Issue #8: Toronto ML Summit. GPT-2. ML Feedback Loops. Explainability Struggles. Judgy AI.
Issue #7: Trustworthy AI in the Govt, re:Invent, Underspecification, Tools for Software 2.0
Issue #6: MLOps Resources. Feature Stores. Interpretability. Predictive Uncertainty.
Issue #5: Cloud ML Platforms. Disappearing Data. Data Poisoning. "Building Cars v/s Factories" for ML models
Issue #4: Data Landscape. ML Stack. Imbalanced Datasets. Search@Airbnb. Ball vs Bald?
Issue #3: State of AI. Behavioral testing ML models. Dynamic benchmarks. Data versioning. MadeWithML.
Issue #2: Nuts and Bolts of ML. Unfriendly Comments. Great Expectations. Common ML Misconceptions.
Issue #1: Why is Machine Learning so hard?