We are back! For this episode, we discussed topics about generative AI. We actually used Google's Bard to generate the outline for this episode. It covers topics: What is generative AI? How does it work? What are some of the benefits of generative AI? Challenges of generative AI, the future of generative AI, and many more! Overall, this episode provides a comprehensive overview of this rapidly developing field. It could also explore the ethical implications of generative AI, and its potential...

It has been a while since we released an episode on this channel. Apologies, we were both busy with work and couldn't find a common dedicated time to record an episode. We also changed the intro and outro music. Let us know what you think? In this episode, Koo Ping Shung and I discussed the nitty gritty things about AI ethics. We also voiced our opinions on the different aspects of AI ethics and whether having a governing body to control the ethics aspect of AI is a good thing or not. Have a...

This week, we invited Kelvin Tham, an MLOps Data Program Manager at ViSenze - AI for Visual Commerce. Kelvin has a wide range experience across ML Ops, data analytics, and business process improvement. He is currently working on design, development and shipping of ML Ops model management. In this episode, he shared about how is it like to be working at an AI startup company and his war stories of wearing multiple hats at one go. He also talked about the differences between being a program...

This week, we invited Teck Liang Tan (PhD), a Senior Data Scientist @ NTUC Enterprise to have chat with us. He walked us through his unconventional career move from Physics to Data Science. Also, his reason of getting into the industry instead of continuing in academia. By the way, do you know what is complexity science?  If you are curious, you should definitely give this episode a listen! He also shared learning resources for getting into the field as well as keeping the skills sharp. We...

This week we invited Ivan who is a Lead Data Scientist at Tech in Asia. Ivan has a unique set of technical competencies, project management, interpersonal skills and problem solving abilities. He is also experienced in deploying scalable machine learning systems, data engineering pipelines, dashboards and delivering actionable insights through the use of statistics and data visualization. In this episode, Ivan shared his career journey from being an undergraduate to leading a data science...

Data collection is a crucial step for any data related projects. So much so that you might have encountered something along the lines of the “GIGO” (garbage in, garbage out) concept. Some might even say having the right data is more important than having tons of data that can’t be used. As web scraping being one of the ways to collect data, for this episode, we invited Cliff, a data consultant, back to discuss his personal experience with web scraping. He shared topics such as the basics of...

For this episode, we invited Low Yi Xiang, a Data Scientist at Traveloka again to have a chat about Data Product Management. Yi Xiang covered what is data product management in a nutshell, how it differs from the other product management practices, what are the stages of data product management lifecycle, and many more interesting topics. We hope you enjoy this episode as much as we had fun recording and producing it. Yi Xiang is an experienced data scientist who likes to work on various...

In this episode, we invited one of our popular guests, Charin Polpanumas, back! We got him to share a project that he is passionate about, PyThaiNLP. In this episode, we discuss the challenges of Natural Language Processing and also creating and working on an open-source project. This episode is definitely for anyone who is interested in Natural Language Processing as we discuss many aspects of NLP, building corpus, challenges in translation, and challenges on the limited training datasets!...

So what is MLOps? This is a topic we covered in this episode. We discuss the different aspects of MLOps, for instance, data, business requirements, and also measuring the performance metrics. We discuss also data quality and feature engineering and its impact on the ML pipelines as well. We also do a short introduction on the different tools used in MLOps, such as Containers, Kubernetes, and Airflow. And let us throw in one more technical term...data versioning. Give us a listen to understand...

In this episode, we have another guest - Amelia, a chemical engineer turned data scientist! Listen to the episode to understand more about her successful transition, what are the skills that she finds valuable as a data scientist, and how did she cope with studying for Master's and working at the same time. We had a great discussion on the topic of coping with work, studies, and everything else! If you want some tips and tricks, tune in to this episode to find out more! Amelia also shared...

Symbolic Connection takes a break from interviewing guests and has two non-experts, the co-hosts Thu Ya and Koo to share what they understand about a privacy-preserving model training technique called Federated Learning. We have a discussion on Federated Learning, its relationship with Edge Computing, how the industry solved the challenges associated with implementing Federated Learning, what is centralized and non-centralized FL. Curious and/or preparing for an interview? Hit that "Play"...

In this episode, we interviewed another lady in tech, Poh Wan Ting. She shared her career journey, how she started from computational biology to now leading a data science and engineering team in a well-known Financial Institution. She also shared how she manages her data team and retains them. We also discussed what shall one do when an opportunity comes, to take or leave it, and what are considerations one should take. And of course, being a hiring manager, we asked her how she selects her...

