Wilder Rodrigues: Improving Machine Learning Workflow | PyData Amsterdam 2019 скачать в хорошем качестве

Wilder Rodrigues: Improving Machine Learning Workflow | PyData Amsterdam 2019

As machine learning practitioners, we know how hard it can be to have a smooth process around training and serving production-ready models. Processing the data, saving all the relevant artifacts to make experiments reproducible, packaging and serving the models; all these individual components can be a nightmare to implement and manage. MLflow - an amazing new platform for managing the ML life cycle - comes to the rescue. In this talk, we will present a Docker powered infrastructure that combines MLflow, JupyterHub and Minio (S3 compliant storage) that aims to solve the above problems and improve your machine learning workflow. In addition, we will present a CI-CD pipeline which is responsible for fetching production-ready models from storage, and building and publishing Docker images that serve these models in production. With this in place, tasks like experimenting, releasing and serving models become more straightforward and less manual. We will explore how this infrastructure can speed up our work, make it less error-prone, and help us manage all ML related artifacts better. We will start the talk by presenting the infrastructure and its components and how they address practitioners’ pain points. Next, we will show how our solution helps to train models in a structured way. And lastly, we will demonstrate how to automate packaging and serving of the models prior to deployment www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...