How Livefyre Doubled its Pace of Analysis

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How doubled its pace of analysis with

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Company Profile

built a pla*orm that enables businesses to engage consumers through real-‐5me conversa5on, content, social cura5on and ads.

As more conversations move online, social media has become a prized target for spammers. Livefyre specializes in sorting social chatter in real time, filtering spam and delivering only the most relevant content to its customers.

Livefyre’s Product Engineering team uses sophisticated machine learning techniques to build and evolve detection models, which they must constantly improve in the race to keep ahead of spammers.

Key Problems• Computationally demanding

machine learning tasks bottlenecked on users' desktops

• Wasted time finding and reproducing past work

• No good ways to share work

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Eugene Scherba is a Sr. Data Scientist on the Product Engineering team. Eugene wanted a platform to scale up Livefyre’s model development while letting

the team track, reproduce, and collaborate on work.

Livefyre’s existing tools had several shortcomings:

Data scien5sts were constrained by their hardware

They wasted 5me finding and reproducing past work.

They shared their work inefficiently, resul5ng in unnecessary overhead and missing shared context among their team.

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After a couple months with Domino, Livefyre has doubled its pace of progress on its models, with Domino analysts can run more experiments in parallel, while saving time they would have wasted reproducing past work, and

creating more shared context on the team.

Lets take a look at the IMPACT……

“Domino is the only platform designed to address this sort of

complex, modern analytical workflow.”

Eugene Scherba, Senior Data Scientist

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Livefyre is now running more experiments, faster

Before Domino, Livefyre’s computationally intensive machine learning tasks were limited by the power of analysts’ desktops and other internally managed compute resources.

Domino lets Livefyre run its models on powerful cloud hardware, with a single command and without any changes to their existing code. Each “run” on Domino takes place on whatever hardware Livefyre chooses, up to machines with 32 cores and 250GB of memory.

Livefyre data scientists can start as many “runs” as they want — Domino automatically spins up a new machine for each one — giving Livefyre “one-click” access to unlimited compute resources, and allowing them to monitor the progress of their in-flight runs through Domino’s web interface.

Instead of trying one experiment at a time, Livefyre can now try dozens of experiments in parallel. This accelerates their pace of progress, without requiring any infrastructure management or setup.

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Domino provides automatic version control for code, data, and results

Analytical work is inherently exploratory, and progress isn’t always linear. Without an automatic log of past techniques attempted and results, Eugene and his team spent a lot of time recreating past work.

Domino automatically records each experiment, creating a record of the associated code, data, and results — and anyone on the team can browse and search these records.

Past work is readily available and past results are always reproducible, saving data scientists invaluable time and frustration.

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Sharing and collaborating is easy with Domino

Before Domino, sharing work happened haphazardly and inefficiently, if at all, through a hodgepodge of email attachments, code check-ins, shared drive links.

Domino gives Livefyre a central hub for their analysis. Authorized users can browse Domino’s modern web interface to see what work is in flight and any recent results. Team members can also subscribe to notifications to get alerts when results change.

This gives managers unprecedented transparency into what’s going on, while enabling analysts to make more connections about related work, sparking new insights and creativity.

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Data & Analytics

How Livefyre Doubled its Pace of Analysis