Full Stack Data Engineering
Building a single-page application that fetches stocks data and displays their performance.
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Designing Data-Intensive Applications (M. Kleppmann): reading notes 1/3
Reading through this classic about data engineering. Part 1: Foundations of Data Systems
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Why quick-sort works
Prooving the correctness of quick-sort algorithms with high-school maths.
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A multi container ML app (3/3): Kubernetes and Google Cloud Platform
Deploying our 3-container translation app on a Kubernetes cluster to get scalability and resilience.
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A multi container ML app (2/3): deploying with Docker Compose
Now that we have Docker containers, let’s deploy them together with Docker Compose. Also covered: security with Docker secrets, data persistence with Docker volumes and dependency ordering.
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A multi container ML app (1/3): Docker
Building a translation app by putting together 3 containerized microservices: a Flask frontend, a FastAPI backend and a MySQL database. Let’s skim through the development process and the containerization. Also covered: Docker registry and CI/CD with GitHub Actions.
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Build a real-time stream of air quality data with Apache Kafka
Let’s build a data stream with Kafka today! We will retrieve air quality data using the World Air Quality Index project’s API, then push it on a Kafka cluster.
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An asymmetric loss for regression models
Drive regression models towards under/overestimation while keeping accurate outputs with the linear-exponential loss.
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NLP with 🤗 Hugging Face
Zero-shot classification is basically text classification with no training at all. How does it compare with transfer learning/fine-tuning? We’ll see using the beloved 🤗
transformers library.
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Interpretable machine learning with SHAP
In this post, we predict health insurance costs with an efficient black box model, namely random forest. Then we interpret individual predictions as well as the global behavior of the estimator using SHapley Additive exPlanations.
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