THE ML ENGINEER · YEAR ARCHIVE

Newsletter issues from 2019

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#54Dec 2019Yoshua Bengio; Towards system 2, AI Index 2019 Report, Microsoft's NLP Best Practices, The day that changed Netflix tech, Attention and Augmented RNNs + more 🚀llms · nlp · ml-research #53Dec 2019Real time computer vision at scale, Ray for the curious, Evolution of Zulily’s Airflow, Key trends in ML for 2020, A gentle intro to imbalanced ML + more 🚀computer-vision #52Dec 2019Top Python ML Libraries in 2019, NeurIPS 2019 Videos are Out, Modern NLP with SpaCy Podcast, Testing Guide for Software, Spotify on Better ML Infrastructure + more 🚀mlops · nlp #51Dec 2019Play Endless Game Built by AI, Gentle Intro to Model Selection, Code Reviews for Jupyter NBs, Netflix Releases Metaflow, Adversarial Detection Hands On + more 🚀mlops · ml-security · generative-ai #50Dec 2019Data Science Best Practices, A Contract for the Web, Deep Learning Indaba 2019, Uncertainty Quantification in DL, Google XAI Whitepaper + more 🚀explainability · ml-research · mlops #49Nov 2019Outlier & Adversarial Detector, The AI Governance Dilemma, Contextually Keyed Word Vectors, Time Series Anomaly Detection, Pyro 1.0 Released + more 🚀mlops · ai-policy · nlp #48Nov 2019ONNX Joins the Linux Foundation, The Nuances in DevOps for ML, Continuous Delivery for ML, Learnings reaching 2% in Kaggle, The New Data Exchange + more 🚀mlops #47Nov 2019E2E ML with MLFlow and Seldon, Reconstructing thoughts with ML, Scalable AutoML with Ray, Tensorflow World Videos, 14 types of learning in ML + more 🚀mlops · ml-research · ml-education #46Nov 20196 lessons learned at Booking.com, EurNLP 2019 videos released, Consistency of AI Summarization, Linux Foundation Trusted AI, AI Ethics - Whose Ethics? + more 🚀ai-ethics #45Oct 2019Deep fake detection challenge, Human knowledge to improve AI, Neural text search data flow, The Causal Inference Book, Netflix Open Sources Polynote + more 🚀reinforcement-learning · ml-research #44Oct 2019Awesome AI Guidelines List, MLFlow simplifying model mgmt, Choosing intuitive visualisations, ML Explainability at AI O'Reilly, Machine learning in 6 steps + more 🚀ai-ethics · explainability #43Oct 2019IEML joins the Linux Foundation, Case studies with NumFocus, ML for business & ops intelligence, The open FairML Book, AI Ethics - whose ethics? + more 🚀ai-ethics · ai-policy #42Oct 2019Serverless for ML in Kubernetes, When a model is too big for prod, Optimising Prod ML at Apple, Turn your ML into interactive apps, Modern Applications at AWS + more 🚀mlops #41Sept 2019One data engine to rule them all, The ImageNet for Code, Tackling data processing at scale, Wisdom from debugging at scale, Netflix reimagining experiments + more 🚀ml-research #40Sept 2019Tricking ML Classifiers, ML deployment paradigm, Five must-know graph algorithms, Survey fairness and bias in ML, Google's OSS differential privacy + more 🚀ml-security · mlops · ai-ethics #39Sept 2019Management for Data Science, Selection vs Detection of outliers, Rules for sharing notebooks, AutoML and AI at Google, 5 sampling algos for everyone + more 🚀forecasting · ml-research · ai-ethics #38Sept 2019Continuous Delivery for ML, AIOps and why you should care, Lang models as knowledge bases, AWS data security best practices, A smooth approach to prod ML + more 🚀mlops · ml-security #37Sept 2019Real Time NLP: Spacy and Kafka, Becoming an ML practitioner, Cracking the black box (XAI), Notebook innovation at Netflix, How AI solves scale complexities + more 🚀mlops · explainability · ml-education #36Aug 2019The state of Federated Learning, Data Science Best Practices, Progression of a