I know you're itching to see something real. So let's dive into an example. I will say that the code shown here is from a Mac. It'll work on Linux with extremely minor changes, and it can be made to work on Windows with some minor changes. For brevity, I'll just assert that everything I'm showing here is on a *nix terminal, specifically on a Mac. What I'm going to do is first demonstrate local development.

Machine Learning is an area of research that allows machines the ability to learn without being directly programmed. Machine Learning development is in trend as many students, teachers, developers, and data scientists use machine learning to develop various projects and products. However, developing machine learning models require high system requirement specifications as sometimes the model training process can go from 2 hours to 2 days and more. So low-end systems can not handle training of good machine learning models or even if they somehow train models, critical system issues are likely to occur. However, there are many open-source Machine Learning environments available that do not require any system requirement specification and use cloud infrastructure to train your model in the most optimal time possible.

Microsoft provides a free training environment powered by Jupyter Notebooks/Labs where sharing and collaborating is central to the user experience. Because they've made it so easy to share projects with peers, we created a set of projects for you to explore. While this tool is still in "beta" mode, we're excited about where it will go in the near future! Once inside the Jupyter Lab environment, notice that there are three example notebooks you can choose between. If you want to follow along with this post, I will be using the image-classification-examples project and the dogs_and_cats.ipynb

Across Visual Studio Code and Azure Notebooks, January brought numerous exciting updates to the AI and Machine Learning tooling for Python! The Python extension for VS Code first introduced an interactive data science experience in the last Oct update. With this release, we brought the power of Jupyter Notebooks into VS Code. Many feature additions have been released since, including remote Jupyter support, ability to export Python code to Jupyter Notebooks, etc. The most noticeable enhancement in the Jan 2019 update allows code to be typed and executed directly in the Python Interactive window.

Today we are excited to strengthen our commitment to supporting PyTorch as a first-class framework on Azure, with exciting new capabilities in our Azure Machine Learning public preview refresh. In addition, our PyTorch support extends deeply across many of our AI Platform services and tooling, which we will highlight below. During the past two years since PyTorch's first release in October 2016, we've witnessed the rapid and organic adoption of the deep learning framework among academia, industry, and the AI community at large. While PyTorch's Python-first integration and imperative style have long made the framework a hit among researchers, the latest PyTorch 1.0 release brings the production-level readiness and scalability needed to make it a true end-to-end deep learning platform, from prototyping to production. Azure Machine Learning (Azure ML) service is a cloud-based service that enables data scientists to carry out end-to-end machine learning workflows, from data preparation and training to model management and deployment.

Matt Winkler delivered a talk at Microsoft Build 2018 explaining what is new in Azure Machine Learning. The Azure Machine Learning platform is built from the hardware level up. It is open to whatever tools and frameworks of your choice. If it runs on Python, you can do it within the tools and frameworks. Services come in three flavors: conversational, pre-trained, and custom AI.

They start off with the Titanic dataset and show how to use Azure Notebooks to create a random forest classifier from scratch. The whole machine learning process is detailed from data acquisition, to data cleaning, and finally creating a machine learning model. If you want to read through Lo's machine learning intro primer as well as get the Titanic dataset you can find it here: