For the next few days, I’m going to be practicing delivering a virtual presentation. Instead of planning on standing up on stage and speaking to a crowd this presentation is going to be delivered virtually from my office chair. This format will neutralize one of my best speaking skills: audience engagement. Reading the crowd and adapting to the emotion of the room is a lot easier when you can see the people. At a conference you get the benefit of hearing a ton of other talks and seeing which parts of a talk are going to get the best reactions. That is something that I actually spend a lot of time thinking about. I’ll spend more time and go deeper into topics that the audience might enjoy more. During the course of listening to virtual conference things always just seem more rehearsed and the direct audience reaction is more limited. Generally I just click on links to talks and let them play on one side of my monitor while working on something else. The dynamic of a virtual presentation is totally different.
I’m working on practicing the delivery of my talk, “Demystifying Applied ML: Building Frameworks & Teams to Operationalize ML at Scale.” Within the body of that talk are three core topic areas related to ROI, ML frameworks, and teams. Right now I could hit record and deliver the talk and each of those content areas would get 5-10 minutes of coverage. The way I build out the delivery of a talk is not really based on reading from slides. I try to have a series of topics or very short taglines that sign post the content being delivered. During the course of delivering the presentation those core elements get coverage, but the exact phrasing changes every delivery of the presernation. Within a virtual presentation delivery I’m not going to be able to adapt the presentation to the audience. That probably means that practicing the delivery of a virtual presentation is going to be about delivering the best possible version of the talk.
My practice method is usually a daily delivery cadence for 15-20 days before the talk. This is a big investment of my time given that delivering a 30 minute talk and then listening to the recording is a commitment of about an hour a day to the presentation. At this point, I’m willing to make that investment and it should help ensure the virtual presentation is delivered in a well rehearsed and cohesive way for the audience. In practice, the recording method is usually just me talking to the audio recorder on my Pixel 4 XL smartphone and then listening back to the recording. The part of the process that helps refine the talk during each iteration is listening back to the content being delivered. My preference is generally for a more extemporaneous style of presentation, but in this case I’m going to try to refine the talk as much as possible before delivering the content to a virtual audience.
Talking for 30 straight minutes is not something that I normally do on a daily basis. Even during the course of a presentation I prefer answering questions throughout the talk and engaging in some lively debate. That type of interactive exchange is what I expect in the classroom and prefer even during the course of a presentation. I’ll be curious to see if the virtual presentation format includes a method to receive audience questions throughout the talk or if they get queued up at the end.
Presentation Topic Area: Machine Learning
Title Version 1) Figuring out applied ML: Building ROI models, repeatable frameworks, and teams to operationalize ML at scale
Title Version 2) Demystifying Applied ML: Building Frameworks & Teams to Operationalize ML at Scale
Description: Solving hard business problems requires operationalizing ML at scale. Doing that in a definable and repeatable way takes planning and practice. Understanding how to match the deep understanding of subject matter experts to the technical application of ML programs remains a real barrier to applied ML in the workplace. Understanding applied machine learning models with strong potential return on investment strategies helps make delivery a definable and repeatable process.
1. Beginning to think about the process of building machine learning ROI models
2. Setting the foundation for defining repeatable machine learning frameworks
3. Building teams to operationalize machine learning at scale