One skill every ML engineer has to master ↓ 𝗠𝗟 𝗦𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 Yes. And do you know why? Because good ML system design has NOT changed at all in the last 5 years. And it won’t. 𝗪𝗵𝘆 ❓ Because any ML system is (and will always be) made of 3 types of programs (aka pipelines) 1️⃣ → 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 that transforms raw data into ML model features (e.g. vector embeddings) that are saved in a Feature Store or Vector DB. 2️⃣→ 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴/𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 that read historical features from the Feature Store/Vector DB and generate a new model artifact, either by training from scratch or fine-tuning a base LLM. This model artifact is then pushed to a model registry. 3️⃣ → 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 that load the model from the registry, and the input from the client app (for example a vector of numerical features, or a text prompt), generate a prediction (or a generation) and return it to the client app. This is a universal b

Wanna improve your 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼? Here is a list of projects (with code) to inspire you ⬇️ After 10 years working as… | 12 comments on LinkedIn

Let’s build a Real Time ML System to fraud. Step by step 🧵↓ 𝗧𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 💼 Every time your credit card is used online by… | 23 comments on LinkedIn

11 types of variables in a dataset, explained visually: . . Columns are typically categorized as either a feature or a target. However, there are so many variables that one may find/define in their dataset. 1-2) Independent and dependent variables – Independent variables: Features that are used as input to predict the outcome. – Dependent variable: The outcome that is being predicted. 3) Interaction variables – They represent the interaction effect between two or more variables and are often used in regression analysis. 4) Latent variable – A variable that is not directly observed but is inferred from other observed variables. – For instance, we use clustering algorithms because the true labels do not exist, and we want to infer them somehow. The true label is a latent variable in this case. 5-7) Confounding, correlated and control variables – Confounding variables are typically observed in a cause-and-effect study (causal inference). – Say we want to measure the effect of ice cream sa