Life Long Learning

Background

A long time ago (back in 2013) during a summer internship, I realize that University’s education is not sufficient for workplace. The idea then was to study as much as I can, improve as fast as I can, gobbling up materials.

The end result is I ended up not learning anything, mainly due to not internalizing the content (touch and go). This blog serves as a personal reminder to document the things I have learnt, plus forces me to write the content in a way that is succinct and a personal knowledge based in cold storage .

Framework

The next question becomes - What to study or focus on then? - choosing what to study has a larger impact and consequence? A common interview question was always:

Where do you see yourself in 5 years time?

And whatever answer I had for myself in 5 years time, was no longer true within the next year. I decided to use a different approach, that is to treat myself as a product, focusing on 3 major (personal) pain points in the current year to set as my goals for the next year.

Why 3?

• Something to rotate around incase I am bored
• Allows me to focus!

Overall, this has been working well for me so far - definitely space for improvements!

2025

Goal:

Finished my masters, got a new role (within the same company).

• Work on my health and fitness (that took a backseat during the past 3 years)
• Improve my engineering skills
• Catch up on LLM (that I missed out the entire cycle since the release of GPT in 2022)

Rejected Goals:

• 3D printing
• Robotics

2024

Busy with OMSCS

2023

Busy with OMSCS

2022

Gotten a new job, and wish to work more on my data & statistical skills! These are my (undecided) goals so far, in the order of priority

• Spend time on ML, in particular regression, d-trees, and bayesian statistics.
• Get better at data structures.
• Explore the SWE stack and practice data structures.
• Pick up neural network / graphical related technologies.
• Uncertain about other techniques, like time series, ranking, recommendations, computer vision, nlp, reinforcement learning etc.
• Deciding whether to enroll in an online masters or not (?)

I ended up taking OMSCS starting in Fall 2022, and completed it in Fall 2024!

2021

Goal:

The odds are someone else has encountered my problem and either the cloud platform has a solution for it or there is some open source tool out there. I should spend more time exploring such tools and evaluate them instead of jumping in directly and build my own.

Solution:

• Invest and upscale my cloud knowledge & tooling. (GCP in particular)
• Understand more on data best practices and explore more open source tools. (Tf-X, MLflow, Feature stores?).
• Understand more on experiments & algorithms. (Just for learning)

In addition, starting this year, I also decided to note down my reflection as well as goals that did not make it to the list!

Reflection:

• Understood cloud build, cloud functions, ai platform better.
• Explored more on apache beam, DBT, fastapi, docker compose.
• Did a post on hypothesis testing, and will double down on this.

Rejected Goals:

• study a new programming language, uncertain whether it should be golang or java
• Try to implement a deep learning use case.
• Study hardware engineering and build something related to IOT.

2020

Goal:

As my abilities “scaled” (and being able to see things “end-to-end”), I end up getting multiple (conflicting) requests from many teams. I delivered as many of those as I could, but ended up with a huge technical debt and operational overhead. I want to reduce this going forward.

Solution:

• Build my own website as a way of knowledge repository, both for easy recall and sharing. (It was also a way to put what I have learnt into good use.)
• Write better code, understanding typed python, testing, linting, CI/CD.
• Wanted to know more on experiments and a/b testing (I failed this) - but I learnt a lot more on engineering side, such as tooling, Docker etc.

2019

Goal:

While I was successful in launching a machine learning model for credit scoring, there were lots of miscommunication and alignment required. I decided to go back to be an Individual Contributor (IC) and wanted to invest more in my engineering skills.

Solution:

• Develop an end-to-end (sounds like a marketing jargon) application that is integrated with the product funnel to be used in production. This would be a true test of “work ready”.
• Learn about data streaming processing (real time streaming instead of batch)
• Learn SQL - to use BigQuery (I was the Select * Data Scientist and I dataframe-d everything. )

2018

2018 was a very interesting year for me I joined a new company and was (suddenly) responsible for a new product launch.

For that year, I put the company’s goals above my own. It was definitely one of the most painful years but highlight of my career there and rewarded handsomely for it! Also, being a new joiner there was already plenty of tools and process for me to learn! (E.g It was the first time I had to deal with billion rows and using spark was a necessity instead of a proof of concept.)

I was in-charge and tasked to develop a credit model for Traveloka’s new fintech vertical despite having no experience in credit or putting models into production.

2017

Goal:

I was frustrated being a “Power Point/Dashboard” Data Scientist to someone who is able to deploy models into production.

Solution:

• Get skills in order to find employment in a tech company. (Made the shift in Q4 2017)
• Learn how to build data products.
• Learn Python.

2016

Goal:

After trying to master everything, I realize i was chasing hype which resulted in half-baked understanding. I should focus and understand why companies use certain technologies and the “why”.

Solution:

• Learn Modeling Intuition (Why use one model over the other?)
• get familiar with Big data stack (Hive, Spark, etc) and the use cases.
• Get familiar with cloud computing and the use cases.