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Building a Media Understanding Platform for ML Innovations

Netflix Tech

Earlier we shared the details of one of these algorithms , introduced how our platform team is evolving the media-specific machine learning ecosystem , and discussed how data from these algorithms gets stored in our annotation service. We build creator tooling to enable these colleagues to focus their time and energy on creativity.

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Container Runtime: GPU Training & Inference with Snowflake Notebooks

Snowflake

Build cost-effective, scalable and flexible ML models in Snowflake Many enterprises are already using Container Runtime to cost-effectively build advanced ML use cases with the easy access to GPUs. CHG builds and productionizes its end-to-end ML models in Snowflake ML. With over $5.5 See this quickstart to learn more.

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Why Data Capabilities Follow Up a Digital Transformation

Team Data Science

Going further, when a restaurant creates a digital channel for its customers to order food online, it is not only digitizing information. It was mainly a "product first, customers second" mentality of building products and services. This is digitalization in the making [ , 7 ]. 22 , , 23 , , 24 , , 25 ].

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Data News — Week 24.07

Christophe Blefari

ByteDance boximator, create motion on images — Boximator is a friendly method to instruct generative algorithms with boxes. This is crazy how Google feels outdated when you look at smaller AI companies in term of hype or magic they are able to build. ByteDance is the company behinds TikTok. More details on Twitter.

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Lifecycle of a Successful ML Product: Reducing Dasher Wait Times

DoorDash Engineering

Building an ML-powered delivery platform like DoorDash is a complex undertaking. Once we identify an opportunity, we usually start small by testing the ML hypothesis using a heuristic before deciding to invest into building out the ML models. If the initial heuristic works, we build out and replace the heuristic with the ML models.

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How Our Paths Brought Us to Data and Netflix

Netflix Tech

I bring my breadth of big data tools and technologies while Julie has been building statistical models for the past decade. Outside of work, we share a love of good food and coffee, exchanging tips on making espresso. Chris] There’s a lot of things to consider when we roll out a new compression algorithm.

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Predicting the Generative AI Revolution Requires Learning From Our Past

Snowflake

Infrastructure = data Products = algorithms If data is the infrastructure in our equation and algorithms the product, what then is the X factor? This algorithmic thinking, at scale and across society, will launch a revolution. To understand how these gen AI models work, we need to understand how a generative algorithm works.

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