Over the last decade, the industry has gone from celebrating the rise of the “central ML team” to questioning whether it should exist. I can’t help but feel like I’m watching Rome burn. It doesn’t have to be this way. Why It’s Becoming Trendy To Bash Central MLAs the emerging field of machine learning operations (MLOps) continues to grow rapidly and new tools and techniques proliferate, the potent
In software engineering, decreasing cycle time has a super-linear effect on progress. In modern deep learning, cycle time is often on the order of hours or days. The easiest way to speed up training, data parallelism, is to distribute copies of the model across GPUs and machines and have each copy compute the loss on a shard of the training data. The gradients from these losses can then be accumul
So, your company decided to invest in machine learning. You have a talented team of Data Scientists churning out models to solve important problems that were out of reach just a few years ago. All performance metrics are looking great, the demos cause jaws to drop and executives to ask how soon you can have a model in production. It should be pretty quick, you think. After all, you already solved
Apparently a lion, bear, and tiger are friendsPyTorch-lightning is a recently released library which is a Kera-like ML library for PyTorch. It leaves core training and validation logic to you and automates the rest. (BTW, by Keras I mean no boilerplate, not overly-simplified). As the core author of lightning, I’ve been asked a few times about the core differences between lightning and fast.ai, PyT
Both NVidia and Google recently released dev board targeted towards EdgeAI and also at a cost point to attract developers, makers and hobbyists. Both the dev boards are primarily for inference, but support limited transfer learning re-training. The Edge TPU supports transfer learning training using weight imprinting technique. Both of the dev kits consists of a SOM (System-on-Module) connected to
Hyperparameter optimization is one of the crucial steps in training Machine Learning models. With many parameters to optimize, long training time and multiple folds to limit information leak, it may be a cumbersome endeavor. There are a few methods of dealing with the issue: grid search, random search, and Bayesian methods. Optuna is an implementation of the latter one. Will Koehrsen wrote an exce
One of the biggest problems that we face when we tackle any machine learning problem is the problem of unbalanced training data.The problem of unbalanced data is such that the academia is split with respect to the definition, implication & possible solutions for the same.We will here try to unravel the mystery of unbalanced classes in the training data using an image classification problem. What i
When training deep neural networks, it is often useful to reduce learning rate as the training progresses. This can be done by using pre-defined learning rate schedules or adaptive learning rate methods. In this article, I train a convolutional neural network on CIFAR-10 using differing learning rate schedules and adaptive learning rate methods to compare their model performances. Learning Rate Sc
Edit: February 2019 Minor code changes. Improved experience of Jupyter notebook version of the article. IntroductionIn statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinit
After finishing the Deep Learning Foundation course at Udacity I had a big question — how did I deploy the trained model and make predictions for new data samples? Fortunately, TensorFlow was developed for production and it provides a solution for model deployment — TensorFlow Serving. Basically, there are three steps — export your model for serving, create a Docker container with your model and d
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