The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 16 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Bloom: Netflix couldn't deploy its $1M prize algorithm due to complexity
“They paid a million dollar bounty, and they wound up looking at the code, and there were hundreds of separate models that were then boosted together, and they said there's no way we can do it.”
Spencer Kimball: Netflix operates hundreds of CockroachDB clusters
“They have hundreds of cockroach clusters already.”
Borgman: Netflix uses Parquet for essentially all of its infrastructure
“Ah, Parquet, Netflix uses that for all of their infrastructure essentially.”
Netflix relies completely on implicit data rather than explicit user feedback
“At Netflix it was like pulling teeth to get the customer to tell you anything about themselves, right? They rely completely on implicit data.”
Retail churn is fuzzy and non-deterministic compared to subscription businesses
“And in the world of retail, this concept of churn is really fuzzy. So Netflix knows when their customers quit, you know, because you actually cancel. But a retailer, you know, your big retail brand, Amazon or something, and you had this customer who was orderi…”
ClickHouse counts Spotify, Netflix, Disney, and Uber as customers
“We are taking this very powerful analytical database that's used by companies like Spotify and Netflix and Disney and Uber and eBay and Cloudflare, and we're building a managed service in the cloud that we're calling ClickHouse Cloud.”
Chip Huyen: Netflix generates recommendation batches offline rather than in real time
“All these recommendations actually generate offline.”
Netflix data scientists are free to choose their own tools
“Netflix has this really interesting corporate culture of freedom and responsibility. Which means that our data science teams, they are essentially free to use whatever tooling that works best for them.”
Netflix drives ETL and ML pipelines using internal scheduler Meson
“We have a workflow scheduler called Mason. Mason, which is a program scheduler that's being used to drive all of our ETL as well as machine learning pipelines.”
Netflix's machine learning infrastructure team consists of six engineers
“My team has six engineers right now.”
Netflix uses open-source Titus for container orchestration
“In terms of compute our container orchestration platform is called Titus, which is yet another open source project.”
Netflix uses S3, Spark, Presto, and Snowflake for data querying
“We use SG as so like the storage layer for our data warehouse, and we use Spark, Presto, Snowflake as our query engines.”
Narkhede: Apache Kafka is used by thousands of companies worldwide
“Since we open sourced it, you know, roughly five years ago, Kafka is used in thousands of companies worldwide, from Uber, and LinkedIn, and Netflix, all the way to traditional enterprises like eBay, and PayPal, and Cisco, and Goldman Sachs.”
Bloom: Netflix and Google build fault tolerance to handle ML errors
“Netflix and Google, some of the best machine learning companies in the world, and these are their core products, and they still make mistakes. Yet these are not fatal mistakes. They've built fault tolerance into the machine learning.”