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The Sisu Blog

The best way to inform decisions with data is to never stop asking why. Tune in to the latest in analytics, data, and machine learning news on the Sisu Blog.

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Sisu Launches with $52.5M from NEA and Andreessen Horowitz

Sisu launches its analytics platform and announces $52.5M Series B from NEA, Andreessen Horowitz, and Green Bay Ventures.

O’Reilly Data Show:
Peter Bailis on Machine Learning and Operational Analytics

Sisu CEO Peter Bailis joins Ben Lorica on the O’Reilly Data Show for a conversation about how machine learning can improve enterprise analytics and BI.

Sisu at the O’Reilly AI Conference San Jose:
Usable Machine Learning

Sisu is at the 2019 O’Reilly AI conference in San Jose. Don’t miss Peter Bailis’s presentation on Usable Machine Learning in the AI Business Summit.

What Dog Days of Summer?
Get the Facts on our Incredible Sisu Interns

We sat down with our Sisu interns at the end of the summer to discuss their experiences and advice.

Why Everyone Needs a Dedicated Analyst Team

With the investment we’re making in collecting structured data, everyone could benefit from a dedicated analyst team. But almost nobody does. Sisu can help

Three Design Principles for Operational Analytics

With more data, we need faster tools for analysis. At Sisu, we've identified three key design principles for making analytics accessible and understandable.

Three Inconvenient Truths about the State of Enterprise ML

Last week at TieCON 2019, Sisu CEO Peter Bailis challenged conventional wisdom around enterprise ML with three principles for making ML accessible to all.

Three Takeaways from SysML 2019:
More Data, Better Tools, Accessible Models

Reflecting on the research and discussions at SysML 2019, program committee member Peter Bailis shares his observations on three emerging trends.

Lightning-fast Schema Inference in Redshift

In this post, we’ll show you a simple trick we’ve used to improve schema inference performance by over 100x in Redshift.

Towards Off-the-Shelf Machine Learning

Achieving high accuracy in machine learning often requires extremely large amounts of training data – but just how much training data is required?

What to do with Big Data?
Making ML useful is a platform problem

With today’s excitement about making AI and machine learning useful, it’s easy to forget that we were only recently enamored with Big Data and its promise.

Systems for Software 2.0:
A Lesson from Web 1.0

Our modern stack is built around a deterministic, precise model of computation. But statistical forms of correctness are the new normal for software 2.0.

Introducing Sisu

Introducing Sisu: we're building a new kind of software to help people use data to make better decisions. We’re building the new analytics stack.