Digest #2019.09.09 – Machine Learning on a Budget

9 September 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 for hiring and tools you could use, like Kubeflow. How much money will it take to get something off the ground? Read on ..

• New homepage and improved collaboration features for AI Hub – Google’s AI Hub is powered by Kubeflow. This announcement discusses new features added to AI Hub. Although still in beta, AI Hub provides a view into how Kubeflow can be leveraged on prem. Do you have platform engineers building your corporate AI toolkit? Read this article to see what Google is doing. Advanced multi-user use cases, new machine learning taxonomy, asset favoriting, and public data sets or solutions – including a TensorRT-optimized BERT notebook from Nvidia.
• Three pitfalls to avoid in machine learning – As scientists from myriad fields rush to perform algorithmic analyses, Google’s Patrick Riley calls for clear standards in research and reporting. Machine-learning tools can turn up fool’s gold — false positives, blind alleys and mistakes. Read this article to learn about three problems in machine-learning analyses that Google faced, and solved, in the Google Accelerated Science team.
• Use case spotlight: Little Ripper deploys croc-spotting AI drones – A different take on how AI is saving lives. To help keep beachgoers safe from crocodiles 🐊 in the water and on land, the same AI drone technology that the Little Ripper Group used for its shark 🦈 detection drones is now being used to spot crocodiles in Queensland. In additino to spotting wildlife, this technology is used to help swimmers. Last summer, 51 drones were deployed around Australia to help spot rips and swimmers in distress.

Digest #2019.09.02 – The why of Kubeflow

2 September 2019

• 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, DevOps, scaling, performance, and cost. Enter Kubeman (or Kubeperson?), who personifies Kubeflow, and saves the day!

• Basics of Data Science Product Management: The ML Workflow – Another look at the complicated space that Kubeflow helps solve. “Something I quickly learned was that managing ML products is difficult because of the complexities and uncertainties involved with the different steps in the machine learning workflow” – (1) Review of related literature; (2) Data gathering & processing; (3) Model training, experimentation, & evaluation; (4) Deployment
• Hardware Science: Researchers demonstrate all-optical neural network for deep learning – In a key step toward making large-scale optical neural networks practical, researchers have demonstrated a first-of-its-kind multilayer all-optical artificial neural network. The next generation of artificial intelligence hardware will be much faster and exhibit lower power consumption compared to today’s computer-based artificial intelligence.
• Hardware Science: Quantum computing should supercharge this machine-learning technique – Researchers from IBM and MIT show how an IBM quantum computer can accelerate a specific type of machine-learning task called feature matching. Feature matching is a technique that converts data into a mathematical representation that lends itself to machine-learning analysis. Using a quantum computer, it should be possible to perform this on a scale that was hitherto impossible.

Digest #2019.08.26 – Kubeflow basics, TensorFlow 2.0

26 August 2019

• Kubeflow for Poets – This article introduces the core concepts necessary to understand all of the moving pieces in a Kubeflow based machine learning Pipeline. It includes a brief introduction to microservices, Docker, Kubeflow, Kubernetes, virtualisation, Google cloud and more. Read this article for step-by-step low level interaction with every component in the toolchain, from dockerfiles to GKE.

• TensorFlow 2.0 RC Available – Take advantage of the next wave of innovation for TensorFlow. The 2.0 release is a major milestone and brings a focus on ease of use and simplification. It comes with many features to support this focus – eager execution, tighter Keras integration, loadable TF-Hub models, end-to-end ML pipelines with TFX, and much more.
• How AI Is Changing The Game For Recruiting – In this use case spotlight, we review how machine learning toolkits like Kubeflow and AI are changing the recruiting industry. Talent acquisition is expensive, and getting it wrong is more expensive. AI can helping improve talent acquisition efficiency and effectiveness. From finding the right candidates to creating and delivering the right message to those candidates, read this article to discover more.

How AI is changing Recruiting

20 August 2019

How AI Is Changing The Game For Recruiting – In this use case spotlight, we review how machine learning toolkits like Kubeflow and AI are changing the recruiting industry. Talent acquisition is expensive, and getting it wrong is more expensive. AI can helping improve talent acquisition efficiency and effectiveness. From finding the right candidates to creating and delivering the right message to those candidates, read this article to discover more.

Digest #2019.08.19 – Kubeflow at CERN

19 August 2019

• Replicating Particle Collisions at CERN with Kubeflow – this post is interesting for a number of reasons. First, it shows how Kubeflow delivers on the promise of portability and why that matters to CERN. Second, it reiterates that using Kubeflow adds negligible performance overhead as compared to other methods for training. Finally, the post shows another example of how images and deep learning can replace more computationally expensive methods for modelling real-word behaviour. This is the future, today.
• AI vs. Machine Learning: The Devil Is in the Details – Need a refresh on what the difference is between artificial intelligence, machine learning and deep learning? Canonical has done a webinar on this very topic, but sometimes a different set of words are useful, so read this article for a refresh. You’ll also learn about a different set of use cases for how AI is changing the world – from Netflix to Amazon to video surveillance and traffic analysis and predictions.

• Making Deep Learning User-Friendly, Possible? – The world has changed a lot in the 18 months since this article was published. One of the key takeaways from this article is a list of features to compare several standalone deep learning tools. The exciting news? The output of these tools can be used with Kubeflow to accelerate Model Training. There are several broader questions as well – How can companies leverage the advancements being made within the AI community? Are better tools the right answer? Finding a partner may be the right answer.
• Interview spotlight: One of the fathers of AI is worried about its future – Yoshua Bengio is famous for championing deep learning, one of the most powerful technologies in AI. Read this transcript to understand some of his concerns with the direction of AI, as well as the exciting developments in AI. Research that is extending deep learning into things like reasoning, learning causality, and exploring the world in order to learn and acquire information.