Now, you can fully streamline your AI workflow by labeling only the data that provide valuable information and by using an arbitrarily large hardware infrastructure to execute it – all of this comes in an easy-to-use environment powered by Samsung SDSA’s autoLabel and RedBrickAI.
This article shows how the distillation process can be scaled up using a distributed training scheme and other training techniques.
This article contains a Cluster Management video tutorial of the AI Accelerator and describes how to use it.
This article contains a video tutorial of how to install the AI Accelerator on AWS.
This article contains a quick video tutorial of the AI Accelerator and describes how to use it.
This article contains a video tutorial of the TensorFlow version of the AI Accelerator and describes how to use it.
This article contains a video tutorial of the PyTorch version of the AI Accelerator and describes how to use it.
This article contains a video tutorial of the Keras version of the AI Accelerator and describes how to use it.
Brightics AI Accelerator automated, distributed machine learning (AutoML) speeds network training time up and over the critical point when automated model selection, feature generation, and hyper-parameter tuning become possible. Brightics AI Accelerator does all of this for you - yielding a much better model.
Data science aims to take data from some domain and produce a high-level description or model of it that can be applied practically to solve some particular challenge in that domain. How much knowledge about the domain does the data scientist have to have to do a good job? We explore this question in this article.
This article discusses various stages of autonomous driving and explores Computer Vision aspects of it in detail. Semantic segmentation is the partition of an image into coherent parts. Instance segmentation is Semantic Segmentation with the addition of identification of each unique entity in the image.
As artificial intelligence impacts people's lives more and more, it is indispensable that we watch out for ethical issues. The new field of AI Ethics deals with bias in datasets and models as well as misdirected use cases. This article discusses some topics at a high level. I'm interested in your opinions and any other cases where you've seen ethical challenges in datascience, machinelearning, and AI.
Artificial Intelligence is a collection of disparate models that perform extremely well in a very narrow domain. What is the future of AI and ML? It is not just collecting more data or tweaking an algorithm. We must reimagine a wholistic use case on the one hand, and upgrade the model architecture with reasoning. Please read my latest article and let me know what you think.
Studies conducted through collaboration between an operator that knows the physical reality and a data-science company that knows the best machine-learning methods yield good practical results. This article analyzes the various categories of Oil & Gas projects and papers and provides a recommendation for collaboration of data science and domain expertise for best results.
Distributed machine learning is a fascinating thing, reducing the training time by the number of computers that work on the job. Using 512 computers, for example, will reduce training time from 3 weeks to 1 hour. This model training speed up enables automated model selection, feature generation, and hyperparameter tuning. Imagine training an AI model in the time it takes to go to lunch!
Samsung SDS, a global leader in digital transformation and innovation solutions, today announced the launch of Brightics AI Accelerator…