Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. The World Health Organization states the following…

As we are leveraging data for making significant decisions that affect individual lives in domains such as health care, justice, finance, education, marketing, and employment, it is important to ensure the safe, ethical, and responsible use of AI. In collaboration with the Aether Committee and its working groups, Microsoft is bringing the latest research in responsible AI to Azure: these new responsible ML capabilities in Azure Machine Learning and our open source toolkits, empower data scientists and developers to understand machine learning models, protect people and their data, and control the end-to-end machine learning process. In 2015, Claire Cain Miller wrote on The New York Times that there was a widespread belief that software and algorithms that rely on data were objective. Five years later, we know for sure that AI is not free of human influence. Data is created, stored, and processed by people, machine learning algorithms are written and maintained by people, and AI applications simply reflect people's attitudes and behavior.

It's (mostly) true that Data Scientists do not care about infrastructure. Indeed, even though DevOps is a very interesting field, most of them are not exactly eager to start a VM, allocate the needed resources, configure the network, ssh into the machine, build a docker image and launch a Jupyter Notebook server. To cut to the chase, in this story, we create a ready to use, GPU accelerated Deep Learning environment, that has already TensorFlow and PyTorch installed. To do that we need to create the Dockerfile that describes the environment, build it and use it as the image of the Notebook server inside a Kubeflow instance. So, without further ado let's see the Dockerfile and walk through it step by step.

As interest in Artificial Intelligence (AI), and specifically Machine Learning (ML), grows and more engineers enter this popular field, the lack of de facto standards and frameworks for how work should be done is becoming more apparent. A new focus on optimizing the ML delivery pipeline is starting to gain momentum. Data scientists are becoming more involved in the delivery pipeline of products, and it is a non-trivial task ensuring that their work survives the delivery process. Of course, this isn't a new problem: in the past, traditional software development teams would throw their work "over the wall" to the operations team to serve in production with little to no context. A community effort to solve the inevitable mess resulted in what we now think of as DevOps, removing the wall between development and operations to drive increased efficiency and improve product quality.