Mentoring at Launchpad Accelerator Africa — Class 3, HTP 2
This is my first experience with the Google Launchpad Accelerator. The session is organized such that there are three high touch-points (HTP). This was the second HTP, and held in Nairobi over a period of five days. We made use of iHub, an amazing and lovely location in Nairobi.
HTP1 was the first time we were meeting all of the startups. It was an extremely intense week, with the startups getting feedback from all sorts of new faces, and trying to make sense of everything they were hearing, probably for the first time. It was like a hurricane.
HTP2 was a more relaxed atmosphere. The startups had a few weeks between the first and second touch-points and were able to review and make some adjustments to their strategies and operations. It was like tuning the performance of their engines.
The accelerator focuses on both the operations and technical aspects of the startups. In HTP1, the startups were busy trying to figure out what sort of advice they needed, as well as who could best provide the advice. In HTP2, the startups were better able to request the specific mentors that they felt would understand their needs and offer the right sort of advice. The environment was much more relaxed.
One of my internal observations during HTP1 was the fact that the startups were in need of some upskilling with respect to the technology offerings from Google. This was because some of the challenges the startups were experiencing could be solved by simple migrations to the Google Cloud Platform. I was pleasantly relieved to see that an entire day was dedicated to bootcamps during HTP2.
There were two bootcamps, one dedicated to Design Sprints, and the other to Machine Learning. Both were vital to the success and operations of the startups. I was honoured to facilitate the Machine Learning bootcamp.
All I can say is, I had an amazing time at this bootcamp. What could be better than spending an entire day discussing topics of interest with like-minded individuals?
Summarizing the various tools that Google has for Machine Learning in a single day is a difficult endeavour. How do you decide what to mention in-depth and what to gloss over? In addition, how do you decide what to demonstrate? It was a struggle, but I managed to cover BigQuery ML, Firebase ML Kit, Dialogflow, Cloud ML API, AutoML, Cloud ML Engine, and TensorFlow.
We also did four labs on the Qwiklabs platform.
Did I expect the participants to remember everything? No, I did not. But the goal was to let them know as much as possible about the various tools and choices that are open to them.
Over the next few days and weeks, I expect them to ask more questions as they try to apply the technologies to their startups.
I am grateful to Google Developers Launchpad and Google Developer Relations for an opportunity to spend a week with some very inspired and brilliant minds. I look forward to seeing how these startups turn out at the end of the programme.
