Better Code Quality for Data Science, Julia Antokhina

Better Code Quality for Data Science At this tutorial, I'll show the full pipeline of setting up code quality checks and tests at your repository. I'll focus on quick wins and solutions for common difficulties. These instruments seem simple but may help you a lot not only improve your own skills but work better as a team of Data Scientists Julia Antokhina, Data Scientist @ Mobile TeleSystems All talks & tutorials from Machine Learning REPA Week 2021: - All talks at Track 1 - Machine Learning Product and Team Management: https://youtube.com/playlist?list=PLlxErbAvYYLBa2uQkROxn4OZJvGQACn8i - All talks at Track 2 - ML pipelines automation. Code and Data version control. Reproducibility. MLOps: https://youtube.com/playlist?list=PLlxErbAvYYLDRP6cHtVP76f2g5Yoh6c5R Links: - Learn more about ML REPA: https://mlrepa.com/ - Learn more about LeanDS: https://leands.ru/ - Learn more about DataTalks.Club: https://datatalks.club/ - Machine Learning REPA Week 2021 Online Conference: https://mlrepa.com/mlrepa-week-2021 Join us: DataTalks.Club #mlrepa - https://datatalks-club.slack.com/archives/C01Q6698JTV Slack ODS.ai #mlrepa -https://opendatascience.slack.com/archives/C019A9H5V0X #mlrepa #machinelearning #datascience #reproducibility #mlops #artificialintelligence #ai #python #deeplearning #technology #programming #coding #bigdata #computerscience #data #dataanalytics #tech #datascientist #pythonprogramming #ml #developer #software #robotics #innovation #coder #datavisualization #analytics #neuralnetworks #leands #automation