Today is going to start off with a few thoughts about a presentation I’m working on related to machine learning. Thanks to the advent of the massive digital only online conference it looks like I’m going to be able to record the presentation ahead of time as a video file and submit it instead of delivering the talk live. I’m still planning on delivering a live talk online next month, but the other opportunity seems to allow the submission of a recorded video file. That will be a good opportunity to use my new Sony ZV-1 camera to record some highly professional looking presentation video. That camera captures way better video than my Logitech Brio webcam. Hopefully the presentation is judged by the audience based on the content of the presentation, but solid video will help keep that focus on the content instead of critiques of my ability as cinematographer or my internet bandwidth during the presentation. Watching 4 tech CEOs testify in front of Congress this week made it very clear that people do pay attention to video quality and what people have in their offices. I’m going to make sure that my video quality is better than what they managed to present. My bookshelves in the background are just full of books from my years in college. At this point, they are what they are. They are shelves full of books that represent little chapters in my educational experience. Thankfully the Sony ZV-1 camera will ensure my video quality is solid right out of the gate without a lot of effort on my part.
What I’m really curious about is if they needed one continuous video or if they wanted it edited with the slides embedded into the content. The ability to record the presentation in sections would make it a lot easier to functionally complete. Maintaining energy and focus for 5 minutes is a lot easier than trying to sustain the same presentation level energy for 30 minutes. That is something I’m going to work on this weekend and see what happens. The content is geared toward a 30 minute presentation and this video needs to be just over 20 minutes. If I’m doing my own editing and transitions between slides, then I could really do some interesting things with the video in PowerDirector 365 to ensure it is a really solid presentation. That is where my thoughts are right now and that is probably not a bad place to be this morning. My focus needs to be more geared toward producing presentations and academic papers. That is one of those things that will always be true. Every year I’m supposed to publish three academic papers in journals to keep up with my academic peers. That is a goal that I’m aware of and have fallen short of achieving. On the brighter side of things it is always possible to achieve that goal moving forward. It is never possible to achieve it retroactively so my focus needs to be on the potential of my future actions instead of dwelling on the academic consequences of my procrastination.
Apparently, I need to make a decision today about paying for a Pandora premium subscription or letting that expense lapse. I know that is the case thanks to a giant banner at the top of my Pandora instance. Over the years I have paid for the subscription on and off and I’m not entirely sure why I go one way or the other. My overarching goal of course is to remove any subscriptions from my path as they need to be really compelling to stick around. That is a strategy that a lot of people adopt to help faster savings instead of expense on a monthly basis. Maybe next month I’ll focus on listening to the well over 50 vinyl records sitting next to me in my office instead of streaming content. That is probably one way to go that will be more interesting, but it will also be a lot more work.
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
This week my time is going to be spent on working out my new speaking presentation. It is about time to get going on that project. At the moment, the working title of that talk is, “Effective ML ROI use cases at scale.” I’m not totally sold on that title and that might be why it is taking me so long to finish this presentation. Previously back in November I gave a talk in New York City about, “Figuring out applied machine learning: Building frameworks and teams to operationalize machine learning at scale. Thinking back on that now it was a very long title for a talk and a very different time before quarantine and the pandemic. Building that presentation ended up in a roughly 5,000 word presentation that was recorded into Mp3 format for easy listening. You can find that content here:
Writing this new paper is going to include a few different exercises along the journey. To help include you in the adventure I’m going to try to describe the process before it starts. Generally, I have used two different writing strategies to build out new presentations. One of these might work for you or might need an entirely different writing strategy. First, sometimes I just sit down and write the presentation from start to finish. Previously that has happened a few times and in one solid writing session driven by the headwinds of inspiration a paper goes from start to finish in one session. You could say in that example of a writing strategy you have to wait for the spark to strike and the paper will just end up happening. Second, I will take out one of my notebooks with blank pages and sketch out the structure of the paper and then start filling out the necessary sections like building random bricks in a wall. That analogy does not work in practice, but in the world of writing you can generally work on any part of the paper. That is the power of imagination within the process. Using a little bit of imagination you don’t have to build the paper from the bottom up like setting bricks in a wall.
Seriously, I’m not even entirely sold on the current writing project. It is a work progress to be sure. Three different titles have received attention; “Effective ML ROI use cases at scale”, “Building effective ROI ML use cases”, and “ML use cases at scale with effective ROI.” At some point along the way the title could even change. Right now the structure of the presentation is probably going to center on 5 solid ML use cases and how the ROI is calculated for those examples. That is probably all it will take to round out the presentation. My best to get this done is to start a shell in Microsoft PowerPoint tonight and work to get the PowerPoint slides built out one at a time. Completing the presentation in PowerPoint will allow me to have all my thoughts lined up and ready to present. The next step in the process would be to write out the complete talk. Working on that plan will generate another roughly 5,000 word block or prose that could be easily converted into some type of academic paper. It is possible that the paper will only surround the best use case or perhaps the machine learning return on investment model itself.
Oh, some wondering is happening. A little bit of thinking. A little bit of planning. My thoughts were all over the place. Last night I sat down to write some notes about attending a conference this week in California. At some point, I’m going to spend some time really digging into and writing about talking and the experience of getting to meet so many folks interested in machine learning. Instead of doing that writing at the moment, I need to spend some time working on week 2 of the “Crash Course on Python” by Google on Coursera. It feels like forever since I did week the first week of course work. This weekend will be a good opportunity to really jump in and dig into the course with the attention it deserves.
Greetings from Day 3 of the #GlobalAI Conference. I’m speaking at two sessions today during the conference. Earlier today I went ahead and shared the slide deck and a pre read in mp3 format on LinkedIn.
Here are the talks I listened to today with a few notes:
Business Track: The Autonomous Pharmacy: Applying AI and ML to Medication Management Across the Care Continuum (Ken Perez) – This was an interesting way to start the day. A lot of this talk focused on targeting and adherence. Both of those targets are about helping people get and sustain care.
General Keynote Session: The Pros and Cons of Automated Machine Learning in Healthcare (Sanjeev Kumar) – This talk really dug in and tried to address silos and data quality. Those are two things that make it very hard to use dispersed and highly inaccessible data from legacy systems.
General Keynote Session: Google’s Journey to AI-First (Chanchal Chatterjee) – This was a really fun talk. Everybody really enjoyed it. The 3 hour version of this talk would have been epic.