Emerging Architectures for LLM Applications | Andreessen Horowitz
There are many different ways to build with LLMs, including training models from scratch, fine-tuning open-source models, or using hosted APIs. The stack we’re showing here is based on in-context learning, which is the design pattern we’ve seen the majority of developers start with (and is only possible now with foundation models). The next section gives a brief explanation of this pattern; experi
CS 329S | Syllabus
Schedule & syllabus The lecture slides, notes, tutorials, and assignments will be posted online here as the course progresses. Lecture times are 3:15 - 4:45pm PST. All deadlines are at 11:59pm PST. This schedule is subject to change according to the pace of the class. See Past course for the last year's lectures. Date Description Materials Events
七声ニーナを支えるバックエンド技術 | BLOG - DeNA Engineering
データ統括部AI基盤部の竹村( @stakemura )です。本記事では、このたびリリースされた、自分の声をキャラクターの声に変換できるWebサービス VOICE AVATAR 七声ニーナ を支えるバックエンド技術についてお話しします。 本サービスはDelight Boardという部署横断型のプロジェクトにて、1000人を超える社員投票により自分の案がまさかの採択となったことがきっかけとなります。幸運にも、百戦錬磨のプロジェクトメンバーに助けられ今日のリリースを迎えましたが、採択当時は人脈も信用貯金も何もない入社一年目の思いつきにすぎず、言い出しっぺである自分の力不足によりタイトなスケジュールでの開発となってしまいました。本記事では、その限られた開発期間の中で、自分が何を考えて実装したかを中心にお伝えします。 サービングに求められる要件 七声ニーナの音声変換はブラウザから受け取った入力音声
How we grew from 0 to 4 million women on our fashion app, with a vertical machine learning approach
My name is Gabi (my bio), and I’m the CEO and co-founder of Chicisimo. We launched three years ago, our goal was to offer automated outfit advice. Today, with over 4 million women on the app, we want to share how our data and machine learning approach helped us grow. It’s been chaotic but it is now under control. Our thesis: Outfits & closets are the best assets to understand people’s taste. Under
Continuous Delivery for Machine Learning
Automating the end-to-end lifecycle of Machine Learning applications Machine Learning applications are becoming popular in our industry, however the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application. They are subject to change in three axis: the code itself, the model, and the data
Google AI Chief Jeff Dean’s ML System Architecture Blueprint
ML has revolutionized vision, speech and language understanding and is being applied in many other fields. That’s an extraordinary achievement in the tech’s short history and even more impressive considering there is still no dedicated ML hardware. Back in January, Google AI Chief and former head of Google Brain Jeff Dean co-published the paper A New Golden Age in Computer Architecture: Empowering
大規模データに対するデータサイエンスの進め方 #CWT2016
Cloudera World Tokyo 2016 有賀発表 データサイエンスを含めたチームづくりと、機械学習を活かしたプロダクトの作り方について話しました。
Machine Learning, Recommendation Systems, and Data Analysis at Cloud Academy | Amazon Web Services
AWS News Blog Machine Learning, Recommendation Systems, and Data Analysis at Cloud Academy In today’s guest post, Alex Casalboni and Giacomo Marinangeli of Cloud Academy discuss the design and development of their new Inspire system. — Jeff; Our Challenge Mixing technology and content has been our mission at Cloud Academy since the very early days. We are builders and we love technology, but we al
DSN 2014 Keynote: "Sibyl: A System for Large Scale Machine Learning at Google"
Large scale machine learning is playing an increasingly important role in improving the quality and monetization of Internet properties. A small number of techniques, such as regression, have proven to be widely applicable across Internet properties and applications. Sibyl is a research project that implements these primitives at scale and is widely used within Google. In this talk I will outline
Midwest.io 2014 - Building a Production Machine Learning Infrastructure - Josh Wills
This talk was given at Midwest.io 2014. Cloudera's Data Science Team has a simple mission: build an analytics infrastructure so awesome that it makes Google's Ads Quality Team seethe with jealousy. To that end, I'll give an overview of Cloudera's current data science tools, including Oryx and Spark for building and serving machine learning models, Gertrude for multivariate testing, and Impala for
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AI Helps Duolingo Personalize Language Learning
How a Two-Person Team (and their AI) Entered the Top 10 of the World’s Most Successful Funds
GitHub - chiphuyen/machine-learning-systems-design: A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"
Architecture for MLOps using TensorFlow Extended, Vertex AI Pipelines, and Cloud Build | Cloud Architecture Center | Google Cloud
Continuum: a platform for cost-aware low-latency continual learning | the morning paper
https://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
igt198d-schleier-smith.dvi
https://www.cidrdb.org/cidr2015/Papers/CIDR15_Paper19u.pdf
NIPS : Conferences : 2014 : Program : Software Engineering for Machine Learning
From the Lab to the Factory: Building a Production Machine Learning Infrastructure