The Morning Paper

FEBRUARY 20, 2020

Extending relational query processing with ML inference , Karanasos, CIDR’10. Given the desire to keep enterprise data within the database, and to treat models as data also, the question is can we do inference in the database with acceptable performance? In Raven, model pipelines are packaged using MLflow.

Processing 72

Processing Hardware Database Azure 72

The Netflix TechBlog

JUNE 4, 2019

As it turns out, for the large majority of Netflix use cases, its performance is far from optimal. This service then queries a local GBRT model (retrained every couple of hours on weeks of data collected from the whole Titus platform) predicting the P95 CPU usage of each container in the coming 10 minutes (conditional quantile regression).

Cache 251

Cache Latency Airlines Logistics 251

Adrian Cockcroft

JANUARY 20, 2023

Custom HPC optimized CPUs and GPUs will become the mainstream in the next few years, with chiplets based on the ARM and RISC-V pattern libraries and tooling, with competition between cloud providers, startups and the large systems vendors. Next generation architectures will use CXL3.0 switch from XConn shown in the Expo.

Architecture 52

Architecture Latency Benchmarking AWS 52

The Morning Paper

SEPTEMBER 12, 2019

To make that vision of a one-stop shop for all of an organisation’s data needs come true, we need to be able to handle the most demanding large scale machine learning tasks – those requiring distributed learning. Except there’s one glaring omission from that list: handling all of the machine learning use cases.

Network 44

Network Database Programming Hardware 44

Alex Russell

APRIL 29, 2021

Engines add features at different rates, and the Confluence graphs illuminate both the absolute scale of differences and the pace at which releases add new features. CSS's new Typed Object Model ) may feature large expansions in API surface area for relatively little increase in power available to the developer. CSS Snap Points.

Media 145

Media Games Education Engineering 145

The Netflix TechBlog

FEBRUARY 13, 2023

We have been leveraging machine learning (ML) models to personalize artwork and to help our creatives create promotional content efficiently. Our goal in building a media-focused ML infrastructure is to reduce the time from ideation to productization for our media ML practitioners.

Media 290

Media Storage Infrastructure Systems 290

The Morning Paper

JULY 9, 2019

Three key checklists and remedies for trustworthy analysis of online controlled experiments at scale Fabijan et al., Last time out we looked at machine learning at Microsoft , where we learned among other things that using an online controlled experiment (OCE) approach to rolling out changes to ML-centric software is important.

Metrics 42

Metrics Design Testing Software 42

O'Reilly

SEPTEMBER 19, 2023

Across one story arc, they said, we often see several structural evolutions —smaller-scale versions of that wider phenomenon. Hadoop’s value—being able to crunch large datasets—often paled in comparison to its costs. Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools.

Hardware 99

Hardware Storage Big Data Blockchain 99

O'Reilly

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. What does a modern technology stack for streamlined ML processes look like?

DevOps 138

DevOps Software Engineering Infrastructure Open Source 138

Smashing Magazine

NOVEMBER 22, 2021

Large preview ). This high rate of growth, coupled with the current scale and diversity of offerings presents a huge challenge when setting out to improve performance. There are more challenges for us in addition to scale and diversity: Retaining existing design and behavior. Large preview ).

Website 126

Website Performance Google Media 126

The Netflix TechBlog

OCTOBER 16, 2019

One example displaying the need for dataset propagation: at any given time Netflix runs a very large number of A/B tests. Rather than each team interested in consuming the model having to build in fallbacks to degrade gracefully, there is a lot of value in centralizing the work to allow multiple teams to leverage a single team’s effort.

Cache 187

Cache Architecture Metrics Java 187

All Things Distributed

NOVEMBER 30, 2016

From the early days of Amazon, Machine learning (ML) has played a critical role in the value we bring to our customers. There are many more application areas where we use ML extensively: search, autonomous drones, robotics in fulfillment centers, text processing and speech recognition (such as in Alexa) etc. Amazon Lex.

AWS 165

AWS Lambda Artificial Intelligence Mobile 165