Technical Content Marketing

Context

During my time on the Weights & Biases growth team, I wore many hats, from project manager to podcast producer and social media manager.

My favorite, though, was technical content writer! I wrote content marketing articles for Weights & Biases’s AI/ML developer audience.

Explaining Machine Learning Models

The goal of this content marketing piece was to create a beginner-friendly introduction to machine learning (ML) models that could educate and attract a broader, non-expert audience.

I led a structured content development process that began with extensive research and expert interviews. I collaborated with data scientists and ML educators to ensure technical accuracy, using their insights to create a logical, informative narrative. Then, I developed content outlines that I revised iteratively based on stakeholder feedback.

The final result balances technical depth and breadth with acccessibility, helping introduce machine learning to new audiences while supporting content marketing and thought leadership goals.

Transcribing Audio With Whisper

For this piece, I wrote a guide on how to use OpenAI’s newly released Whisper model to transcribe audio and save that transcription as captions with time code data.

By the time I wrote this article, I had produced over 35 episodes of Gradient Dissent, an AI/ML-focused podcast that interviews leaders and founders in the field. I was intimately familiar with both the importance of providing accurate podcast captions, and also the extremely tedious process of preparing those captions!

Since this article was part of a content marketing strategy to drive engagement beyond a core developer base, I designed this article for readers with little to no background in AI/ML.

To make the content more approachable, I included high-level explanations before introducing technical details slowly and as needed. I worked closely with an engineer to test Whisper and validate technical details, before cleaning and organizing our work into a public Google Colab that combined step-by-step instructions with actual code.