Ming Chen

🎉 Big congratulations to our incredible team (Haoyu Chen, Nick Greenquist,Yuxing Wang, Sridevi Maharaj, Ruiyang Wu and many more) for delivering several quarters-long impactful features on the Play Store! 🚀 We’re thrilled to have launched key updates like Search Home, Sports Moment, and WearOS recommendations, all designed to enhance personalization and help billions of users worldwide search and discover content they love. 🌍✨ Check out the Play Store Q3 updates: https://lnkd.in/dn5wx8Mi #GooglePlay #Personalization #Recommendation

74

1 Comment

Check out the latest in our "Debuggability" blog series! This blog is authored by Kevin Downey Jason Johl and me and talks about how we built a Debuggability Paved Road at Intuit using Ephemeral Containers and Argo Workflows. We'd love to hear your thoughts on the topic. Also, what tools do you build as-a-platform to help debug service, compute and network issues in Kubernetes at scale? #intuit #kubernetes #debug

23

Waymo is probably both more expensive and longer waits on average than Uber or Lyft today, but they are close enough, and rapidly getting better. These markets will flip to fully autonomous when the tech is ready.

2

You want to do Machine Learning on large scale Marketing solutions ? Check below 🚀

3

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

70

3 Comments

How you can make your LLM applications better!

5

"Build in primitives" -Andy Jassy, #Amazon CEO https://lnkd.in/gHMYt7NX Happen to take the time to read this year's Annual investor letter from Andy Jassy. Deep in 2nd half, it talks about how Amazon started thinking about "building in primitives" and how it has been game-changing for them and their customers. Probably old news for some, but eye-opening for me. Definitely a good read!

2

Come join us tomorrow in SF to learn more about the latest updates to run AI workloads on Google Cloud!

11

1 Comment

We launched structured data retrieval in Bedrock Knowledge Bases last week at re:Invent. While embeddings-based semantic search has worked wonders for unstructured data, we felt we need a new approach for structured data. We needed a way to translate customer natural language queries to an expressive query language that traditional databases already support. https://lnkd.in/eTPStNif #rag #text2sql #aws #bedrock

29

What does it feel like to be an engineering manager at Amazon: Since the RTO announcement it became popular to write shit about Amazon, but it's still one of the best companies around the world. A few weeks ago, Gilad Naor shared with the 'Leading Developers' readers 3 lessons from his time as an engineering manager at Amazon. Here is the first one, read the full article here: https://lnkd.in/dHBxv2Ha 𝟭. 𝗦𝗼𝗹𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝘄𝗶𝘁𝗵 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 My first meeting at Amazon was baffling. I entered a room with about a dozen Software Development Managers (SDMs) and Product Managers (PMs). Everyone sat in silence and looked at their laptops or the printed papers they had before them. It was my second day at Amazon, and I picked up one of the extra sheets and started reading. After those first ten awkward minutes, everyone finished reading and the meeting began. Why do Amazon meetings always start this way? This is a specific example of how Amazon approaches problem-solving. It is the "Good Intentions Don't Matter" approach. In a famous internal video, Jeff Bezos talks about why good intentions are irrelevant. When things don't work, it does not matter what people wanted. All that matters is fixing the problems with mechanisms. For example, many busy managers come to meetings without first reading the prep material. They all intended to read the 6-Pager before the meeting. They just got sidetracked by the latest emergency. Scolding the managers won't solve the problem. A mechanism will. This is why all Amazon meetings start with a study period. The remaining meeting time is then spent on high-quality decision-making. Meta solves the same problem by creating cultural norms. I can't count the number of times that I heard a senior leader cut off the presenter: "Stop, assume that everyone here already read the slides. Let's get to the point." This is a culture-first approach. I have seen this work well in some teams, and not work at all in other teams. At Amazon, the solution is part of the company's "operating system". ------------------------- Anton here - by now everyone is familiar with the 6-pager concept, but that "operating system" thinking can be used almost anywhere. Think about something annoying that your developers do, and how can you solve it. For example, I remember being annoyed that people don’t update the Jira tickets. Finally, after requesting multiple times, now we just take 20 seconds from the standup to update them.

