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Kubeflow news

Your weekly update of curated kubeflow news from across the web.

Weekend watch list

6 March 2020

CNCF introduction to Kubeflow

Arun Gupta, Senior Engineering Manager at Apple walks viewers through the benefits of Kubeflow and how to get started.

Securing Kubeflow

Talk by Barton Rhodes, Senior Machine Learning Engineer at DaVita Inc. This talk is motivated by Ian Coldwater during the Kubernetes Podcast ‘Attacking and Defending Kubernetes:

Containers are only as secure as their runtimes, and their orchestration frameworks, and their kernels, and their operating systems, and everything else”

Building and monitoring Kubeflow

Kirk Kaiser, APM at Datadog, talks about how to build and monitor Kubeflow machine learning Services with examples for an art project.

Kubeflow 1.0 is here!

2 March 2020

The wait is over, it’s official, Kubeflow 1.0 is here.

Many of you have been waiting for Kubeflow to reach 1.0 to suggest it to your managers, put it in production or use it more often in business critical applications. With Kubeflow reaching the 1.0 stage you can now do this with confidence and knowledge that Kubeflow is ‘here to stay’.

Read more from Thea Lamkin, Open Source Strategist for AI/ML at Google, talking about this release in her blog post.

Digest #2020.02.03 – AI-Created Medicine All Set for Human Trials

3 February 2020

-xkcd comics-

Artificial intelligence-created medicine to be used on humans for first time – For the first time, a drug created by AI and Machine Learning has been approved for human trials. The drug was created by British start-up Exscientia in collaboration with Japanese pharmaceutical firm Sumitomo Dainippon Pharma. While typical drug development takes five years to get to trials, this drug took 12 months! The molecule AKA DSP-1181 was created by using algorithms that sifted through potential compounds, checking them against a huge database of parameters. The drug will be used to treat patients who have obsessive-compulsive disorder (OCD).

DIY object tracking system with TensorFlow – Thinking of a great machine learning project on your spare Raspberry Pi(s) + some DIY hardware? Check out this interesting tutorial to build a real-time object tracker using TensorFlow. The article walks you through setting up the hardware and the TensorFlow libraries needed to get you started with machine learning; it’s gonna be a fun weekend project!

Hyperparameter tuning with Keras Tuner – If your ML projects have struggled with hyperparameter tuning you need to try Keras Tuner. This article is a great way to get started with Keras Tuner with a step-by-step walkthrough. Also, it’s an open-source project so check out their Github repo if you’d like to report issues, changes or contribute.

OpenAI goes all-in on PyTorch – Elon Musk’s AI research firm announced that it will be moving to PyTorch for its new machine learning projects. Many of the projects and engineers at OpenAI are transitioning to PyTorch as their main framework.  “Going forward we’ll primarily use PyTorch as our deep learning framework but sometimes use other ones when there’s a specific technical reason to do so.” OpenAI is also expected to open-source its PyTorch binding for blocksparse kernels in the coming months.

Digest #2020.01.27 – Which Algorithm Should I Use?

27 January 2020


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) and graph neural network (GNN) to tag road features and predict the types of roads, lanes and obstruction along the route.

Solving Problems with TensorFlow – A good article on solving practical problems with TensorFlow. The article walks through solving an optimization problem, solving a linear regression problem, and a “Hello World” of Deep Learning classification projects with the MNIST Dataset. Check out for an interesting “getting started with tensorFlow” and train a neural network with visual representations and some fun.

Which Machine Learning Algorithm Should I Use? – One of the most common questions in the machine learning space; people are often confused about their ML needs and do not know the right qualifying questions to ask. The Microsoft team provides a walkthrough on how to decide the answer to which machine algorithm to use. It usually comes down to what are the requirements for your scenario? And, what do you want to do with your data?  There are some neat cheat sheets for reference.

Data Sampling Methods for Imbalanced Classification – At some point we’ve all probably been thrown off by over-optimistic performance of machine learning solutions. The author walks through the challenges of machine learning with imbalanced classifications, a tutorial on sampling techniques to handle skewed class distributions, and explanations of undersampling, oversampling and a hybrid of both. A well-put tutorial for learning!

Digest #2020.01.20 – Machine Learning for Music, Video, Weather…

20 January 2020

– xkcd comics –

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 Katib, the native hyperparameter tuner in Kubeflow. While there are clear efficiency related benefits of using Kubernetes native Katib, for users that are using Azure Kubernetes, NNI now gives another option.

