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.
Throughout the year I have been focusing on some professional development related to advanced machine learning. Today I finished up the Advanced Machine Learning with TensorFlow on Google Cloud Platform 5 course specialization from Coursera and the Google Cloud team. It was fun to really dig in with TensorFlow and learn what the right tools can help you accomplish. It is an amazing time that you can just get online and check out the out the Advanced Machine Learning with TensorFlow on Google Cloud Platform specialization with just a web browser and a small fee. That gives you access to a ton of videos, labs, and quizzes. I really enjoy learning and digging into things. Part of being a true lifelong learning is just jumping in and learning new things. That is something that can be done by reading books and enjoying the written word in other forms. Some of the more complex things like advanced machine learning call for a more hands on approach. That is where the labs provided by Coursera really help me dig into actually using and running advanced machine learning models. My technical knowledge of the subject was stronger than my applied skills. That was something that I needed to work on and have been attacking for a couple hours a night.