Infrastructure Software Engineering Storage Training 
The Netflix TechBlog
NOVEMBER 29, 2023
A vital aspect of such development is subjective testing with HDR encodes in order to generate training data. In spite of reaching higher qualities than the fixed ladder, the HDR-DO ladder, on average, occupies only 58% of the storage space compared to fixed-bitrate ladder. Krasula, A. Choudhury, S. Malfait, A.
High Scalability
JANUARY 11, 2022
FUN FACT : In this talk , Rodrigo Schmidt, director of engineering at Instagram talks about the different challenges they have faced in scaling the data infrastructure at Instagram. After that, the post gets added to the feed of all the followers in the columnar data storage. System Components. Fetching User Feed. Optimization.
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The Morning Paper
FEBRUARY 18, 2020
Many of the software engineering discipline and controls need to be brought over into an ML context. The following chart breaks down features in three main areas: training and auditing, serving and deployment, and data management, across six systems. But model inference migrating into the DBMS is a bolder prediction.
Brendan Gregg
APRIL 30, 2018
LISA originally stood for "Large Installation System Administration," where "large" meant systems with more than a gigabyte of storage, or with more than 100 users. Submit #lisa18 talk, training, panel, and demo proposals by May 24 [link]. Have something to say on the present & future of #ops? Hope to see you in Nashville!
Brendan Gregg
APRIL 29, 2018
LISA originally stood for "Large Installation System Administration," where "large" meant systems with more than a gigabyte of storage, or with more than 100 users. Submit #lisa18 talk, training, panel, and demo proposals by May 24 [link]. Have something to say on the present & future of #ops? Hope to see you in Nashville!
The Netflix TechBlog
APRIL 26, 2022
We built Axion primarily to remove any training-serving skew and make offline experimentation faster. We make sure there is no training/serving skew by using the same data and the code for online and offline feature generation. Our machine learning models train on several weeks of data.
The Netflix TechBlog
DECEMBER 3, 2019
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?” mainly because of mundane reasons related to software engineering. like they would do in a Jupyter notebook.
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