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.

Issue #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.

Issue #2019.08.12 – The Kubeflow Machine Learning Toolkit

12 August 2019

• Kubeflow — a machine learning toolkit for Kubernetes – An introduction to Kubeflow from the perspective of a data scientist. This article quickly runs through some key components – Notebooks, Model Training, Fairing, Hyperparameter Tuning (Katib), Pipelines, Experiments, and Model Serving. If you are looking for a quick overview, give this article a go. Here’s a key diagram from the article:

• Why is it So Hard to Integrate Machine Learning into Real Business Applications? – For teams just getting started, getting a trained model with sufficient accuracy is success. But that is just the starting point. There are many engineering and operational considerations that remain to be done. There are components that need to be built, tested and deployed. This post presents a real customer AI-based application, explaining some of the challenges, and suggests ways to simplify the development and deployment.
• Further afield – Techniques to improve the accuracy of your Predictive Models – a look at few techniques to improve the accuracy of your predictive models. The code base is in R, but the principles are applicable to a variety of code bases and algorithms.
• Use case spotlight – https://www.technologyreview.com/s/614043/instead-of-practicing-this-ai-mastered-chess-by-reading-about-it/. Instead of practicing, this AI mastered chess by reading about it. The chess algorithm, called SentiMATE, was developed by researchers at University College London. It evaluates the quality of chess moves by analysing the reaction of expert commentators. These learning techniques could have many other applications beyond chess – for instance, analysing sports, predicting financial activity, and making better recommendations.

Issue #2019.08.05 – Kubeflow 0.6 Release

5 August 2019

• Kubeflow v0.6: support for artifact tracking, data versioning & multi-user – version 0.6 includes several enterprise features to support multiple users and better model training pipelines. For multiple users, Kubeflow v0.6 provides a flexible architecture for user isolation and single sign-on. For data, enhancements have been added to Kubeflow Pipelines and jupyter. In total, over 250+ merged pull requests!
• Kubeflow Components and Pipelines – This is a useful article that demonstrates how to build pipelines, how to create and use components, and how to leverage them inside of notebooks. Specifically, how to run pipelines and experiments inside of a Notebook. The article also includes easy to understand, ready to use examples.
• Use Case Spotlight – The world’s most-advanced AI can’t tell what’s in these photos. Can you? A useful exploration into the the current limits of AI in visual recognition. Researchers from UC Berkeley, the University of Washington, and the University of Chicago are building the ultimate archive of photos that confuse AI. AI is judged by its answers .. we can get all sorts of unexpected bias from AI. This poses a major problem when AI systems are being used in technology like autonomous cars or fields like criminal justice.

Issue #2019.07.29 – Kubeflow Releases so far (0.5, 0.4, 0.3)

29 July 2019

• Kubeflow 0.5 simplifies model development with enhanced UI and Fairing library – The 2019 Q1 release of Kubeflow goes broader and deeper with release 0.5. Give your Jupyter notebooks a boost with the redesigned notebook app. Get nerdy with the new kfctl command line tool. Power to the people – use your favourite python IDE and send your model to a Kubeflow cluster using the Fairing python library. More training tools added as well, with an example of XGBoost and Fairing.
• Kubeflow 0.4 Release: Enhancements for Machine Learning productivity – The 2018 Q4 release will supercharge your productivity! Fairing, a python library to keep you in python code, hits alpha. Katib – the necessary hyperparameter tool for production ready models – adds support for TFjob. Jupyter notebook CRD – you can create a notebook from the command line. Kubeflow Pipelines! With pipelines, you can codify your workflows, removing boilerplate drudgery, adding hours back to your life every week.
• Kubeflow 0.3 Simplifies Setup & Improves ML Development – The 2018 Q3 release (see the pattern with releases?) sees the beginning of expanded options for training and serving. Kubeflow PyTorch and Chainer CRDs expand the model training options. Katib, hits alpha. Kubebench, a too for benchmarking, will assist you in assessing your hardware. Tensorflow Data Validation, part of TFX, added to Kubeflow Jupyter images. Model serving improvements through TF Serving and Nvidia’s TensorRT Inference Server
• Use Case Spotlight: AI in medicine – deriving intelligence from a sea of data – This article shows the power of what one person with passion can do to make an impact. Read this inspiring article about how an AI expert took on his toughest project ever: writing code to save his son’s life.
  

Issue #2019.07.22 – Kubeflow and Conferences, 2019

22 July 2019

• Kubeflow at OSCON 2019 – Over 10 sessions! Covering security, pipelines, productivity, ML ops and more. Some of the sessions are led by end-users, which means you’ll get the real deal about using Kubeflow in your production solution
• Kubeflow at KubeCon Europe 2019 in Barcelona – The top Kubeflow events from Kubecon in Barcelona, 2019. Tutorials, Pipelines, and Kubeflow 1.0 ruminations. The discussion on when Kubeflow will reach 1.0 should be of interest to those waiting for that milestone.
• Kubeflow Contributor Summit 2019 – Presentations and Slide decks, 22+ of them. Reviewing them will help you understand how the sausage is made. One of the interesting videos focuses on a panel discussion with machine learning practitioners and experts discussing the dynamics of machine learning at their workplace.
• Kubeflow events calendar – Find a past or future event. This is a great resource for reviewing content from community leaders and leveling up on the current state of Kubeflow. If you are aware of something that is missing, feel free to add the content through github – become a community member! 
• Use Case Spotlight: IBM’s photo-scraping scandal shows what a weird bubble AI researchers live in. This bubble is all about data – who owns it, who can monopolize it, who is monetizing it, and what the expectations around it. The expectations is the crux of the issue – people using the data may be at odds with the people supplying the data.