Andrew Fairless, Ph.D.
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2021-03-20
What I Watch: Why Distributed Systems Are Hard
2021-03-19
What I Read: Adversarial generation of extreme samples
2021-03-18
What I Read: Where Programming, Ops, AI, and the Cloud are Headed
2021-03-17
What I Read: technical debt, ML pipelines
2021-03-16
What I Read: Ensemble, knowledge distillation, and self-distillation
2021-03-15
What I Read: ML Models are Missing Contracts
2021-03-14
What I Read: MLOps for effective AI strategy
2021-03-13
What I Read: Transformer Networks to Answer Questions About Images
2021-03-12
What I Read: Interpretation for Image Recognition
2021-03-11
What I Read: Neural Text Generation
2021-03-10
What I Read: Why I’m lukewarm on graph neural networks
2021-03-09
What I Read: How Transformers work
2021-03-08
What I Read: Medicines Machine Learning Problem
2021-03-07
What I Read: definitive guide to AI monitoring
2021-03-05
What I Read: Data-efficient image Transformers
2021-03-03
What I Read: Machine learning is going real-time
2021-03-01
What I Watch: Future of Data Engineering
2021-03-01
What I Read: Long Live Data Discovery
2021-02-28
What I Read: NeRF Explosion 2020
2021-02-28
What I Watch: The medical test paradox, Can redesigning Bayes rule help?
2021-02-27
What I Read: Dismal Spring Awaits Unless We Slow Covid-19
2021-02-27
What I Read: XGBoost, What it is
2021-02-26
What I Read: Can you trust AutoML?
2021-02-26
What I Read: MLOps Changing How Machine Learning Models Developed
2021-02-25
What I Read: Feature Store vs Data Warehouse
2021-02-25
What I Read: Simplicity Creates Inequity, Fairness, Stereotypes, and Interpretability
2021-02-24
What I Watch: Modern Machine Learning Platform on Kubernetes
2021-02-24
What I Read: Understanding Unintended Consequences of Machine Learning
2021-02-23
What I Read: Deploying Machine Learning, a Survey of Case Studies
2021-02-23
What I Read: Introduction to Graph Neural Networks
2021-02-22
What I Read: Associative Memories
2021-02-22
What I Read: How Build Production Workflow SQL
2021-02-21
What I Read: Approximate Nearest Neighbor Search in Vespa
2021-02-21
What I Read: What If You Never Get Better From Covid-19?
2021-02-20
What I Read: Interpretability in Machine Learning
2021-02-20
What I Read: What is Data Observability?
2021-02-19
What I Read: Building Robust Machine Learning Systems
2021-02-19
What I Watch: Functional Data Engineering
2021-02-18
What I Read: Building a Gigascale ML Feature Store
2021-02-18
What I Read: HuggingFace Transformers
2021-02-17
What I Read: Cellular Automata in Stream Learning
2021-02-17
What I Read: Covid Survivors, Lingering Health Issues
2021-02-16
What I Read: Dynamic Data Testing
2021-02-16
What I Read: Isolation Forest
2021-02-15
What I Read: Revisiting Sutton’s Bitter Lesson for AI
2021-02-15
What I Read: The way we train AI is flawed
2021-02-14
What I Read: How DAGs grow
2021-02-14
What I Read: long-term symptoms of Covid-19
2021-02-13
What I Read: Mismatches between Optimization Analyses and Deep Learning
2021-02-13
What I Read: US Government Will Pay Doctors to Use AI Algorithms
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