to learn and collaborate with all. |
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Hello everyone! 👋 I'm Durgesh Khade, an third year undergraduate student in IT Engineering at Pune Institute of Computer Technology (PICT), India. My passion lies at the intersection of machine learning systems and real-world engineering — building AI infrastructure that not only works in theory, but scales, adapts, and performs reliably under real-world constraints. Over the past year, I’ve worked on projects that sit at the crossroads of AI, systems engineering, and national-scale problem-solving. As an SDE Intern and Group Leader during a year-long internship with TASL, I contributed to the Indian Navy’s CTS-71 project, translating complex defense requirements into scalable and secure software. My role spanned full-stack development, AI model deployment, and cloud-native systems — building production-grade tools under high-stakes environments. Why ML Systems? I’m deeply curious about what makes models usable and efficient at scale — from deployment pipelines and intelligent scheduling to hardware-aware optimizations and resource-constrained inference. These are the invisible layers that turn ideas into impact, and I’m eager to dive deeper, learn from this community, and contribute meaningfully. Looking forward to connecting, learning, and building together! |
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Hi everyone, I’m Varun. I recently finished my Master’s in Computer Science at the University of South Florida, where I worked on machine learning projects in healthcare, including deploying AI models on low-power devices. I got into ML systems when I realized that getting a model to work in the real world is a completely different challenge from training it in a lab. I’m especially interested in edge AI for safety-critical applications, where every millisecond and bit of memory matters. Excited to be here, learn from all of you, and share ideas about making ML models faster, smaller, and more reliable in the real world. |
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Hi everyone! 👋
I’m Prof. Vijay Janapa Reddi, the author and editor of this resource, and I’m passionate about making foundational technologies like ML/AI accessible to everyone. Along with my students and TAs, I created this resource because we noticed a significant gap: while there are countless books and materials on deep learning theory and concepts, there’s a shortage of resources focused on the ML systems side.
If ML algorithm developers are like astronauts, ML systems engineers are the rocket scientists and mission control specialists who get them to space and keep the mission on track. 🚀 📡
The critical aspects of ML—optimizing models for specific hardware, deploying at scale, and ensuring efficiency and reliability—are often overlooked. These elements transform ML models into real-world applications. Yet, many practitioners struggle due to a lack of available knowledge, not interest.
That’s a little bit about me and my passion.
Now, I’d love to get to know you—our readers and fellow enthusiasts!
Please introduce yourself below and share:
Whether you’re just getting started or have years of experience—your perspective is important to us. Let’s connect, share our insights, and build a vibrant community around machine learning systems together. 🚀
Sincerely,
vj.
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