Data Scientist, Observability 3 year retrospective, Intro to transformer architecture + more 🚀privacy · llms #35Aug 2019The future of Data Engineering, From self study to ML Engineering, 12 NLP Researchers to Follow, N-Shot Learning with Small Data, Causal Inference: Counterfactuals + more 🚀nlp · ml-research · data-engineering #34Aug 2019A survey on the state of AutoML, Got speech? Voice Applications, Cloud native semantic text search, Learning from adversaries, Python-compatible spreadsheets + more 🚀ml-research · ml-security #33Aug 2019Brooklin for data streaming, Tensorflow AI Interpretability, LIDAR and its smart applications, All hail the (AI) algorithm, Machines (and AI) Gone Wrong + more 🚀data-engineering · explainability · ai-ethics #32Jul 2019E2e ML Pipelines in Enterprise, Code-free deep learning Ludwig, ML Reidentification and Privacy, OSS + AI will take us to the Moon, Large Scale Distributed Systems + more 🚀mlops · privacy #31Jul 2019End-to-end XAI in production, Causal inference to improve UX, Managing ML in enterprise, Intro to Adversarial Examples, The GAN Story so far + more 🚀explainability · ml-research · ml-security #30Jul 2019Production-level ML Explainers, AI Explanations w Counterfactuals, Privacy & Cybersecurity Merging, Hightlights of AI O'Reilly Beijing, 18 Impressive GANs Applications + more 🚀explainability · ml-security · generative-ai #29Jul 2019Major trends in AI & Data, Production-level AI Explanations, 85% of Big Data Projects Fail, ML Mastery on building GANs, Question-answering AI in K8s + more 🚀explainability · data-engineering · ml-security #28Jul 2019The state of AI in 2019, Production ML in 2019, Model governance and ops, The best of modern NLP, Adversarial examples with FGSM + more 🚀mlops · explainability · ml-security #27Jun 2019Distributed AI made easy w Ray, Model Interpretation with Alibi, Principled Machine Learning, Comparing Time Series Models, The quest for high-quality data + more 🚀explainability · reinforcement-learning · mlops #26Jun 2019PyTorch Hub + Reproducible ML, Privacy-preserving AI free course, MLFlow for pipeline management, E2E NLP Pipelines with Kubeflow, The Brains behind SpaCy + more 🚀mlops · privacy · nlp #25Jun 2019Google Research on MLOps, The Book on AutoML, Deep Learning for face detection, Maintainable ETL Pipelines, Counterfactuals for Explainable AI, The Semi-Supervised Revolution + more 🚀ml-research · explainability · computer-vision #24May 2019Standford's Deep NLP Course, The Data Orchestration Layer, The Illustrated Transformer, People plus AI Guidebook, Build a reproducible ML Pipeline, GANs in Action Book + more 🚀nlp · llms · mlops #23May 2019Best practices for ML Engineering, The Journal of Open Source, Visualising Attention in Deep NLP, Pruning API in Tensorflow, ML Explanations with VIBI, Two missing links in serverless + more 🚀explainability · mlops #22May 2019A conversation on practical NLP, "I don't like notebooks" @ ICLR19, A Berkeley view on serverless, Face detection in Python OpenCV, Human-Centric ML Infrastructure, A tutorial on Convolutional NNs + more 🚀nlp · mlops · computer-vision #21May 2019Alibi for black box explanations, Karpathy's tips on training NNs, Nando on learning to learning, A gentle intro to ImageNet, SparkML Kafka Environment, An introduction to computer vision + more 🚀explainability · computer-vision #20Apr 2019An R book for programmers, Real time ML with Kafka & Spark, Deltalake DB & data lake layer, Amunsen data discovery engine, DAWN Machine Learning Tools, Serverless operator reuse + more 🚀mlops · data-engineering #19Apr 2019Advanced NLP with SpaCy, Grid search across scikit models, A chat on ML version