Blog posts tagged "Kubeflow News"

Using AI to Enrich Digital Maps – Your commute may now be saved from the road closures, private routes and diversions! Researchers at MIT and Qatar Computing Research Institute (QCRI) have invented a model that could improve GPS navigation on digital maps.An interesting read about the research combining convolutional neural network (CNN)

Microsoft NNI and Kubeflow – continued support and a way to tune hyperparameters –  Microsoft released version 1.3 of their NNI project. NNI is Microsoft’s Neural Network Intelligence project, it lets you search for the best neural network architecture and hyperparameters.  Microsoft NNI supports Kubeflow, where NNI can take the place of

Architecture for MLOps using TFX, Kubeflow Pipelines, and Cloud Build – If you’re a data scientist or an enthusiast and have been wanting to try the TFX (TensorFlow Extended), this article is a good place to start. The article also helps guide through setting up CI/CD and CT ( Continuous Training) using Kubeflow Pipelines and

AI to Identify Unknown Civil War Soldiers – An interesting piece by Time on the use of Artificial Intelligence for facial recognition; the software calculates the difference between proportions of the face and facial portraits like the eyes to match an uploaded picture with picture available on the web. This allows matching of discoloured

AI Competing with Wall Street –  Artificial Intelligence is replacing traders on Wall Street; what used to be a busy bustling market with chatter and cold calls are now the slight hums of machines running algorithms and executing trades. The demand for people with coding skills who can train models for intelligent trading and fight

Google pushing TPU with new TensorFlow release – TensorFlow 2.1 is here, welcome to the TPU push; that’s good news for many. We’ll also see improved performance on Linux and Windows and GPU support out-the-box. There are a few changes and issues to be aware of and if you’re using Python 2, TF 2.1 will

Throwback to Kubecon last week where Kubeflow was the most talked about topic other than Kubernetes itself. It was great to see so much excitement around Kubeflow. For those of you who missed the event, or were too busy to catch these talks, or just couldn’t be everywhere exciting at once, below is a list

KubeFlow’s Serverless Component: 10x Faster, a 1/10 of the Effort – Orit Nissan-Messing, Iguazio Enabling Kubeflow with Enterprise-Grade Auth for On-Prem – Yannis Zarkadas & Krishna Durai Measuring and Optimizing Kubeflow Clusters at Lyft – Konstantin Gizdarski & Richard Liu Towards Continuous Computer Vision Model Improvement with Kubefl

AI for Biodiversity Research – Google, in collaboration with Global Biodiversity Information Facility (GBIF), iNaturalist, and Visipedia is making a push to bring AI to biodiversity research. While ML is prevalent in biodiversity research, proper attribution and oversight is a hit or miss. Google is hoping to bridge the gap and raise the

The Ultimate Learning Machine – Babies… This article dives into how machines are trained and their motivation for learning compared to a human baby. Guess what… babies learn faster, require less data and have a power that machines don’t – curiosity! The AI lab at UC Berkeley is trying to innovate with AI to mimic

How NASA uses Machine Learning – If you think the Earth is the only planet with Machine Learning you were wrong. The Mars Rover learning its path and environment at Mars? The healthcare needs for future astronauts? Planet exploration and discovery? Robotic astronaut!? All of these are questions NASA is experimenting and researching with M

Understanding Fairness in Machine Learning – This article is a great reminder and defense for the statement, “the data speaks for itself.” Biases in training models affect the results of analyses. It is essential to understand how our models make decisions to tackle this bias by adding more balanced training data. Knowledge of biases in

MLOps, Rise of the Term: Most of us by now have heard this word frequenting around; MLOps, or Machine Learning Operations.. This is an interesting article on the rise of the term and the challenges actually faced by teams working cross-functionally and dealing with Machine Learning. It talks about the limitation of managed solutions for

Machine Learning and AI in 2019: A recent survey conducted by Dresner Advisory Services shows Machine Learning and AI to rank as highest priority for enterprises. R&D, Marketing, Sales, Insurance, Fintech, Telco, Retail and Healthcare enterprise rank machine learning as their biggest bet and believe it is critical to their success. “2019

How To Develop Successful Machine Learning Projects On A Budget – A quick journey through some of the principles for a successful AI getting started project. The article includes an example of how to go from nothing to something – from data pipeline creation to models in production. The primary focus is on a model

AI Tales: Building Machine learning pipeline using Kubeflow and Minio – Understand the Kubeflow value proposition in an entertaining format. The story starts with Joe, the neighbourhood Machine learning enthusiast. Joe reads a few things, becomes an expert, and then the real fun begins. He quickly runs into problems with portability, DevO