The Netflix TechBlog

MARCH 2, 2021

Our experimentation and causal inference focused data scientists help shape business decisions, product innovations, and engineering improvements across our service. In this post, we discuss a day in the life of experimentation and causal inference data scientists at Netflix, interviewing some of our stunning colleagues along the way.

Analytics 207

Analytics C++ Innovation Engineering 207

All Things Distributed

DECEMBER 20, 2017

Marketers use big data and artificial intelligence to find out more about the future needs of their customers. If data take center stage then companies must learn how to create added value out of it – namely by combining the data they own with external data sources and by using modern, automated analytics processes.

Artificial Intelligence 79

Artificial Intelligence Social Media Logistics AWS 79

The Netflix TechBlog

FEBRUARY 16, 2021

However, with our rapid product innovation speed, the whole approach experienced significant challenges: Business Complexity: The existing SKU management solution was designed years ago when the engagement rules were simple? Rule owners are responsible for making sure the requirements match the rule definition and request context.

Mobile 209

Mobile Engineering Infrastructure Scalability 209

The Netflix TechBlog

MARCH 25, 2019

In the rest of this blog, we will a) touch on the complexity of Netflix cloud landscape, b) discuss lineage design goals, ingestion architecture and the corresponding data model, c) share the challenges we faced and the learnings we picked up along the way, and d) close it out with “what’s next” on this journey.

Infrastructure 219

Infrastructure Big Data Transportation Architecture 219

Highly Scalable

MARCH 10, 2015

More specifically, the article was inspired by three major case studies from Albert Heijn [KOK07], the largest supermarket chain in the Netherlands, Zara [CA12], an international apparel retailer, and RueLaLa [JH14], an innovative online fashion retailer. LE13] The Definitive Guide to Predictive Analytics Models for Marketing, D.

Retail 152

Retail C++ Analytics Metrics 152

The Netflix TechBlog

SEPTEMBER 10, 2019

Instead of relying on engineers to productionize scientific contributions, we’ve made a strategic bet to build an architecture that enables data scientists to easily contribute. The two main challenges with this approach are establishing an easy contribution framework and handling Netflix’s scale of data. This is an ongoing journey.

Metrics 215

Metrics Architecture Infrastructure Innovation 215