A very accurate and vivid description of being a data scientist in a consulting context. I can definitely say that I have experienced this myself, and it does take a while to get used to presenting comfortably (therefore confidently). But like it or not, the holy grail in data science has always been about connecting… Continue reading Being a data scientist in consulting
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Delegating to chatGPT & Stable Difussion
This week, I am jumping on the bandwagon by delegating the task of creating my LinkedIn post to chatGPT (for text) and Stable Diffusion (for image) with the following prompts. ⌨ chatGPT : Can you write an opinion article, discussing your opinion on this topic, “In the future, do you see a shift in companies… Continue reading Delegating to chatGPT & Stable Difussion
State of DS Survey by Anaconda – Skill Gaps
According to the State of Data Science survey done by Anaconda, the top 5 most important skill gaps in data science are: ⭐️ Engineering skills ⭐️ Probability and statistics ⭐️ Business knowledge ⭐️ Big data management ⭐️ Communication skills These skill gaps cut across multiple knowledge domains, from technical skills to soft skills, reflecting the… Continue reading State of DS Survey by Anaconda – Skill Gaps
Illustrated stable diffusion from Jay Alammar
If you are interested in knowing how Stable Diffusion generates amazing AI arts, but are put off by the steep technical details, This illustrated guide from Jay Alammar should help you, https://jalammar.github.io/illustrated-stable-diffusion/. The guide helpfully breaks down the model into components and substitutes complex equations with simple flowcharts. P/S: I also highly recommend his illustrated… Continue reading Illustrated stable diffusion from Jay Alammar
Short review of Designing Machine Learning System
I have finally finished “Designing Machine Learning Systems” after a few weekends of focus reading. It is one of the rare technical books that I finished in its entirety, and I thoroughly enjoyed it. Just to offer a quick book review below. The book is amazing in the following aspects: ✅Provides a high-level overview of… Continue reading Short review of Designing Machine Learning System
Automatic speech recognition – Whisper OpenAI
Whisper is a recently released transformer-based automatic speech recognition (ASR) model from OpenAI. It can be used for: 🗣Language identification 🗣Voice activity detection 🗣Multi-lingual speech recognition 🗣Multi-lingual speech translation When evaluated on the ESB datasets (including LibriSpeech, Common Voice), Whisper outperformed Conformer RNN-T from NVidia and Wav2Vec2 from Meta. Link to blog: https://openai.com/blog/whisper/Link to repo:… Continue reading Automatic speech recognition – Whisper OpenAI
Data versioning
“Data versioning is like flossing. Everyone agrees it’s a good thing to do, but few do it.” ~ Chip Huyen, Designing Machine Learning Systems Unlike code versioning, it is a lot more difficult to implement data versioning in data science / machine learning projects. It is because of the following reasons: ➡️ Data is often… Continue reading Data versioning
Concept drift vs data drift vs covariate drift
Do you always get confused among concept drift vs data drift vs covariate drift like me? The diagram (from a research paper, https://arxiv.org/abs/1511.03816) provides a clear illustration of the different terms. In summary, concept drift in data refers to changes in environmental conditions that differ from the original environmental conditions under which a model is… Continue reading Concept drift vs data drift vs covariate drift
MLU-Explain : Visual explanation of ML concepts
A very cool website from Amazon that explains various machine learning concepts using interactive and visual essays. https://mlu-explain.github.io/ Using simple and interesting examples, the website really brings to life many core concepts in machine learning and makes them accessible to more people. This reminds me of how I learned physics during my high school era.… Continue reading MLU-Explain : Visual explanation of ML concepts
ML system design – problem definition & consulting
I recently started reading the excellent book called Designing Machine Learning Systems by Chip Huyen. In the first few chapters, the book illustrated very clearly the differing stakeholder expectations of an ML system, by using a restaurant recommendation app as an example. Data scientists / ML engineers ➡️ Want a model that recommends the best… Continue reading ML system design – problem definition & consulting