presentations scale-large-ml-models 
The Netflix TechBlog
MARCH 4, 2024
Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. With the integrated intelligence, we can properly meet the requirements of remediating different errors.
The Netflix TechBlog
JANUARY 25, 2023
Scalable Annotation Service — Marken by Varun Sekhri , Meenakshi Jindal Introduction At Netflix, we have hundreds of micro services each with its own data models or entities. Teams should be able to define their data model for annotation. This allows users to make add/remove properties in their data model.
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The Netflix TechBlog
OCTOBER 18, 2019
theme of the ML Platform meetup hosted at Netflix, Los Gatos on Sep 12, 2019. Broadly speaking, these approaches can be seen as a stepping stone to full-on Reinforcement Learning (RL) with closed-loop, on-policy evaluation and model objectives tied to reward functions. He described a simple Policy API that models the Slate tasks.
Adrian Cockcroft
NOVEMBER 26, 2023
Deploy a machine learning model that uses AutoGluon binary classification models to predict how weather features may result in unhealthy air quality. Open source geospatial AI/ML analysis, along with IoT-connected sensors, can provide near real-time data platforms built in the cloud and assist decision-making.
The Netflix TechBlog
OCTOBER 18, 2019
theme of the ML Platform meetup hosted at Netflix, Los Gatos on Sep 12, 2019. Broadly speaking, these approaches can be seen as a stepping stone to full-on Reinforcement Learning (RL) with closed-loop, on-policy evaluation and model objectives tied to reward functions. He described a simple Policy API that models the Slate tasks.
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).
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.
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