Hi ,
It’s been 12 years since Thomas H Davenport and DJ Patil first declared data science to be “the sexiest job of the 21st century” and in that time a lot has changed. Universities have started offering data science degrees; the number of data scientists has grown exponentially; and generative AI
technologies, such as Chat-GPT and Dall-E have transformed the world.
Yet, throughout that time, one thing has remained the same. Most machine learning projects still fail to deploy.
However, it’s not the technical capabilities of data scientists that let them down
– those are now better than ever before. Rather, "it’s the lack of a well-established business practice that is almost always to blame.”
In the latest episode of Value Driven Data Science, I'm joined by Dr Eric Siegel, CEO and co-founder of Gooder AI and author of The AI Playbook to discuss bizML, the new “gold-standard", six-step practice he has developed "for
ushering machine learning projects from conception to deployment.”
Highlights include:
- (01:21) Challenges in machine learning deployment
- (05:00) The importance of business involvement in ML projects
- (15:39) Defining bizML and its steps
- (25:32) Understanding predictive analytics
- (26:52) Challenges in model deployment and MLOps
- (29:12) BizML for generative and causal AI
- (31:25) Exploring uplift modeling
- (35:45) Gooder AI: bridging the gap between data science and business value
- (45:45) Beta testing and future plans for
Gooder AI
- (47:35) Final advice for data scientists
You can listen to this episode at the link below, or find it on Apple Podcasts, Amazon Music, Spotify or Google Podcasts.
