Breakfast happened. Espresso shots were made. The movie Alien (2003) is playing with a few directors cut edits on the television. I have to say that today got off to a good start. I’m setup to sit and work for the next few hours without any planned interruptions or disturbances. The entire Alien quadrilogy is queued up to provide something in the background to help keep me focused. That might sound counterintuitive, but I work better with either some music or content on in the background. Sitting down and working in absolute silence is a recipe for my mind to start racing and for my attention to get pulled in a myriad of directions.
Things got real pretty quickly this morning. It just took a few moments to setup a new project called newsletter in my Google cloud Platform (GCP) account. Setting that new project up was probably going to be the easiest part of the whole process. The next little bit of time will be spent working on setting up a natural language processing neural network within my GCP project. My efforts today will be broken down into three areas: 1) setting up the neural network or maybe using the natural language API, 2) building a content ingestion method, and 3) starting work on developing the workflow necessary to ingest content, process that content in a meaningful way, and generate a newsletter based on that content. Writing all of that into one really long sentence made me realize just how much work is on tap today. All of the foundational things are already in place. Today will be able staying focused and putting things together in an ordered and thoughtful way.
Yeah —- I made some progress today, but my ambition outpaced my ability to build all of that in one day. In hindsight, I probably should have known better.
Taking the day off was a great idea. Every now and again having a Wednesday off is great. Part of the day was devoted to taking the AWS certified machine learning specialty beta exam. The whole process took about two hours and was very interesting. The PSI system took a little bit to setup and get running. The actual exam part took about 90 minutes. It has been a bit since my last adventure to a testing facility. It was pretty much exactly the way I remember it being.
It has been some time since I last fell asleep during an epic studying session. It was probably back in graduate school. Those days were fun, but they seem like forever ago. Let me set the state for you a little bit for a moment. My Dell ultra wide monitor was playing some AWS videos on one side and a document was open on the other half of the monitor. My entire focus was on what the presenter was saying then all of a sudden my hands hit the keyboard and my attention jerked back into focus. It had happened all of a sudden. Sure enough for a minute or two I had fallen asleep during some AWS training videos on my day off in the middle of the day. Maybe the right way to look at it is to assume my cup was full and it was time for a nap. Perhaps it was an unplanned power nap. It was most definitely not part of my machine learning and artificial intelligence learning plan for the day. Today was a day that was entirely devoted to learning and studying. That was what made it such an interesting day. It was full of possibility. Oddly enough, it also was a day that that included an impromptu nap.
All of my spare time is being devoted to working on AWS training and certification this week. The one class that really caught my attention was the course, “Seeing Clearly: Computer Vision Theory.” With the announcement of the new AWS DeepRacer and corresponding league it seemed like a good thing to think about and dig into today. Digging in is the fun part of being a lifelong learner. I’m not entirely sure how I feel about the DeepRacer announcement. It is a good method to help build models. It sounds like a very interesting thing to push forward models at an accelerated rate. Seeing what happens will be pretty interesting throughout the next year.
This whole thing looks like it would be fun to do with my first grader. Maybe that would be a good enough reason to buy the racer car and start building models. I really do think this is a method to get people to produce a bunch of distributed models to farm out the work without having to pay top talent. It is a pretty good strategy to get a market edge or to in some ways change the market. People would probably watch this type of racing league.
I just did the “Introduction to Amazon Machine Learning” hands-on demo from Qwiklabs for free. Apparently, Qwiklabs has about 35 free hands on demos that you can do right now for fun to learn and grow your skills. That as you might have already guessed is pretty darn cool and a great opportunity to sharpen your skillset. You can login to Qwiklabs –> Catalog –> Filter: Price –> Free. That should let you check out what hands-on labs might inspire you to start learning today… If that does not inspire you to start digging into the world of machine learning I do not know what will. It is fairly amazing that we have access to free online hands-on labs. Labs are one of the best ways to really see the technology in action. Sure, you can watch a demo, but that type of learning is not the same opportunity. Being able to really dig in and poke around is what makes a hands-on lab so impactful.
You are going to be totally surprised. Yeah – you are probably not going to be surprised at all based on my post yesterday. All of my free time today was spent watching AWS machine learning videos. I really started to dig into those videos and all the content that is now online for free. My honest opinion is that the content from Coursera on the Google Cloud Platform was more dynamic and the combination of constant quizzes and hands-on labs really helped me dig in and absorb the material. However, given that the AWS machine learning content is free and organized pretty well to be highly consumable it works. My plan is to take on every single bit of content they made available. That is about 30 courses and 45 hours of material. The one thing that I have noticed so far is that you can only listen to the content at 1x speed. That might not seem like a very big deal, but normally I listen to lectures in fast forward. That is how I like to go about things. Instead of listening to music in the morning and at night I’m powering through machine learning content. Focusing in on machine learning and improving my skillset has been pretty rewarding.
The AWS training and certification learning library is sorted into domains and a few other filters. Sorting down to the machine learning domain will reduce the learning library to 92 items. At the moment, I have completed 17 of the 92 items. That is not a bad start. I’m not entirely sure how long it will take me to power through all of that content. Some of the items are more involved than others. That is probably a good start toward consuming the whole learning library of 393 items. Some of them looking interesting, but I am willing to bet that the machine learning related items hold my attention better than any of the general items. Based on my recent laser focus on machine learning investing the time to finish the 92 items probably makes sense. They are free and a little bit of training every night is pretty much the path I am electing to walk down.
My thoughts are awash with what might be in the 45 hours of training that Amazon just released. All of my efforts to date have been focused on the Google Cloud Platform certifications, but I think it might be fun to chew through the 30 courses featuring over 45 hours of training that was just released and take the exam. I do enjoy taking exams for some ineffable reason. The idea that the exam is still in beta does make it seem extra shiny. That might just be enough to drive me to the finish line. Being first in the pool is always more fun than having to take the boring post beta version of an exam related to machine learning…
Starting tonight I’m going to tear through these courses and probably write about the process for the next two weeks or so as I absorb the content. The thing that I am the most curious about is which platform I will want to use going forward. I’m super comfortable in the Google Cloud Platform and ready to go do machine learning in that ecotype without reservation. My knowledge of AWS is rather limited. I just spun up an account about fifteen minutes ago.