control, Beyond black holes with the code, Scientific python in the browser, XAI v0.0.5 released into the wild + more 🚀nlp · explainability #18Apr 2019Reducing Bias in Bios, Data Augmentation for Images, Common statistical tests, AutoML with code generation, Strata Data San Fran Highlights, Stackoverflow Developer Survey + more 🚀ai-ethics · ml-research #17Apr 2019The SpaCy universe of resources, Preprocessing images with Keras, Calling out statistical significance, ✨ Introducing Plotly Express ✨, Time series with TF Probability, Massive Multi-Task Learning + more 🚀nlp · ml-research #16Mar 2019🥳🎉We've reached 1000🎈🎆, 1. Real time ML big data streams, 2. Test Driven Development in AI, 3. Machine Learning Landscape, 4. Detecting ouliers & anomalies, 5. Checklist to debug neural nets, 6. Evaluating model performance + more 🚀data-engineering · mlops · forecasting #15Mar 2019AI debiasing doesn't debias bias, The NLP of human noises, Feature visualisation via activation, The GAN stroke of genious, Essential NLP Tools, Code & Tips, AI comedy generated by humans + more 🚀ai-ethics · explainability · nlp #14Mar 2019Domain knowledge in DL, Federated learning in Tensorflow, Live ggplots for your twitter rants, Evolving Jupyter in the Lab, 8 Books for Computer Vision, Qrash Course on RL, Introducing data labelling section + more 🚀privacy · reinforcement-learning #13Mar 2019A deep dive on ML versioning, How to become an ML engineer, Developing competence in DL, 🚀 beyond Jupyter with Jupytext, Tensorflow 2.0 Alpha is out, Adversarial drawing with GANs + more 🚀ml-education · ml-research #12Mar 2019Bias and Explainability in ML, Federated Learning with PyTorch, Data Visualisation Deep Dive, Practical recommenders tutorial, Learning Curves in ML evaluation, Machine Learning Cybersecurity + more 🚀explainability · privacy · recommender-systems #11Feb 2019Top Sources for ML Datasets, Major RL Papers in 2018, Get Better Deep Learning Results, A beginners intro to deep NLP, This AirBnB does not exist, Feature flags in Machine Learning + more 🚀reinforcement-learning · ml-education · nlp #10Feb 2019Delayed impact of FAIR ML, Getting started with Google Collab, Data Science Salaries in Europe, Building tensorflow from scratch, Better language models, AI where? In the blockchain 💰 + more 🚀ai-ethics · ml-education #9Feb 2019Tools to scale your production ML, Outlier Detection by ML for ML, How Facebook scales their ML, Transfer Learning to improve DL, Towards Federated Learning, Quantum ML Online Course + more 🚀mlops · forecasting · privacy #8Feb 2019Papers with code update, NVIDIA's AI generated graphics, Serverless and machine learning, Tensorflow 2.0 APIs, 16k research paper analysis, Google Brain Research in 2018 + more 🚀ml-research #7Jan 2019AI vs Human - Starcraft II Edition, From zero to distributed, Tensorflow 2.0, I heard you like machine learning, Impact of Learning Rate, Monitoring Parking with R-CNN + more 🚀reinforcement-learning · ml-research · computer-vision #6Jan 2019Machine Learning Michaelangelo, The new role of data engineers, Why data scientists love MLOps, Seeing theory: Probability & stats, Data Science vs Engineering, Accelerate ML with Batch Norm + more 🚀mlops · data-engineering · ml-research #5Jan 2019XAI - eXplainability library for AI, Deep neural inspection, AI against alzheimer's disease, Differential privacy for tensorflow, The role of ML in databases + more 🚀explainability · ml-security #4Jan 2019Andrew Ng and Reproducibility, 2018 machine learning nostalgia, Magic of Feature Engineering, Explainability/bias with tensorflow, Ensembles and more ensembles, CI/CD for Machine Learning + more 🚀mlops · explainability