Elena SamuylovainTowards Data ScienceMonitoring unstructured data for LLM and NLPA code tutorial on using text descriptorsJun 27, 2023Jun 27, 2023
Elena SamuylovainTowards Data ScienceHow to Measure Drift in ML EmbeddingsWe evaluated five embedding drift detection methodsJun 14, 20234Jun 14, 20234
Elena SamuylovainTowards Data ScienceMonitoring NLP models in productionA code tutorial on detecting drift in text dataFeb 20, 20236Feb 20, 20236
Elena SamuylovainTowards Data ScienceMonitoring ML systems in production — which metrics should you track?When one mentions “ML monitoring,” this can mean many things. Are you tracking service latency? Model accuracy? Data quality? The share of…Jul 18, 2022Jul 18, 2022
Elena SamuylovainDeepnoteEvaluating Data Drift and ML Model Performance with Evidently and DeepnoteHow to quickly build beautiful interactive reports on your model.Dec 14, 2021Dec 14, 2021
Elena SamuylovainTowards Data Science“My data drifted. What’s next?” How to handle ML model drift in production.An introductory overview of the possible steps.Dec 1, 20212Dec 1, 20212
Elena SamuylovainTowards Data ScienceWhat is the difference between outlier detection and data drift detection?Explained simplyNov 8, 20211Nov 8, 20211
Elena SamuylovainTowards Data ScienceA Machine Learning Model Monitoring Checklist: 7 Things to TrackHow to monitor your models and which open-source tools to useApr 19, 20211Apr 19, 20211
Elena SamuylovainTowards Data ScienceMachine Learning in Production: Why You Should Care About Data and Concept DriftNo model lasts forever. Even if the data quality is fine, the model itself can start degrading. What does this mean in practice?Dec 7, 20208Dec 7, 20208
Elena SamuylovainTowards Data ScienceMachine Learning Monitoring: What It Is, and What We Are MissingIs there life after deployment?Sep 10, 20202Sep 10, 20202