30:17 Haris Pozidis- Snap ML: Accelerated, Accurate,Efficient,Machine Learning| PyData Global 2020 PyData
29:25 Gael Varoquaux- Dirty Data Science Machine Learning On Non Curated Data| PyData Global 2020 PyData
31:02 Arnaud Van Looveren - Monitoring machine learning models in production | PyData Global 2020 PyData
27:48 Liucija Latanauskaite - Why I didn't use deep learning for my image recognition| PyData Global 2020 PyData
1:27:15 Markus Loning - Introduction to Machine Learning with Time Series | PyData Fest Amsterdam 2020 PyData
31:41 Jim Dowling - Hopsworks.AI - A feature Store for Machine Learning | PyData Fest Amsterdam 2020 PyData
1:09:25 MLflow - An open platform for the machine learning lifecycle - Abdulrahman Alfozan | PyData Riyadh PyData
30:26 Cyrus Vahid: Anyone can Build Great Deep Learning Applications - Deep Numpy | PyData Warsaw 2019 PyData
29:36 Pawel Cyrta: Sound Modelling - parametric methods and deep learning... | PyData Warsaw 2019 PyData
26:46 Kasimov & Petrova: Machine Learning on big data in security applications | PyData Warsaw 2019 PyData
34:20 Robert Kostrzewski: Modern Machine Learning flow with Quilt and Polyaxon | PyData Warsaw 2019 PyData
25:06 Marina Volkova: Machine Learning Spacecraft Designing for Cybersecurity | PyData Warsaw 2019 PyData
56:36 Vladimir Osin, Milan Mulji: Managing Machine Learning Lifecycle with MLflow | PyData Eindhoven 2019 PyData
14:06 Axel Goblet: Scheduling machine learning pipelines using Apache Airflow | PyData Eindhoven 2019 PyData
1:13:11 Hayley Song: Experimental Machine Learning with Holoviz and PyTorch in Jupeyterlab | PyData LA 2019 PyData
39:25 Avik Das: Dynamics Programming for Machine Learning- Hidden Markov Models | PyData LA 2019 PyData
32:11 Dmitry Petrov: Machine Learning Models Versioning Using Open Source Tools | PyData LA 2019 PyData
28:46 Hao Jin: Accelerate NumPy Data Science Workloads and Deep Learning Applications | PyData LA 2019 PyData
45:02 Dr. Benjamin Werthmann: Law, ethics and machine learning - a curious ménage... | PyData Berlin 2019 PyData
50:21 Peter Wang: Rethinking Open Source in the Era of Cloud & Machine Learning | PyData Berlin 2019 PyData
29:41 Benjamin Bossan: skorch: A scikit-learn compatible neural network library... | PyData Berlin 2019 PyData
48:52 Sarah Diot-Girard: Privacy-preserving Machine Learning for text processing | PyData Berlin 2019 PyData
29:58 Adrin Jalali: Current affairs, updates, and the roadmap of scikit-learn and... | PyData Berlin 2019 PyData
28:54 Andreas Hantsch: Machine learning with little data - from digital twin to... | PyData Berlin 2019 PyData
31:02 Alexander Engelhardt: Interpretable Machine Learning: How to make black box... | PyData Berlin 2019 PyData
33:12 Jacob Barhak: Visualizing Machine Learning of Units of Measure using PyViz | PyData Austin 2019 PyData
34:17 Saloni Jain: Speeding up Machine Learning tasks using GPUs in Python | PyData Austin 2019 PyData
32:43 Samuel Rochette: Quantifying uncertainty in machine learning models | PyData New York 2019 PyData
31:03 Aditya Lahiri: Dealing With Imbalanced Classes in Machine Learning | PyData New York 2019 PyData
32:13 Thomas J Fan: Deep Dive into scikit-learn's HistGradientBoosting Classifier.. | PyData New York 2019 PyData
43:55 Moussa Taifi: Clean Machine Learning Code: Practical Software Engineering... | PyData New York 2019 PyData
34:29 Marianne Hoogeveen: The physics of deep learning using tensor networks | PyData New York City 2019 PyData
25:45 PyData Tel Aviv Meetup: Monitoring Machine Learning at Scale - Naama Horesh and Anna Reznikov PyData
1:32:04 How to easily set up and version control your Machine Learning Pipelines | PyData Amsterdam 2019 PyData
38:47 Alejandro Saucedo: Guide towards algorithm explainability in machine learning | PyData London 2019 PyData
38:54 Elina Naydenova: Bridging health inequalities through machine learning | PyData London 2019 PyData
35:38 Maria Navarro: Quantifying uncertainty in Machine Learning predictions | PyData London 2019 PyData
40:25 Igor Gotlibovych: Deep Learning and Time Series Forecasting for Smarter Energy | PyData London 2019 PyData
19:29 Marianne Hoogeveen: Plant Factory: Sensor, Data, Machine Learning | PyData Amsterdam 2019 PyData
34:44 Benjamin Bengfort: Visual Diagnostics for More Effective Machine Learning | PyData Miami 2019 PyData
1:39:00 Mash Zahid: Applying Rigorous Machine Learning Methods in Business Strategy | PyData Miami 2019 PyData
20:37 It is never too much: training deep learning models with more than one modality - Adam Słucki PyData
38:11 The Lifecycle of Artificial Intelligence with IBM's Deep Learning as a Service - Justin McCoy PyData
46:42 PyData Ann Arbor: Haitham Maya & Brandon Stange | Methods for Interpretable Machine Learning PyData
28:43 Learning to Scale Data Science, Machine Learning, and Pandas with Ray and Modin - Devin Petersohn PyData
1:26:04 Tamara Louie: Applying Statistical Modeling & Machine Learning to Perform Time-Series Forecasting PyData
31:42 Detecting Signed and Unsigned Documents with Deep Learning - Beyond Transfer... - Jordan Bramble PyData
29:15 Deploy and Use a Multiframework Distributed Deep Learning Platform on... - Animesh Singh, Tommy Li PyData
28:48 Dean Allsopp - Hermeneutic Investigations: What Can Interpretable Machine Learning Do Today? PyData
44:17 The Face of Nanomaterials: Insightful Classification Using Deep Learning - Angelo Ziletti PyData
