Remove AWS Remove Design Remove Infrastructure Remove Training
article thumbnail

Scale your enterprise cloud environment with enhanced AI-powered observability of all AWS services

Dynatrace

Missing operational insights, lack of context, and limited understanding of cloud service dependencies making it almost impossible to find the root cause of customer-facing application issues or underlying infrastructure problems. Achieve full observability of all AWS services. AWS AppSync. AWS CloudHSM. AWS Chatbot.

AWS 262
article thumbnail

Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding.

Systems 226
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Sustainability Talks and Updates from AWS re:Invent 2023

Adrian Cockcroft

The Pantheon in Rome — Extremely sustainable architecture — photo by Adrian I wrote a medium post after AWS re:Invent 2022 summarizing the (lack of) news and all the talks related to Sustainability. This includes providing the efficient, resilient services AWS customers expect, while minimizing their environmental footprint.

AWS 52
article thumbnail

Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

About two years ago, we, at our newly formed Machine Learning Infrastructure team started asking our data scientists a question: “What is the hardest thing for you as a data scientist at Netflix?” Our job as a Machine Learning Infrastructure team would therefore not be mainly about enabling new technical feats.

article thumbnail

Evolution of ML Fact Store

The Netflix TechBlog

We built Axion primarily to remove any training-serving skew and make offline experimentation faster. We will share how its design has evolved over the years and the lessons learned while building it. To understand Axion’s design, we need to know the various components that interact with it.

Storage 187
article thumbnail

Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

About two years ago, we, at our newly formed Machine Learning Infrastructure team started asking our data scientists a question: “What is the hardest thing for you as a data scientist at Netflix?” Our job as a Machine Learning Infrastructure team would therefore not be mainly about enabling new technical feats.

article thumbnail

Lerner?—?using RL agents for test case scheduling

The Netflix TechBlog

The device ecosystem is rich with partners ranging from Silicon-on-Chip (SoC) manufacturers, Original Design Manufacturer (ODM) and Original Equipment Manufacturer (OEM) vendors. Solving the above problems could help Netflix and our Partners save time and money during the entire lifecycle of device design, build, test, and certification.

Testing 163