56

8 Comments

I first joined Amazon in December of 2005. Even though I left for two years, my "Amaversary" still coincides with the end of the year and a natural moment for reflection. As I reached my 17 year mark this December, I also had the privilege of working with Amazon Careers to share some of my journey and thoughts about individual contributor technical leader and people manager career paths. In my ruminating, I put together far more content than could be used for a single article. So, I've decided to post here as a series of Q&As - in case others might find my observations, experiences, and perspectives interesting or useful. Below is the first of 6. 1. 𝘽𝙧𝙞𝙚𝙛𝙡𝙮 𝙨𝙝𝙖𝙧𝙚 𝙮𝙤𝙪𝙧 𝙘𝙖𝙧𝙚𝙚𝙧 𝙟𝙤𝙪𝙧𝙣𝙚𝙮; 𝙬𝙝𝙖𝙩 𝙢𝙤𝙩𝙞𝙫𝙖𝙩𝙚𝙙 𝙮𝙤𝙪 𝙩𝙤 𝙩𝙖𝙠𝙚 𝙤𝙣 𝙙𝙞𝙛𝙛𝙚𝙧𝙞𝙣𝙜 𝙘𝙖𝙧𝙚𝙚𝙧 𝙥𝙖𝙩𝙝𝙨, 𝙄𝘾 𝙫𝙨 𝙢𝙖𝙣𝙖𝙜𝙚𝙧? I started my career as a software engineer 25 years ago. I've worked for startups, enterprise companies, and 7 different organizations over 17 years and two tours at Amazon. I've written firmware for robotic camera mounts; built highly scalable distributed systems; designed restaurant marketing, ordering, logistics, and operations software; created automated test harnesses for mobile games; tested HD DVD encoding; helped launch grocery delivery to customers around the globe - just to name a few of the many cool things I've had the privilege to work on. I've been an entry-level software engineer, a technical lead of teams, a software manager on a couple of occasions, chief software architect reporting to a CTO, and (for the last few years) Amazon Principal Engineer directly supporting a VP with about 200 engineers within my remit. I first became a manager because I mistakenly thought it was the only way to continue growing as a leader, and in many ways I failed. I learned some very humbling lessons about leadership through that experience, which I used to be a better individual contributor technical leader on my path to Senior Engineer. My second time as manager was because my organization had a need that I could fill. I had grown enough as a leader that this time I was much more successful. I said yes to an opportunity that started with managing a small team through delivery of a complex and critical project. Once I grew a high performing team that delivered that initial project, I took on more scope and expanded into multiple teams. After a couple of years of success growing and investing in my teams so that we could deliver greater value to our customers and business, I decided to prioritize being close to my family. For me, at that time, it meant giving up my people management role and returning to the role of an individual contributor technical leader that supports a globally distributed organization. Even though I really enjoyed my time as a people manager, there is no better job for me than that of a Principal Engineer. #amazon #careerdevelopment #technicalleadership #peoplemanagement

91

3 Comments

Chip Huyen on Twitter today below. I 100% agree. Metric hygiene is system hygiene. LLM-Generated metrics require a higher standard of care than they're currently receiving, and much more scrutiny. Mind the compasses. 🐦  A big issue I see with AI systems is that people aren't spending enough time evaluating their evaluation pipeline. 1. Most teams use more than one metrics (3-7 metrics in general) to evaluate their applications, which is a good practice. However, very few are measuring the correlation between these metrics.If two metrics are perfectly correlated, you probably don't need both of them. If two metrics strongly disagree with each other, either this reveals something important about your system, or your metrics just aren't trustworthy.2. Many (I estimate 60 - 70%?) use AI to evaluate AI responses, with common criteria being conciseness, relevance, coherence, faithfulness, etc. I find AI-as-a-judge very promising, and expect to see more of this approach in the future.AI-as-a-judge scores aren’t deterministic the way classification F1 scores or accuracy are. They depend on the judge's model, the judge's prompt, and the use case. Many AI judges are good, but many are bad.Yet, very few are doing experiments to evaluate their AI judges. Are good responses given better scores? How reproducible the scores are -- if you ask the judge twice, do you get the same score? Is the judge's prompt optimal? Some aren’t even aware of the prompts their applications are using, because they use prompts created by eval tools or by other teams.Also fun fact I learned from a (small) poll yesterday: some teams are spending more money on evaluating responses than on generating responses 🤯 https://lnkd.in/gYnTRZW3

