Random @random

GCP certified random

Finally

I told myself two years ago I want to get both of these certs. Finally did it.

It definitely helps me a lot when designing an end-to-end data science solution.

• A) Professional Data Engineer

  

  
  
  
  

• B) Professional Cloud Architect

  Finally got my architect certificate in the second try! I am super excited about this!
  

  

  
  
  
  

• C) Code Jam 2021 (Qualification round)

  It probably doesn’t mean much. But I am still pretty excited to be qualified in the Code Jam 2021 First round. It’s fun to pick up algorithm again once in a while.
  

  

  
  
  
  
  

Lessons learned

(To be added)

What’s next

Time to pick up blogging again after the long long rest!

Had been having too much fun writing Go, will start some Python related posts soon.

Certification directory

• Credential Holder Directory - Here
  
• Cloud Engineer - Here
  
• Cloud Architect - Here
  

Reference

• Photo by Pineapple Supply Co. on Unsplash
  

Short break random

Short break for a little while.
Short break for a little while.

Preparing for code jam and GCP Exam.

Start writing again in May. Happy Coding.

Reference

• Photo by Pascal Debrunner on Unsplash
  

Why this blog? random

Introduction

When you’ve written the same code 3 times, write a function.

When you’ve given the same in-person advice 3 times, write a blog post - David Robinson

Here we are.

Reasons I started this blog

• 1. I like Golang.

  I like emacs. And I like machine learning. I am writing what I like to do, this is what it’s about, right?
  

  
  

• 2. I love knowledge sharing.

  There have been times when I felt like the knowledge I was looking for wasn’t out there and I am sure some of you have experienced the same thing. I am trying to contribute by putting down what I have learned here.
  

  
  

• 3. DevOps. Scalability and maintainability aspect of Data Science.

  If truth be told, in my team’s settings, I am spending as much time to maintain and evaluate the models I built than actually building the models. I feel like there are not enough articles out there for us to understand on how to productionalize and maintain a DS product development cycle.
  

  
  

• 4. Minimalistic examples.

  When it comes to machine learning or coding, I like learning by some short and reproducible examples. It is like the MVP concept in product development, it helps us understand what the model can do, and validate our ideas early in the development cycle.
  

  
  

• 5. Some data scraping techniques sharing too.

  I constantly find myself needed to write some crawlers at certain point of my projects. Who knows, maybe one day you will find that useful too.
  

  
  
  

Stay tuned if you like these topics

• 1. Machine Learning.

  Machine learning is a very board topic. Just to add my two cents on what excites me and how I get started in this field.
  

  
  

• 2. Work more efficiently.

  A carpenter is only as good as his tools. Don’t under-estimate the power of a good editor and workflow can bring you. Give yourself a try if you find something useful here.
  

  
  

• 3. Data Science workflow and maintainability.

  I learned the hard way on how a data pipeline is critical to a successful data science project. There are some software development and operations techniques out there that will help you with faster development of your next project.
  

  
  

• 4. Want to start your own Data science project.

  Sometimes we have a great idea of what our next model would be, but we just need more data. Learning how to write a crawler may help you get started with your project.
  

  
  

• 5. Um.. Golang.

  I am a gopher, what can I say.
  

  
  
  

TechStacks of this blog

• Blog. Hugo - Here
  

• Screen Capture. ttystudio + asciinema - Here and Here
  

• Python and Golang. Emacs. Linux. Jenkins. Git.
  

Photo by Franki Chamaki on Unsplash

TODO Empty template

Some Content