We have a guest from the banking industry for this episode, Jeanne, from the Bank of Singapore. She shared her journey, how she moved from studying animals to being an AI lead in the banking industry. We discussed how to encourage more females to join the tech industry, how does conducting training help one's career. Jeanne also shared how it is like working on tech in the banking industry and the weirdest interview questions she encountered. As a hiring manager, what is Jeanne looking for in...

In this episode, we have another guest from AI Singapore. He is Ryzal Kamis, Senior Platform Engineer. In this episode, we discuss a great deal on MLOps, what it is, and for anyone who is interested in the MLOps area, what are the learning resource, etc. Another topic that we discuss is data versioning, what it is and why is it important. We also discuss more on programming, debugging, and how to get better at it. Ryzal also shared how he transitioned from a Banking and Finance degree to an...

In this episode, we take a break and talk about documentation, something that is a "necessary evil" for any software engineer and data professional. Thu Ya and Koo, tackle the documentation topic by sharing their experience, their pains, and frustrations when documentation is not done well. They also shared what is documentation to be done for each stage of the project, the data preparation, the modeling process etc. Tackle documentation with less frustration and more effectiveness by...

This week we invited Michael Ng from Agilent Technologies to share his background and career journey. :) What got him interested in the field? What are the key skills in dealing with business stakeholders? What are the questions he asked his interviewees? What makes a good analysis? These are the questions we tackled during the podcast. Michael also shared his interview experience after he has graduated from his Masters, for instance, the "interesting" questions he was asked. One 'hot'...

Happy New Year everyone! We are back with an episode that may help planning your Data Science & Artificial Intelligence career! In this episode, you will find career tips to build a solid foundation in your Data Science career. Thu Ya & Koo discuss taking up an internship, contract, and full-time job and their possible impact on your career path, They also discuss the possible Data Science experience gained working in a Start-Up, Small Medium Enterprises, and MNCs. And should you join...

For this episode, instead of having a guest over, Koo interviewed his co-host, Thu Ya Kyaw, Machine Learning Engineer @NE Digital to share his programming journey including the motivations, the opportunities, and the struggles. Oh, did you know about 'tab vs space' war? You should definitely check out this episode.

This episode's guest is Low Yi Xiang, a data scientist at Traveloka. He is also a member of the DataScience SG Working Committee. He showed his unique perspectives on his career and data science and how he got started. Yi Xiang shared his journey from a “dashboard data scientist” to putting models in production in a technology unicorn. He also shared his process on important lessons and take backs being a data scientist.  Last but not least, we also did a discussion on Python and R, how the...

In this episode, Chong Zi Liang continues to share what steps he took to land his current role after the boot camp. He also discussed his current job scope, the tools he is using and what is he learning from his job. Hear what interview tips Zi Liang has as well. Last but not least, we discussed the different roles of data professionals and how they relate to one another. Check out this episode if you are interested in being a data professional, especially if you’re not from a STEM...

In this episode, we are very happy to invite Chong Zi Liang, a data analyst at 99.co, to share his career journey, particularly on his transition from a non-data, non-STEM background.  There is a lot of content to share again so this podcast has a few parts! In this part, we discussed why Zi Liang chose the Data Analytics field and how he started his career change, including how he managed his self-learning. Listen to his bootcamp experience and how he went about hunting for an analytics...

We continue our Data Myths busting in this second episode given the raving reviews and feedback from the audience. Yes, we are back with myth-busting! Here are the myths we discussed. 1 - "If the mathematics is not complicated/complex, it is not Data Science." 2 - "R vs Python...any winner?" 3 - "Data Scientist is the top job! Data Analyst & Data Engineer should aim to be a Data Scientist." Like to know more why they are myths and also how we debunk them, check out this episode and also...

In this episode, we continue our conversation with Chris Leong. We mostly discuss what is the difference between software engineering and machine learning. Chris also shared more on the projects he worked on during AI Singapore days. And last but not least, some pointers on getting into the Data Science profession. Have a listen!  LinkedIn Profile: https://www.linkedin.com/in/cleongks/ Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9

If you like Machine Learning and Gaming, this episode is really for you. We invited Chris Leong to join us in this episode, where Chris shared how as a software engineer, he managed to include machine learning into his capabilities. We had a surprising conversation talking about AI in gaming and that is how it leads to a 2-part episode. If you are interested to know how Chris joined the AI profession and AI in gaming, the back-end of how it can be used to generate game assets, do give us a...