2

I recently had the opportunity to attend 3 major AI events in the last 30 days, where I gained valuable insights into the future of artificial intelligence. Here are some key takeaways: Amazon AI Day: - Amazon CTO (Werner Vogels) highlighted the potential of generative AI and large language models in driving innovation and societal impact, particularly in healthcare and sustainability. - Explored how generative AI is revolutionizing development processes and transforming businesses across industries. MagicBall - AI Week: - Showcased advancements in AI infrastructure by over 24 startups from Bengaluru, emphasizing the city's significance in AI development. - Engaged in discussions on responsible AI development and ethical considerations for the future. Microsoft AI Day: - Microsoft's Innovation Hub India CTO (Sandeep Alur) discussed AI's critical role in digital transformation and the company's commitment to ethical AI development. - Showcased the revolutionary potential of Generative AI and its integration into Microsoft Azure. These events, combined with insights from the ISB CTO program, emphasize that AI is here to create new opportunities and possibilities, not to take away jobs. Embracing a growth mindset is crucial to thrive in the evolving AI landscape. The AI revolution has just begun, and it's essential to adapt and innovate for a sustainable future. #GenAI #AI #BrightFuture

29

6 Comments

From my conversations, I'm noticing different trends between how AI engineers deploy evaluation models (model as a judge) in production monitoring vs application testing. 💡 Value - Monitoring: provides real time alerts of performance or used to preemptively block problematic outputs from surfacing to the user -Testing: regression testing applications to establish baseline performance before pushing to deployment ⚡ Speed - Monitoring: near real time, requires extremely fast token processing - Testing: batch, async, often processing large test sets, lower token processing speed required 🎯 Accuracy - Monitoring: overindex on false positives, better to flag something as problematic and be wrong than to let something slip between the cracks - Testing: high correlation with human graders, be as accurate and robust as possible 💰 Cost - Monitoring: prefer cheaper models, otherwise the additional inference cost per generation will add up exponentially - Testing: willing to take on higher costs, calculation is accurate testing leads to better performing outputs 🛑 Risk - Monitoring: if your monitoring judge gets something wrong, there is a high consequence because it directly impacts the end user - Testing: if your testing judge gets something wrong, async review gives some potential to remediate the issue At the moment, it seems using model as a judge for testing is a little more popular due to the high cost constraints of constant monitoring, but that is subject to change as more specialized models come out. As the industry matures, I strongly believe we're gonna see specialization between models for monitoring and models for testing. If you're currently using model as a judge techniques, would love to chat more! Let me know what you think. Twitter Handle: https://lnkd.in/eAsQvtiu

26

Want a preview of what I'm going to be talking about in my keynote for RubyConf? You can catch a glimpse of it here!

10

Thats the exact explanation I gave to a group of investors last week. (… it’s just Precision/Recall, attributes vectorization as “embedding”, K-Nearest Neighbor, User Search Intent, DAG, ML algorithms, etc all over again …). LOL and they asked what’s new this time around. And I was like not much, ML is still the backbone, and AI is the inference and actions based on the model. It works better when there is a feedback loop in a closed system. But the current state of the art like ChatGPT is based on scraping the Internet (garbage in, garbage out). Kinda like early search engines. What is a better analogy is that NVIDIA/Snowflake etc are selling tools, aka pickaxes to prospectors. The gold is up to you to find. And the opportunity right now is to monetize industry specific (healthcare, travel, financial services) clean data to build models with (aka a treasure map for the prospectors). From an enterprise perspective, what’s out there today aka generative AI isn’t very useful. Too much hype. To build useful engines will require an effort similar to search revolution 2 decades ago. From

16

3 Comments

So excited to see our team's work featured here! These are fundamentals directions for recommender systems, essential for advancing the field. Excited to see how others will build on this and drive deeper insights. https://lnkd.in/gkqpq7HS

134

4 Comments

How good are Trainium and Inference clusters for online serving of production traffic? Check this blog from Trishul Chilimbi on how Rufus responses are powered with high throughput and resiliency. #Trainium #Neuron #AWS Amazon Web Services (AWS)

6