Run SageMaker Processing Jobs from Step Functions

By Simon Löw |

SageMaker Processing jobs are a great way to run heavy processing steps in your machine learning pipeline, without the need for setting up an ECS cluster or a Batch job. But integrating them into a Step Functions workflow can be tricky, since there is no direct service integration for processing jobs. In the following I will show you, how you can easily run and monitor SageMaker Processing jobs from Step Functions.
More..

XGBoost hyperparameter tuning with Bayesian optimization using Python

By Simon Löw |

XGBoost is one of the leading algorithms in data science right now, giving unparalleled performance on many Kaggle competitions and real-world problems. Unfortunately, XGBoost has a lot of hyperparameters that need to be tuned to achieve optimal performance. In the following, I will show you how you can implement Bayesian optimization in Python to automatically find the best hyperparameters easily and efficiently. What you will learn: What Bayesian optimization is and why it is superior to random or grid search How to implement Bayesian optimization in Python How you can automatically optimize your XGBoost hyperparameters using Bayesian optimization What is Bayesian optimization?
More..

SqueezeNet and MobileNet: Deep learning models for mobile phones

By Simon Löw |

Do you want to use image recognition in your mobile app? To deploy machine learning models to your phone and get fast predictions, the model size is key. SqueezeNet and MobileNet are two network architectures that are well suited for mobile phones and achieve impressive accuracy levels above AlexNet. While the current trend is to make deeper and deeper networks to improve accuracy, SqueezeNet and MobileNet both try to keep the models small and efficient without sacrificing too much accuracy.
More..

Image classification with Convolutional Neural Networks

By Simon Löw |

Convolutional Neural Networks are the state of art approach to classify images. In this post I will show you what Convolutional neural networks (CNNs) are and how you can use them for image classification. Together we will apply them to the famous CIFAR-10 data-set and classify all the images in 10 different categories. What you will learn: What CNNs are How you can implement CNNs with Keras and Python in no time What CIFAR-10 data-set is and how you can use it with Keras The Problem: CIFAR-10 The CIFAR-10 data set servers as a the perfect example here.
More..