An introductory chapter on machine learning and deep learning, covering core concepts, categories, and terminology from a university course.
A personal analysis of the pros and cons for CS grads choosing between pursuing a PhD or entering the tech industry, focusing on machine learning careers.
Notes from Spark+AI Summit 2020 covering application-specific talks on ML frameworks, data engineering, feature stores, and data quality from companies like Airbnb and Netflix.
Summary of key application-agnostic talks from Spark+AI Summit 2020, focusing on scaling and optimizing deep learning models.
Answers common questions about data science in business, covering requirements, model interpretability, web scraping, and team roles.
Explores the career choice between being a technology generalist or specialist, analyzing the pros and cons of each path in the evolving tech industry.
Announcement for the Azure Skåne AI Day event, featuring sessions on Azure Cognitive Search, AI monitoring, Custom Vision, and transfer learning.
A guide to best practices for monitoring, maintaining, and managing machine learning models and data pipelines in a production environment.
Explores six unexpected challenges that arise after deploying machine learning models in production, from data schema changes to organizational issues.
A data scientist shares their workflow using the {drake} R package to manage dependencies and ensure reproducibility in long-term machine learning projects.
Announcement for an online Azure Skåne User Group event featuring AI/ML sessions on predicting earthquake damage and building chatbots.
A tutorial on using AWS AutoGluon, an AutoML library, to build an object detection model with minimal code.
A comparative analysis of the underlying architecture and design principles of TensorFlow and PyTorch machine learning frameworks.
Explains K-Fold cross-validation for ML models with a practical example using BERT for text classification.
An updated overview of the Transformer model family, covering improvements for longer attention spans, efficiency, and new architectures since 2020.
A data-driven blog's thank you post, sharing visitor stats, popular posts on ML topics, and future plans for guest contributions.
A guide to deploying and understanding H2O's distributed machine learning platform on a Kubernetes cluster, focusing on its stateful architecture.
A guide to streamlining ML experiments by combining Jupyter, Papermill, and MLflow for parameterized runs and centralized logging.
A curated list of top data annotation companies worldwide, grouped by annotation type, focusing on services for computer vision, NLP, and audio data.
Analyzes the potential impact of the COVID-19 pandemic on major machine learning conferences, discussing outbreak scenarios and contingency plans.
