As a machine learning project grows, so should its infrastructure. In this talk, Alejandro Saucedo covers some of the key trends in machine learning operations, as well as libraries to watch in 2019.

The talk is based on the “Awesome Machine Learning Operations” list maintained by The Institute for Ethical AI & Machine Learning, and focuses on the topics of reproducibility, orchestration and explainability.

ONNX is a open format to represent deep learning models that is supported by various frameworks and tools. This format makes it easier to interoperate between frameworks and to maximize the reach of your hardware optimization investments

In this episode, Seth Juarez (@sethjuarez) sits with Rich to show us how we can use the ONNX runtime inside of our .NET applications. He gives us a quick introduction to training a model with PyTorch, and also explains some foundational concepts around prediction accuracy.

Useful Links

Neural networks have proven themselves very capable of performing tasks that have eluded researchers for years. When you find out that no one really knows why neural networks behave the way they do, it only adds to their mystique.

The fields of eXplainable AI (XAI) and Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) aim to provide insight into how neural networks comes to the conclusions that they do.

Ever wondered what breed that dog or cat is? In this show, you’ll learn how to train, optimize and deploy a deep learning model using Azure Notebooks, Azure Machine Learning Service, and Visual Studio Code using Python. Using transfer learning to retrain a mobilenet model via Tensorflow to recognize dog and cat breeds using the Oxford IIIT Pet Dataset.

Next, watch how to optimize that model using the Azure Machine Learning Service HyperDrive service, and improve the accuracy of our model to over 90%. Finally, we’ll put on our developer hat, and use Visual Studio Code and our Python Extension to deploy and test our model. Along the way you’ll see cool features like our new Jupyter-powered interactive programming experience in VS Code, our AI powered IntelliSense feature called Intellicode, and our Azure Machine Learning extension.

HyperDrive service, and improve the accuracy of our model to over 90%. Finally, we’ll put on our developer hat, and use Visual Studio Code and our Python Extension to deploy and test our model. Along the way you’ll see cool features like our new Jupyter-powered interactive programming experience in VS Code, our AI powered IntelliSense feature called Intellicode, and our Azure Machine Learning extension.

Github repo for all code used in the show: https://github.com/microsoft/connect-petdetector

Blog post introducing the new features in Azure Notebooks: https://github.com/Microsoft/AzureNotebooks/wiki/Azure-Notebooks-at-Microsoft-Connect()-2018

Blog post introducing our data science features in our Python extension: https://blogs.msdn.microsoft.com/pythonengineering/2018/11/08/data-science-with-python-in-visual-studio-code/

Azure Notebooks: https://notebooks.azure.com

Python Extension: https://marketplace.visualstudio.com/items?itemName=ms-python.python

Azure Machine Learning Extension: https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai

Visual Studio Code: https://code.visualstudio.com/

In this talk from the most recent O’Reilly AI Conference, Laurence Moroney from Google talked about Machine Learning, AI, Deep Learning and more, and how they fit the programmers toolkit. He introduced what it’s all about, cutting through the hype, to show the opportunities that are available in Machine Learning. He also introduced TensorFlow, and how it’s a framework that’s designed to make Machine Learning easy and accessible, and how intelligent apps that use ML can run in a variety of places including mobile, web and IoT.

In what could be a sign of things to come, machine learning algorithms that apply NLP techniques to source code could replace the need for human reviewers. The notion of a computer reviewing source code underscores that it’s not just low skill jobs at risk in the Fourth Industrial Revolution.

In this video, Francesc Campoy, VP Developer Relations at source{d} talks about ML-assisted code review (Lookout) and the Public Git Archive.

You’ll learn how and why source{d} makes uses a dataset based on many GitHub repos available as public datasets to train its models and how “assisted code reviews” apply ML, image processing, and NLP concepts – like word2vec – to code.

Francesc shares his favorite MLonCode moments, why it’s made him a better developer, what’s coming next, and where you can get started.