Walker Rowe – BMC Blogs https://www.bmc.com/blogs BMC Software Tue, 18 Sep 2018 12:46:05 +0000 en-US hourly 1 https://blogs.bmc.com/wp-content/uploads/2016/04/bmc_favicon-300x300-150x150.png Walker Rowe – BMC Blogs https://www.bmc.com/blogs 32 32 GitHub vs GitLab vs Bitbucket: What’s The Difference and How To Choose https://www.bmc.com/blogs/github-vs-gitlab-vs-bitbucket/ Tue, 18 Sep 2018 00:00:17 +0000 https://www.bmc.com/blogs/?p=12819 Github, GitLab, and Bitbucket are software version control repositories. These let programmers check out code and then push updates back to the cloud, normally using Git or Mercurial command line tools. You can also install some of these products in-house. And they can also be connected to a host of applications, like NodeJS or Zeppelin […]]]> Big Data vs Analytics vs Data Science: What’s The Difference? https://www.bmc.com/blogs/big-data-vs-analytics/ Mon, 10 Sep 2018 00:00:42 +0000 https://www.bmc.com/blogs/?p=12778 There is much confusion from people who do not work with the technology what the difference is between big data and analytics. Often you see the names big data analytics, big data, analytics, or data science. What do these mean? In brief, big data is the infrastructure that supports analytics. Analytics is applied mathematics. Analytics […]]]> Testing Frameworks: Unit Tests, Functional Tests, TDD & BDD Explained https://www.bmc.com/blogs/testing-frameworks-unit-functional-tdd-bdd/ Tue, 04 Sep 2018 00:00:06 +0000 https://www.bmc.com/blogs/?p=12731 Programmers can write unit and functional tests using frameworks. Unit tests test individual lines of code. Functional tests test something larger, such as whether a transaction can still be executed. Other frameworks test that the application works on multiple versions of the targeted operating systems, different screen orientations on mobile devices, different browsers, and with […]]]> Introduction to Google Cloud TPUs (Tensor Processing Unit) for ML Acceleration https://www.bmc.com/blogs/google-cloud-tpu/ Fri, 24 Aug 2018 00:00:17 +0000 https://www.bmc.com/blogs/?p=12712 We already wrote how machine learning frameworks are using NVIDIA GPUs (graphical processing units) to speed machine learning. Now Google is taking that idea and using it to speed machine learning using their own ASIC hardware, called TPUs, Tensor Processing Units. What Google has really done is take technology invented by NVIDIA (GPUs) and pushed […]]]> How Keras Machine Language API Makes TensorFlow Easier https://www.bmc.com/blogs/how-keras-machine-language-api-makes-tensorflow-easier/ Fri, 03 Aug 2018 11:34:20 +0000 http://www.bmc.com:80/blogs/?p=12520 Keras is a Python framework designed to make working with Tensorflow (also written in Python) easier. It builds neural networks, which, of course, are used for classification problems. The example problem below is binary classification. You can find the code here. The binary classification problem here is to determine whether a customer will buy something […]]]> Tuning Machine Language Models for Accuracy https://www.bmc.com/blogs/tuning-machine-language-models-for-accuracy/ Fri, 20 Jul 2018 00:00:48 +0000 http://www.bmc.com:80/blogs/?p=12567 Continuing with our explanations of how to measure the accuracy of an ML model, here we discuss two metrics that you can use with classification models: accuracy and receiver operating characteristic area under curve. These are some of the metrics suitable for classification problems, such a logistic regression and neural networks. There are others that […]]]> Bias and Variance in Machine Learning https://www.bmc.com/blogs/bias-variance-machine-learning/ Tue, 10 Jul 2018 00:00:53 +0000 http://www.bmc.com:80/blogs/?p=12595 The risk in following ML models is they could be based on false assumptions and skewed by noise and outliers. That could lead to making bad predictions. That is why ML cannot be a black box. The user must understand the data and algorithms if the models are to be trusted. So here we look […]]]> Mean Squared Error, R2, and Variance in Regression Analysis https://www.bmc.com/blogs/mean-squared-error-r2-and-variance-in-regression-analysis/ Thu, 05 Jul 2018 00:48:43 +0000 http://www.bmc.com:80/blogs/?p=12606 Here we introduce some terms important to machine learning; variance, r2 score, and mean square error. We illustrate with these concepts using scikit-learn. It is important to understand these metrics to determine whether regression models are accurate or misleading. Following a flawed model is a bad idea. So it is important that you can quantify […]]]> Getting Started with scikit-learn https://www.bmc.com/blogs/scikit-learn/ Wed, 27 Jun 2018 00:00:00 +0000 http://www.bmc.com/blogs/?p=12419 Here we explore another machine learning framework, scikit-learn, as well as show how to use matplotlib, to draw graphs. The scikit-learn python ML API predates Apache Spark and TensorFlow, which is to say it has been around longer than big data. It has long been used by those who see themselves as pure data scientists, […]]]> Introduction to Spark’s Machine Learning Pipeline https://www.bmc.com/blogs/introduction-to-sparks-machine-learning-pipeline/ Wed, 06 Jun 2018 00:00:36 +0000 http://www.bmc.com/blogs/?p=12336 Here we explain what is a Spark machine learning pipeline. We will do this by converting existing code that we wrote, which is done in stages, to pipeline format. This will run all the data transformation and model fit operations under the pipeline mechanism. The existing Apache Spark ML code is explained in two blog […]]]>