Machine Learning (ML) is a major solution business and technical leaders can use to drive innovation and meet operational challenges. For managers pursuing specific organizational goals, ML is not just a tool: it’s a mindset. C2C’s community members and partners are dynamic thinkers; choosing the right products for their major projects requires balancing concrete goals with the flexibility to ask questions and adapt. With these considerations in mind, C2C recently invited Google Cloud Customer Engineer KC Ayyagari to host a C2C Deep Dive on The ML Mindset for Managers.
Ayyagari started the session by asking attendees to switch on their cameras and then ran a sentiment analysis of their faces in Vision API:
After giving some background on basic linguistic principles of ML, Ayyagari demonstrated an AI trained to play Atari Breakout via neural networks and deep reinforcement learning:
To demonstrate how mapping applications can use ML to rank locations according to customer priority, Ayyagari asked the attendees for considerations they might take into account when deciding between multiple nearby coffee shops to visit:
As a lead-in to his talking points about the ML mindset for managers, Ayyagari asked attendees for reasons they would choose to invest in a hypothetical startup he founded versus one founded by Google’s Madison Jenkins. He used the responses as a segue into framing the ML mindset in the terms of the scientific method. Startup management should start with a research goal, he explained, and ML products and functions should be means to testing that hypothesis and generating insights to confirm it:
Before outlining a case study of using ML to predict weather patterns, Ayyagari asked attendees what kinds of data would be necessary to use ML to chart flight paths based on safe weather. Guest Jan Strzeiecki offered an anecdote about the flight planning modus operandi of different airports. Ayyagari provided a unique answer: analyzing cloud types based on those associated with dangerous weather events.
The theme of Ayyagari’s presentation was thinking actively about ML: in every segment, he brought attendees out of their comfort zones to get them to brainstorm, just like an ML engineer will prompt it’s machines to synthesize new data and learn new lessons. ML is a mindset for this simple reason: machines learn just like we do, so in order to use them to meet our goals, we have to think and learn along with them.
Are you a manager at an organization building or training new ML models? Do any of the best practices Ayyagari brought up resonate with you? Drop us a line and let us know!