The Project:
Podsights connects podcast downloads to on-site activity, giving advertisers and publishers unprecedented insights into the effectiveness of their podcast. Their overarching goal is to grow podcast advertising. Podsights has worked with almost 1,900 brands, the majority new to podcasting, to measure and scale their advertising.
To accomplish their goal of becoming the “operating system” of podcasting, Podsights created a Machine Learning Research & Development Team consisting of ML researchers Chase Bosworth and Victor Nazlukhanyan, working together with data analysts and API & Operations support.
Chase leads the Machine Learning Research & Development Team (ML R&D) as Machine Learning Engineering Manager. Her own research focuses on the NLP domain. She loves translating the rich conversational and storytelling podcast medium into insights via deep learning. Projects she works on include Brand Safety and Suitability and Ad Detection.
Victor was the second ML Engineer to join the ML R&D team at Podsights. His work includes researching and developing models relating to user segments, demographics, and conversion. The scope of his role is to holistically assess and address the breadth of machine learning methods that can be used to solve the problems at hand.
As Podsights seeks to grow both the headcount and project scope of their ML R&D team, with projects including vocal cloning, stylized text generation and content analytics, they needed a solution to fill in for their missing MLOps roles.
The Challenge:
Before Chase and Victor joined the team, Podsights was new to the machine learning space and lacked the experience necessary to put models into production.
Podsights’ core feature, Podcast Attribution, didn’t rely on machine learning, and with the company’s sights set on broadening their offerings into media planning and beyond, the new ML team had to start from scratch. Despite being faced with a steep DevOps learning curve, prototyping and deployment were top of mind. Podsights recognized the importance of a tool that would eliminate the need to build MLOps infrastructure in-house.
The Solution:
Within a day of putting into use, Grid was already generating value for the ML R&D team. The learning curve wasn’t steep and support was extremely responsive, which reduced any misunderstanding or misuse of the platform.
The last mile problem in machine learning is a problem for everybody in the industry. A big challenge with many research teams and startups is not being able to easily add new team members as they scale production of their models. Using Grid, Podsights was able to develop high quality models without increasing the size of their team, moving from proof of concept to production ready 50% faster than industry standards. Grid makes the R&D process and rapid prototyping seamless and easy with a diversity of hardware accelerator configurations included with Runs. This helps Podsights automate, monitor and version models effortlessly.
“Grid allowed us to work independently, completely self-sufficient, and be able to get models into production significantly faster than had we needed to invest in MLOps roles internally.” Chase Bosworth, Machine Learning Engineering Manager (Spotify x Podsights)
Grid has proven essential to the Podsights team: it works, it scales, it meets their needs. After nearly a year using Grid the two person Podsights team has now put almost 4 models into production.
The team benefited from:
- Having a wide variety of hardware instances type where you can match to the right use case
- Grid Runs makes hyperparameter tuning a breeze
- Being able to pause and resume Sessions in order to save cost or switch gears
- Datastores being a cleaner solution than changing up the dataset and having to download the new one during sessions
- Using the UI over CLI. UI is extremely smooth to use and you always have the CLI as a backup if needed
“The intangible value of Grid: I am a happier data scientist because I get to focus on the stuff that I love to work on, and ultimately the reasons that they hired me, which is to research and develop models and translate real world problems in podtech into machine learning products. I think it presents a justification for machine learning engineers and data scientists to focus on what we were hired to do, rather than spinning our wheels on infrastructure.” Chase Bosworth, Machine Learning Engineering Manager (Spotify x Podsights)
Getting Started with Grid:
Interested in learning more about how Grid can help you manage machine learning model development for your next project? Get started with Grid’s free community tier account (and get $25 in free credits!) by clicking here. Also, explore our documentation and join the Slack community to learn more about what the Grid platform can do for you.