NNI Docs – How NNI works with Kubeflow and Kubernetes

TensorFlow for Video Analytics – With the ever-increasing importance of object detection the application of machine learning is attracting a lot of interest. The article discusses how TensorFlow is used to identify objects like humans, puppies, faces, etc. The article is a good getting started tutorial for people looking to get their hands dirty with machine learning. 

Using Machine Learning for Weather Prediction – Machine learning being prevalent is not going to miss out on something as important as the weather! Check out this AI research piece from Google on how to improve weather forecasts with real-time data using machine learning compared to existing techniques with ~3 hours of latency. The improvements can lead to significantly better forecasts on weather events that are time-sensitive. You may be able to take that picnic in the sun without rain showers.

Spotify Personalization with Machine Learning – Ever wondered how Spotify gets most of the song recommendation on your Spotify Home right? With machine learning, yes! Spotify engineering delivers and improves its recommendation system using a standardized ML framework including TensorFlow Extended, Kubeflow and the Google Cloud Platform Ecosystem. There is also a presentation by Tony Jebara on the topic that can be viewed here.

Digest #2020.01.13 –Machine Learning and AI 2020

13 January 2020

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 Cloud Build. Learn about MLOps, pipelines and taking your deployments to the cloud.

TensorFlow 2.1.0 is here – A new update to TensorFlow is out, welcome 2.1.0! The new version comes with new features and improvements, Pip packages become even more useful. There might be some breaking changes outlined in the release that could affect some users. And a whole lot of bug fixes. For a complete list, check out the Github Release docs. Oh, and also, goodbye Python 2! “TensorFlow 2.1 will be the last TF release supporting Python 2”

Is Deep Learning the same as Machine Learning? – Both Machine Learning and Deep Learning forms of AI have a lot of similarities and are often used interchangeably. However, they are not really the same and have a few subtle differences, deep learning performs much better at object recognition for example. The article dives deeper into the similarities, differences and applications of the two AI technologies.

AI Trends to watch for in 2020 – Artificial Intelligence was one of the top technologies of the last decade. AI set quite a few trends that we’ve witnessed and the streak is expected to continue in this decade. Forbes adds a list of trends to watch out for in 2020 in the field of AI. From AI helping on a more personal level to improved scientific discovery times and better entertainment check out the top 10 AI  trend predictions.

Digest #2019.12.16 – Artificial Intelligence of Things

23 December 2019

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, disoriented and partially torn pictures with up to 80% accuracy. The software has identified more than 3300 photos and won the Microsoft Cloud AI Research Challenge.

Understand AI and ML better – AI-related search terms are gaining a lot of popularity and ranking amongst the top are artificial intelligence, machine learning, deep learning and Natural Language Processing (NLP). These technologies, while similar are not exactly the same. Read more in the article to learn the basic differences between the technologies at an introductory level.

Artificial Intelligence Meets IoT – Two of the most widely discussed, researched and invested technologies of the decade are AI and IoT, but combining the two makes our devices, gadgets and digital life a whole lot powerful. Meet the Artificial Intelligence of (connected) Things. Examples of AIoT include the tech that enables Amazon’s Go stores, autonomous driving, fleet management, delivery drones, you know, all the cool things we’ve been waiting for!

Machine Learning Top Job for Developers – Data Science, AI and ML engineers, product managers and architects have dramatically taken over and conquered job postings across the globe. This momentum is shifting the tides of developer attitudes, gaining more traction in the field. New and experienced developers alike voted for AI, ML to be their field of interest. Most developers are trying to get their hands dirty with some form of development in the technologies. Even with all the new peaked interest, the number of open jobs hints that there’s a real shortage in the market. It’s a good time to be in this field!

Digest #2019.12.16 –The Decade of Artifical Intelligence

16 December 2019

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 fraud and security risks is on the rise. Investment banks that once called for traders and placed big bets on them are now pivoting to place their bets on people who have the math, technology, software, coding, data analytics and related skills to work along with electronic trading.

Artificial Intelligence Decade Review – 2010s was a big decade for AI, ML and robots. There were big breakthroughs, tech got cheaper, more convenient, at times more confusing. We made the machines smarter, more powerful, invited them in our homes, put them in control of minor everyday tasks, they also leaked some data at times and then we tried to control them (GDPR…). Read the full article for the decade in review for Artificial Intelligence and Machine Learning.

Reducing Maintainer Toil on Kubeflow – Maintaining Open-Source Software is hard! Kubeflow’s Github repo is seeing an exponentially increasing number of open issues and requests as it is gaining traction. To ease maintainability a bit, the team at Github and Kubeflow OSS introduced Project code-intelligence, the idea for which started out of demos for Kubeflow use-cases. If you’re interested in helping Kubeflow OSS “Help us create reports that pull and visualize key performance indicators (KPI), Github link. Ensemble repo specific and non-repo specific label predictions, Github link.

