Sebastian NeubauerDevOps, Infrastructure, IDEs/ Jupyter, Use Cases
In this talk I will walk you through the proper setup of a local python development environment using docker.
Daniel HeinzeComputer Vision, Deep Learning, DevOps, IDEs/ Jupyter, Machine Learning, APIs, Python
Build a Machine Learning pipeline with Jupyter and Azure: https://notebooks.azure.com/Starlord/projects/pycon-ml-jupyter-azure
Dr. Hendrik NiemeyerBig Data, DevOps, Infrastructure
Learn how to build and ship Python software with Docker Containers.
Raphael PierzinaDevOps, Web, Data Engineering
Learn how to get started with developing automated tests in Python with the pytest test framework!
Christian BarraDevOps, Infrastructure, Web, APIs, Use Cases
Ready to learn about Kubernetes? Join the workshop and be prepared to play with yaml files!
Hari Kishore SirivellaDjango, DevOps, Infrastructure
Monitoring infrastructure and application using Django, Sensu and Celery.
Steph SamsonDevOps, Infrastructure, Use Cases
Learn how to make package and dependency management easier with Poetry.
Dr. Tania AllardAlgorithms, Big Data, Data Science, DevOps, Machine Learning
Devops for the busy data scientist: learn how to leverage these practices to improve your workflows
Yetunde DadaData Science, DevOps, Machine Learning, Data Engineering
Learn how easy it is to apply software engineering principles to your data science and data engineering code. Expect an overview of Kedro, a library that implements best practices for data pipelines with an eye towards productionizing ML models.
Katharina RaschArtificial Intelligence, Data Science, DevOps, Infrastructure
There is now a wealth of tools that support data science best practices (e.g. tracking experiments, versioning data). Let’s take a look at which tools are available and which ones might be right for your project.
Simone RobuttiCode-Review, DevOps
Are you confused about the difference between pyenv and pipenv? Or between pip and Pypi? We will talk about them and many other Python tools