A guide on tailoring your resume for different machine learning roles like Data Scientist and ML Engineer, using a 3-step process.
A tutorial on building a gesture-based payment system prototype using Arduino, TensorFlow.js, and JavaScript for secure transaction confirmation.
A tutorial on creating custom inference handlers for Hugging Face Inference Endpoints to add business logic and dependencies.
Explores why data and ML pipeline tests break incorrectly and offers strategies for writing more robust unit, schema, and integration tests.
Explores why complex ideas and systems are often favored over simpler ones in tech and academia, and argues for the advantages of simplicity.
Explains how to integrate Dask with Kubeflow to accelerate data preparation and ETL tasks in machine learning pipelines using distributed computing.
A curated list and summary of recent research papers exploring deep learning methods specifically designed for tabular data.
A curated list and summary of recent research papers exploring deep learning methods specifically designed for tabular data.
A podcast interview discussing reinforcement learning applications, data science career paths, and productivity insights for tech professionals.
An analysis of GitHub Copilot's ethical and legal implications regarding open source licensing, arguing it facilitates the laundering of free software into proprietary code.
Part 2 of a series on using Azure Anomaly Detector to identify unusual patterns in air quality sensor data for safety alerts.
Explores the application of classic software design patterns, like the Factory pattern, to machine learning code and systems, using examples from PyTorch, Gensim, and Hugging Face.
Compares Amazon SageMaker's four inference options for deploying Hugging Face Transformers models, covering latency, use cases, and pricing.
Explores bandit algorithms like ε-greedy, UCB, and Thompson Sampling to improve recommender systems by balancing exploration and exploitation.
A technical guide explaining methods for creating confidence intervals to measure uncertainty in machine learning model performance.
A guide to creating confidence intervals for evaluating machine learning models, covering multiple methods to quantify performance uncertainty.
Explores synthetic data generation methods like augmentation and pretrained models to overcome limited training data in machine learning.
Explores counterfactual evaluation as an alternative to A/B testing for offline assessment of recommendation systems.
A data scientist shares a structured approach to starting data science projects, focusing on business goals, requirements, and avoiding common pitfalls.
A $10,000 charity bet on whether fully autonomous (Level 5) self-driving cars will be commercially available in major US cities by 2030.