Everyday Applications of Machine Learning – Machine Learning and Artificial Intelligence have become a big part of most people’s everyday lives. From the searches on the internet to the targeted ads and your vacuum cleaner mapping out your room, AI/ML in technology is a prevailing part of our daily lives. Check out 12 applications of AI/ML that are part of some daily lives and business.

Digest #2019.12.02 – Machine Learning VS Minecraft

2 December 2019

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 be the last release you can work with. Check out the summary of changes here.

Machine Learning = Artificial Intelligence? – The article is an interesting read discussing whether machine learning = artificial intelligence. Machine Learning and Artificial Intelligence have a lot of overlap amongst them but they’re not exactly the same. MIT Professor Luis Perez-Breva argues that , “most of what is currently being branded as AI in the market and media is not AI at all, but rather just different versions of ML where the systems are being trained to do a specific, narrow task, using different approaches to ML, of which Deep Learning is currently the most popular.”

Machine Learning Books to Read– For all of us reading fans and bibliophiles interested in Machine Learning, check out this fancy list of machine learning books to read. We’ve personally read most of them and would highly recommend if you’re interested in exploring you ML chops a bit further.

Machine Learning takes on Minecraft – Minecraft vs Machine Learning, fun times! Machine Learning took on the popular game of Minecraft in an intensive gaming competition. The competition allowed coders to devise imitation learning strategies that will help machines win over human players. Imitation learning is the form of machine learning also found in autonomous driving. Check out the full article for an interesting read.

Robots Debating About AI – Check out robots making arguments for and against the dangers of Artificial Intelligence. The demonstration is by Project Debater, an IBM robot designed to hold arguments based on human input. The robots debated using arguments based on 1100 human submissions. The pro-AI side narrowly won the argument by 51.22% audience votes, fascinating!

Kubeflow Talks at Kubecon San Diego 1/2

25 November 2019

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 of are recordings of Kubeflow talks from last week in San Diego.

Building and Managing a Centralized Kubeflow Platform at Spotify – Keshi Dai & Ryan Clough, Spotify

Panel: Enterprise-grade, On-prem Kubeflow in the Financial Sector

Kubeflow: Multi-Tenant, Self-Serve, Accelerated Platform for Practitioners- Kam Kasravi & Kunming Qu

Building a Medical AI with Kubernetes and Kubeflow – Jeremie Vallee, Babylon Health

Supercharge Kubeflow Performance on GPU Clusters – Meenakshi Kaushik & Neelima Mukiri, Cisco

Kubeflow Talks at Kubecon San Diego 2/2

25 November 2019

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 Kubeflow – Derek Hao Hu & Yanjia Li

Tutorial: From Notebook to Kubeflow Pipelines – Jeremy Lewi, Michelle Casbon, Stefano Fioravanzo & Ilias Katsakioris

Advanced Model Inferencing Leveraging KNative, Istio & Kubeflow Serving – Animesh Singh & Clive Cox

Digest #2019.10.28 – AI for Biodiversity Research

28 October 2019

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 academic bar. With the growing importance and awareness of fairness,ethics, and transparency, the innovative uses of machine learning for biodiversity stands to be a challenging, exciting and rewarding task.

A Beginners’ Guide to Machine Learning– This article serves as an interesting machine learning 101. The article breaks down machine learning into three types; classification, regression and unsupervised learning with examples in Python using the library scikit-learn. While there is a lot more to delve into, this is a quick intro to the ML world, or a nice refresher if you have been away for a while.

Polynote by Netflix – Improve notebook execution, code quality in an IDE environment, meet Netflix Polynote! Open-source software that empowers data science and machine learning; super exciting experiments lie ahead. “Plenty of exciting work lies ahead, we are very optimistic about the potential of Polynote, and we hope to learn from the community just as much as we hope they will find value from Polynote.” Check it out on their Github.

AI and Health – The article sheds light on an interesting perspective with the use of AI to improve life spans. Imagine the possibility of knowing our risk profiles when we are born, the types of diseases we could be prone to and preventive measures taken years in advance. However, this will be no easy feat by any means, it will require social inclusiveness, strict governance of AI and data and diversity across the spectrum of devices, code, and human forms. Nonetheless, the idea itself and the nascent research is an exciting take on how AI can improve our lives and make the world a better place.