All Blog Posts
B 09 06 22 Rearchitecting a System Blog
We recently shared a look at how the Abnormal engineering team overhauled our Unwanted Mail service architecture to accommodate our rapid growth. Today, we’re diving into how the team migrated traffic to the new architecture—with zero downtime.
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B Podcast Engineering 11 08 24 22
In episode 11 of Abnormal Engineering Stories, David Hagar, Director of Engineering and Abnormal Head of UK Engineering, continues his conversation with Zehan Wang, co-founder of Magic Pony.
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B Overhauled Architecture Blog 08 29 22
As our customer base has expanded, so has the volume of emails our system processes. Here’s how we overcame scaling challenges with one service in particular.
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B Podcast Engineering 10 07 27 22
In episode 10 of Abnormal Engineering Stories, David Hagar, Director of Engineering and Abnormal Head of UK Engineering, sits down with Zehan Wang, co-founder of Magic Pony.
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B 06 7 22 Disentangling ML Pipelines Blog
Learn how explicitly modeling dependencies in a machine learning pipeline can vastly reduce its complexity and make it behave like a tower of Legos: easy to change, and hard to break.
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B Podcast Engineering9
In episode 9 of Abnormal Engineering Stories, Dan sits down with Mukund Narasimhan to discuss his perspective on productionizing machine learning.
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B 05 11 22 Scaling Out Redis
As we’ve scaled our customer base, the size of our datasets has also grown. With our rapid expansion, we were on track to hit the data storage limit of our Redis server in two months, so we needed to figure out a way to scale beyond this—and fast!
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B 04 28 22 8 Key Differences
At Abnormal, we pride ourselves on our excellent machine learning engineering team. Here are some patterns we use to distinguish between effective and ineffective ML engineers.
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B Podcast Engineering8
In episode 8 of Abnormal Engineering Stories, Kevin interviews Saminda Wijegunawardena, an engineering leader who is no stranger to fast-growing enterprise startups.
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Blog autotrain models cover
There are many approaches to ensuring our system can adapt quickly to new attack trends. One of the most successful approaches we’ve found is to take in the newest attacks and retrain our system end-to-end to detect them.
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Blog model understanding cover
Here at Abnormal, our machine learning models help us spot trends and abnormalities in customer data in order to catch and prevent cyberattacks.
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B 01 20 22 Calibrating Classifiers
Abnormal's fundamental job is to detect malicious emails like phishing and business email compromise attacks and other malicious events, such as suspicious sign-ins that indicate an account has been hacked.
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Blog podcast role cto
Tim Tully, Partner at Menlo Ventures, grew up in Silicon Valley, where a love for coding was kindled in him. Tim is a technologist to the core, which innately led him to become an elite technical leader at companies like Splunk and Yahoo.
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Blog saving memory python cover
At a hyper-growth startup, a solution from six months ago will unfortunately no longer scale. The business is growing rapidly, and this traffic to this service in particular was growing at an unprecedented rate. We hit a point where it needed re-architecting to support 10x the current scale.
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Blog pyspark cover
At Abnormal Security, we use a data science-based approach to keep our customers safe from the most advanced email attacks. This requires processing huge amounts of data to train machine learning models, build datasets, and otherwise model the typical behavior of the organizations we’re protecting.
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Podcast green blog
Tony Dong, Director of Engineering at Rippling, is no stranger to the diverse set of engineering problems that fast-growing startups create. Before building and leading his teams at Rippling, Tony was CTO and co-founder at PerShop, a YC backed startup, and Senior Engineer at Twitter, Periscope, and TellApart.
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Blog distributed codebase cover
As Abnormal grows, we have to maintain a scalable codebase across all areas of engineering to prevent issues around testing, maintainability, and documentation. When organizations scale, a common problem is that the codebase becomes cluttered, with multiple teams writing different code that accomplishes the same task.
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Blog podcast green cover
Many companies aspire to be customer-centric, but few find a way to operationalize customer-centricity into their team’s culture. As a 3x SaaS startup founder, most recently at Orum, and a veteran of Facebook and Palantir, Ayush Sood...
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Blog podcast purple cover
Working at hyper-growth startups usually means that unreasonable expectations will be thrust on individuals and teams. Demanding timelines, goals, and expectations can lead to high pressure, stress, accountability, and ultimately, extraordinary growth and achievements.
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Blog podcast yellow cover
In engineering teams, there’s a mythical concept of a “10x engineer”— engineers who have 10x more impact and responsibility than the average engineer. Do these engineers actually exist? Is this a myth, or a possibility that engineers can realistically aim to become?
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Blog podcast green cover
Over the last three years building our ML-based cybersecurity products at Abnormal Security, I’ve benefitted enormously from discussions with colleagues in the ML space. This podcast aims to make some of those conversations available. In our second episode of Abnormal Engineering Stories...
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Blog purple black ai
It’s one thing to add machine learning and artificial intelligence features to an existing software platform. It’s quite another to build an entire company like Abnormal Security around machine learning technology, and to provide practical, everyday value to enterprise organizations.
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Blog podcast purple cover
As VP of Engineering here at Abnormal Security, I’ve had numerous conversations with our team, venture capitalists, and external engineering leaders about the challenges of building and leading engineering teams. Building applied machine learning products at scale requires solving a wide range of challenges...
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Blog ai algorithm
Our ML pipeline powers a detection engine that catches the most advanced email attacks. These attacks are not only extremely rare, but also change over time in an adversarial way. Since we require both high precision and high recall, and the cost of any error is severe, it is essential...
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