Author: Harini Kannan

I am a data scientist at Capsule8. If you wanna talk predictive models, hit me at https://twitter.com/HariniKSenthil or https://wisemachine.wordpress.com/

AI Product Management in Cybersecurity: McKinsey’s Three Horizons of Growth ? Flip it for startups, keep it for mega corps

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What’s McKinsey’s Three Horizons of Growth ? The McKinsey’s Three Horizons of Growth is a management framework first introduced in the book “The Alchemy of Growth” published in 2000, which has since been referenced by top product strategists to help businesses execute existing business models while simultaneously innovating and pushing the boundaries and creating new … Continue reading AI Product Management in Cybersecurity: McKinsey’s Three Horizons of Growth ? Flip it for startups, keep it for mega corps

The Game Changer: Large Language Models Unleashing the Power of AI in Cybersecurity

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Applied Machine Learning and AI in cybersecurity is slowly growing into mainstream industry, not just as an add-on anymore. As threat actors have started automating and streamlining a lot of their attack pipelines using off the shelf Large Language Models, the good actors are essentially forced to catch up with them ! It’s not just … Continue reading The Game Changer: Large Language Models Unleashing the Power of AI in Cybersecurity

Data Science Leadership Series – Part 2 : How to choose data projects: Core Product  Vs Support Consulting Vs Research | Beware of your bottomline

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The biggest challenge for data scientists / managers in decision making capacity and one with the biggest consequential outcome for both the business and the data team, I feel, is the part where you say yes / no / let’s modify - to a new data project idea from leadership, or even starting a new … Continue reading Data Science Leadership Series – Part 2 : How to choose data projects: Core Product  Vs Support Consulting Vs Research | Beware of your bottomline

Data Science Leadership Series : Part 1 – The need for AI Product Management

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People keep talking about data science being such a rewarding and lucrative career, but I feel it's time to talk about the serious gaps that's been bugging this field, in terms of operationalizing a successful data teams. Few tech companies like the Googles and Microsofts of the world have got this working like a well-oiled … Continue reading Data Science Leadership Series : Part 1 – The need for AI Product Management

The Power of Distributed XGBoost: Efficient and Cost-Effective Training for Petabytes of Data

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In the era of big data, managing and processing large volumes of information is a challenge faced by many organizations. As a data science professional, one must constantly explore innovative techniques to extract meaningful insights from massive datasets. You would be surprised how many data teams in really large organizations still default to neural network … Continue reading The Power of Distributed XGBoost: Efficient and Cost-Effective Training for Petabytes of Data

Explaining P-value to a non technical audience

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Wikipedia defines p-value as "the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct". Well if we give this definition, say in a presentation to a product or a business team, you're most probably gonna receive piercing puzzled looks. One of the major … Continue reading Explaining P-value to a non technical audience

Understanding Multicollinearity and Confounding Variables in Regression

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Multicollinearity When two or more of the predictors are correlated, this phenomenon is called multicollinearity. This affects the resulting coefficients by masking the underlying individual weights of the correlated variables. This is why model weights are not equal to feature importance. Ways to deal with multicollinearity Looking at Variance Inflation Factor (VIf), which measures the … Continue reading Understanding Multicollinearity and Confounding Variables in Regression