Here’s a great walkthrough of how to create a docker image that exclusively uses a custom model to run predictions and returns back the result.

This provides context isolation for the application running the model and avoids any non-reproducibility issue.

When dealing with python versioning you can easily get lost with all the possible combinations of your favorite library version, and the versions of the interpreter. Many different projects, specially the ones involving Tensorflow and Keras, require a very specific library version to run. Many other projects are not […]

Julie Lerman shares some insight and great tips for working with Entity Framework in Docker. You will see thing tips for working with the SQL Server Docker image, using environment variables for passports, using the Docker tools for Visual Studio and so much more!

Useful Links

If you ever wanted to up your home networking game with backup to Dropbox and secure remote access from everywhere through your own VPN, all based on Docker containers, then check out what Andreas Spiess has done.

In this video, he covers:

  • Install Docker with many containers like Mosquitto, Node-Red, Grafana, influxDB, Postgres, Portainer, and Adminer
  • Increase the live expectancy of your SD card by disabling swapping and by installing log2ram- Automatically backup all valuable data to the cloud, in our case, to Dropbox
  • Setup PiVPN to remotely and securely access our home network from anywhere in the world- Besides that, you will learn a lot of useful things about Docker containers

Links & Code:

As AI and Machine Learning become more crucial to the enterprise, systems and processes need to be put in place to manage the output of data science teams: the machine learning models.

This is a field commonly referred to as MLOps and, not surprisingly, is based hon DevOps.

edureka! explores the basics of DevOps in this tutorial video for beginners.

Audio starts around the 1:32 mark.

Python is a powerful stack running many websites that you know and love, but it can be difficult to get your development environment running smoothly, especially when using technologies like Docker.

In this session from Build 2019, learn how to set up the ultimate containerized Python development environment in Visual Studio Code, deploy your application to Azure with a few clicks, and use Azure DevOps to automate your deployments.

After pondering some of the implications of Siraj Raval’s predictions for AI in 2019, it’s time to move learning containers to the top of my learning queue.

In this video, Damian interviews Cloud Advocate Jay Gordon at Microsoft Ignite in Berlin. Containers are still new for a lot of people and with the huge list of buzzwords, it’s hard to know where to get started. Jay shows how easy it is to get started running your first container in Azure, right from scratch.

Resources:

Follow Jay on Twitter: @jaydestro
Follow Damian on Twitter: @damovisa

Here’s an interesting talk from PyCon Germany by Joshua Görner, a Data Scientist at BMW.

From the video description:

Interactive notebooks like Jupyter have become more and more popular in the recent past and build the core of many data scientist’s workplace. Being accessed via web browser they allow scientists to easily structure their work by combining code and documentation. Yet notebooks often lead to isolated and disposable analysis artifacts. Keeping the computation inside those notebooks does not allow for convenient concurrent model training, model exposure or scheduled model retraining. Those issues can be addressed by taking advantage of recent developments in the discipline of software engineering. Over the past years containerization became the technology of choice for crafting and deploying applications. Building a data science platform that allows for easy access (via notebooks), flexibility and reproducibility (via containerization) combines the best of both worlds and addresses Data Scientist’s hidden needs.