Python is a prevalent programming language in machine learning (ML) community. A lot of Python engineers and data scientists feel the lack of engineering practices like versioning large datasets and ML models, and the lack of reproducibility. This lack is particularly acute for engineers who just moved to ML space. We will discuss the current practices of organizing ML projects using traditional open-source toolset like Git and Git-LFS as well as this toolset limitation. Thereby motivation for developing new ML specific version control systems will be explained.
It seems like using these pre-trained models have become a new standard for industry best practices. After all, why wouldn't you take advantage of a model that's been trained on more data and compute than you could ever muster by yourself? Advances within the NLP space have also encouraged the use of pre-trained language models like GPT and GPT-2, AllenNLP's ELMo, Google's BERT, and Sebastian Ruder and Jeremy Howard's ULMFiT (for an excellent over of these models, see this TOPBOTs post). One common technique for leveraging pretrained models is feature extraction, where you're retrieving intermediate representations produced by the pretrained model and using those representations as inputs for a new model. These final fully-connected layers are generally assumed to capture information that is relevant for solving a new task.
Dr. Zaius: I think you're crazy. The concept of a second opinion in medicine is so common that most people take it for granted, especially given a severe diagnosis. Disagreement between two doctors may be due to different levels of expertise, different levels of access to patient information or simply human error. Like all humans, even the world's best doctors make mistakes. At Butterfly, we're building machine learning tools that will act as a second pair of eyes for a doctor and even automate part of their workflow that is laborious or error prone.
The model, Global Automated Target Recognition (GATR), runs in the cloud, using Maxar Technologies' Geospatial Big Data platform (GBDX) to access Maxar's 100 petabyte satellite imagery library and millions of curated data labels across dozens of categories that expedite the training of deep learning algorithms. Fast GPUs enable GATR to scan a large area very quickly, while deep learning methods automate object recognition and reduce the need for extensive algorithm training. The tool teaches itself what the identifying characteristics of an object area or target, for example, learning how to distinguish between a cargo plane and a military transport jet. The system then scales quickly to scan large areas, such as entire countries. GATR uses common deep learning techniques found in the commercial sector and can identify airplanes, ships,, buildings, seaports, etc. "There's more commercial satellite data than ever available today, and up until now, identifying objects has been a largely manual process," says Maria Demaree, vice president and general manager of Lockheed Martin Space Mission Solutions.
Machine Learning model validation helps to make the model prediction accurate. Get the unbiased ML model validation services with Cogito and authenticate the output of your machine learning model at best level. As per the industry and perception model types, there are various types of AI model perception models that can be validated to improve the performance of model into the real world making it reliable among the end-users.
See the ModelDB frontend in action: ModelDB is an end-to-end system to manage machine learning models. It ingests models and associated metadata as models are being trained, stores model data in a structured format, and surfaces it through a web-frontend for rich querying. ModelDB can be used with any ML environment via the ModelDB Light API. ModelDB native clients can be used for advanced support in and . The ModelDB frontend provides rich summaries and graphs showing model data.
Once your machine learning model is predicting accurate results, you can expose it to the outside world. This article presents the easy-to-follow steps which we can follow to host a machine learning model to the outside world. Public can then access your work via their web browsers. It is an important step for anyone who wants to make a business out of their machine learning models. Think of Flask as a collection of software packages which can help you easily create a web application.
Training deep learning models on mobile devices recently becomes possible, because of increasing computation power on mobile hardware and the advantages of enabling high user experiences. Most of the existing work on machine learning at mobile devices is focused on the inference of deep learning models (particularly convolutional neural network and recurrent neural network), but not training. The performance characterization of training deep learning models on mobile devices is largely unexplored, although understanding the performance characterization is critical for designing and implementing deep learning models on mobile devices. In this paper, we perform a variety of experiments on a representative mobile device (the NVIDIA TX2) to study the performance of training deep learning models. We introduce a benchmark suite and tools to study performance of training deep learning models on mobile devices, from the perspectives of memory consumption, hardware utilization, and power consumption. The tools can correlate performance results with fine-grained operations in deep learning models, providing capabilities to capture performance variance and problems at a fine granularity. We reveal interesting performance problems and opportunities, including under-utilization of heterogeneous hardware, large energy consumption of the memory, and high predictability of workload characterization. Based on the performance analysis, we suggest interesting research directions.
Data science is one of the hottest topics in the 21st century because we are generating data at a rate which is much higher than what we can actually process. A lot of business and tech firms are now leveraging key benefits by harnessing the benefits of data science. Due to this, data science right now is really booming. In this blog, we will deep dive into the world of machine learning. We will walk you through machine learning basics and have a look at the process of building an ML model.
Soil moisture is an important variable that determines floods, vegetation health, agriculture productivity, and land surface feedbacks to the atmosphere, etc. Accurately modeling soil moisture has important implications in both weather and climate models. The recently available satellite-based observations give us a unique opportunity to build data-driven models to predict soil moisture instead of using land surface models, but previously there was no uncertainty estimate. We tested Monte Carlo dropout (MCD) with an aleatoric term for our long short-term memory models for this problem, and asked if the uncertainty terms behave as they were argued to. We show that the method successfully captures the predictive error after tuning a hyperparameter on a representative training dataset. We show the MCD uncertainty estimate, as previously argued, does detect dissimilarity.