It’s Not What A Book Says, It’s How It Makes You Feel “Boring, but a real problem to solve” Article after winning FutureBook BookTech Company of the year (thanks for the backhanded comment!) If you buy a TV, you’re interested in the viewing experience, the depth of color, the fluidity […]
Keywords / Deconstructing Amazon Search / Through The Dark Web After indulging in an attempt to find the holy grail of publishing, I pivoted to applying data science to solving a less lofty, but real need in publishing: keywords. The simple pitch was: There was a clear industry need to […]
The Holy Grail Of Publishing: Bestseller Prediction After validating that I could successfully classify novels into industry standard book categories (BISAC), I turned my focus back to the prize of predicting the next bestseller. I tackled this problem along two lines: Slush Filter In publishing, the flood of unsolicited manuscripts […]
I initially founded Kadaxis as a vehicle to experiment and validate hypotheses I had on applying machine learning models to book data. The goal was to see what models worked, then to wrap as a product and bring it to market (a little in reverse but I was intentional about […]
I recently helped a company build out and mature their data and MLOps platform. The company had a team of data scientists and engineers who had found product market fit with a deep learning based product. As with most lean, customer driven approaches, they hadn’t over capitalized on infrastructure too […]
This post originally appeared on the Kadaxis blog. So you’ve found the perfect keywords for a book, how do you know if they’re effective? Off-page keywords aren’t visible to potential customers, so assessing whether they’ll work or not takes a completely different approach to assessing visible metadata (title, description, etc.) about a […]
