Agile Velocity is arguably the most popular software development metric in the world. It’s a very powerful metric when used for individual team sprint capacity planning. And there are two things we know about power… it comes with great responsibility and it corrupts. Read More
Tag Archives: DevOps
Corporate Tools for GPU Access and Software Development
Secure the software development lifecycle with machine learning
Every day, software developers stare down a long list of features and bugs that need to be addressed. Security professionals try to help by using automated tools to prioritize security bugs, but too often, engineers waste time on false positives or miss a critical security vulnerability that has been misclassified. To tackle this problem data science and security teams came together to explore how machine learning could help. We discovered that by pairing machine learning models with security experts, we can significantly improve the identification and classification of security bugs.
At Microsoft, 47,000 developers generate nearly 30 thousand bugs a month. These items get stored across over 100 AzureDevOps and GitHub repositories. To better label and prioritize bugs at that scale, we couldn’t just apply more people to the problem. However, large volumes of semi-curated data are perfect for machine learning. Since 2001 Microsoft has collected 13 million work items and bugs. We used that data to develop a process and machine learning model that correctly distinguishes between security and non-security bugs 99 percent of the time and accurately identifies the critical, high priority security bugs, 97 percent of the time. This is an overview of how we did it. Read More
The End of Agile? Not a Chance.
There’s been a fair amount of opining lately about the end of Agile, the 19-year-old movement that began in software development and has made its way through the workforce as an alternative to more traditional ways of working. People seem to be worried that a strategy that once was considered, lean, mean, and productive, has now become cultish, bloated, and ineffectual. But Agile continues to work, and it continues to work well — when implemented in a disciplined way. Read More
Operationalizing AI
When AI practitioners talk about taking their machine learning models and deploying them into real-world environments, they don’t call it deployment. Instead the term that’s used is “operationalizing”. This might be confusing for traditional IT operations managers and applications developers. Why don’t we deploy or put into production AI models? What does AI operationalization mean and how is it different from the typical application development and IT systems deployment? Read More
There’s No Such Thing As The Machine Learning Platform
In the past few years, you might have noticed the increasing pace at which vendors are rolling out “platforms” that serve the AI ecosystem, namely addressing data science and machine learning (ML) needs. The “Data Science Platform” and “Machine Learning Platform” are at the front lines of the battle for the mind share and wallets of data scientists, ML project managers, and others that manage AI projects and initiatives. If you’re a major technology vendor and you don’t have some sort of big play in the AI space, then you risk rapidly becoming irrelevant. But what exactly are these platforms and why is there such an intense market share grab going on? Read More
How To Leverage Deep Learning For Automation Of Mobile Applications
Mobile applications have already made a mark on the digital front. With a large number of applications already on the Google Play Store and Apple Store. There are applications for almost everything today. But, as the markets of mobile apps expand, they face new challenges and obstacles to be overcome.
Deep Learning is a subsidiary technology for Artificial Intelligence. It uses algorithms to parse the data and provide deep insights into the applications and their issues. Often, time constraints and deadline pressures get the better of developers and do not allow the developers or higher management to test the app properly before the grand launch and here, deep learning can help automate the mobile application testing and deployment.
The interactions between the user and system are facilitated through the GUI(Graphic User Interface). Especially, an interaction may include clicking, scrolling, or inputting text into a GUI element, such as a button, an image, or a text block. An input generator can produce interactions for several tests, Read More
MLflow: an Open Source Machine Learning Platform
Everyone who has tried to do machine learning development knows that it is complex. Beyond the usual concerns in the software development, machine learning (ML) development comes with multiple new challenges. MLFlow is an open interface, open source machine learning platform, released by DataBricks in 2018, that can be used to create an internal ML platform for tracking, packaging, and deploying ML models.
Version Control ML Model
Machine Learning operations (let’s call it mlOps under the current buzzword pattern xxOps) are quite different from traditional software development operations (devOps). One of the reasons is that ML experiments demand large dataset and model artifact besides code (small plain file).
This post presents a solution to version control machine learning models with git and dvc (Data Version Control). Read More
State of the art result for all Machine Learning Problems
Github repository providing state-of-the-art (SoTA) results for machine learning problems. Links categorized as: Supervised Learning, Semi-Supervised Learning, Unsupervised Learning, Transfer Learning, and Reinforcement learning, with details pointing to Research Paper, Datasets, Metric, Source Code, and Year of publication. Read More
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