Presto-powered S3 data warehouse on Kubernetes by @joshua_robinson via @Medium

Joshua Robinson offers up a tutorial on how to set up a Presto data warehouse using Docker that could query data on a FlashBlade S3 object store, and a follow-up tutorial that explains how to move everything, including the Hive Metastore, to run in Kubernetes.

This is very useful to read and might help you to achieve something quicker than you have planned.

WEBINAR: Developing and Testing Shiny Apps - 12 March 2020

Data Science Central Webinar Series Event

Developing and Testing Shiny Apps Join us for the latest DSC Webinar on March 12th, 2020 Shiny is the most popular framework among R users for developing dashboards and web applications. It is commonly used by statisticians and data scientists to present and share their work with broader groups. These dashboards are often developed inside the RStudio IDE and then published to hosting servers. RStudio IDE users have been enjoying the power of Databricks clusters and other workspace features since 2018. Now they can use Shiny on Databricks as well. In this latest Data Science Central webinar, we will review how RStudio Server works on Databricks clusters and the advantages of running RStudio Server inside the Unified Data Analytics Platform. We will introduce a new addition to the Unified Platform for R users on Databricks: support for Shiny applications. This webinar will include a demo that will focus on the lifecycle of developing and testing Shiny applications inside hosted RStudio Server, as well as what can be done with a high-bandwidth connection to a powerful Apache Spark cluster. Speaker: Hossein Falaki, Tech Lead -- Databricks Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Developing and Testing Shiny Apps Date:  Thursday, March 12th, 2020 Time:  09:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

Google has released a tool to spot faked and doctored images by Karen Hao via @techreview

Google has released a tool to spot faked and doctored images.

Very much needed - I really hope it works and is adopted widely.

Deep learning isn’t hard anymore by Caleb Kaiser via @TDataScience

Deep learning used to require large amounts of data, deep pockets, and a novel, usually custom-built, architecture. But with transfer learning (which takes a pre-trained model and retrains the last layers of the model to focus on a new task), a single engineer can deploy a model in a new domain in a matter of days

There is a great link in the article to a primer on Transfer Learning which is well worth the time investment in reading and learning so you can take advantage of that technique.

All Machine Learning Models Explained in 6 Minutes by Terence Shin in @TDataScience

Intuitive explanations of the most popular machine learning models.

This deserves applause, a bookmark and sharing.

WEBINAR: Forecasting: Prophet & Time Series Database - 25 February 2020

Data Science Central Webinar Series Event

Forecasting: Prophet & Time Series Database Join us for this latest DSC Webinar on February 25th, 2020 Data collection is only half of the battle. The other half is being able to easily perform data analysis. FB Prophet aims to make time series forecasting simple and fast. In this latest Data Science Central webinar, we’ll learn how to make a univariate time series prediction with Prophet and a time series database. Speaker: Anais Dotis-Georgiou, Developer Advocate -- InfluxData Hosted by: Stephanie Glen, Editorial Director -- Data Science Central   Title:  Forecasting: Prophet & Time Series Database Date:  Tuesday, February 25th, 2020 Time:  9:00 AM - 10:00 AM PST   Space is limited so please register early: Reserve your Webinar seat now

Interested in machine learning? Better learn PyTorch by Matt Asay via @infoworld

Don’t look now, but easy, straightforward PyTorch has become the hottest product in data science.

As it rivals Tensorflow I would probably suggest you get a grounding in both (if you can).

How AI is battling the coronavirus outbreak by Rebecca Heilweil via @voxdotcom

AI helped spot an early warning about the outbreak and now researchers are using flight data to predict where the coronavirus could pop up next.

AI can be hugely helpful in anything connecting with medicine as it can work out patterns and spot changes that standard methods would either miss or take so long to find that the information was out of date.

Demand for big data-as-a-service growing at 25% annually by Bob Violino via @infomgmt

With big data-as-a-service, tools such as analytics software and storage are delivered via the cloud by a service provider.

Definitely, a cheaper way to have big data.

Blockchain in 5 industries by Alison McCauley via @oreillymedia

Blockchains go beyond finance. Here are five examples.

This is part of a series about Blockchain which can be found here. I recommend you read all three articles as it will give you a great understanding of Blockchain.

WEBINAR: How a Physics-Driven Analytics Platform Detects Reliability Threats - 26 February 2020

Data Science Central Webinar Series Event How a Physics-Driven Analytics Platform Detects Reliability Threats Join us for this latest DSC Webinar on February 26th, 2020 A physics-driven analytics platform aids in improvements to the reliability and efficiency of connected mechanical systems. The solution analyzes large quantities of time series data from IoT sensors to help identify issues affecting system performance in real-time as well as provide accurate data for predictive maintenance. Our presenter chose a time series database for its high ingest and storage of time series data as well as its ability to easily send this data into their systems for predictive analytics. During this latest Data Science Central webinar in association with IoT Central, learn how using a purpose-built time series database helps to continuously optimize reliability of their customers’ connected mechanical systems. Speaker: Jon Herlocker, President and CEO -- Tignis Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  How a Physics-Driven Analytics Platform Detects Reliability Threats Date:  Wednesday, February 26th, 2020 Time:  9:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

AI Can Do Great Things—if It Doesn't Burn the Planet by/via @wired

OpenAI created an algorithm that successfully manipulates the pieces of a Rubik’s Cube using a robotic hand. But this accomplishment cost more than research time and effort—one estimate says it may have consumed about 2.8 gigawatt-hours of electricity, roughly equal to the output of three nuclear power plants for an hour. The computing power required for AI breakthroughs increased 300,000-fold from 2012 to 2018, creating an environmental impact that needs to be considered.

Something that we often don't consider but really should if we are serious about saving the planet.

Google just published 25 million free datasets by @tjwaterman99 via @TDataScience

What you need to know about the largest data repository in the world.

Useful for practice if nothing else.

WEBINAR: Organize a Winning AI Team from Design to Deployment - 19 February 2020

Data Science Central Webinar Series Event

Organize a Winning AI Team from Design to Deployment Join us for this latest DSC Webinar on February 19th, 2020 Designing, testing, and shipping an AI product is no small feat. How you organize your team — from roles and responsibilities to planning and process — can go a long way to ensuring AI success. In this latest Data Science Central webinar, we’ll talk about building a team that can deliver AI products to market using ML technology to deliver business value and impact the bottom line, be that customer delight, efficiency gains, or revenue growth. We’ll look at: How to design a winning team with skills and backgrounds that complement each other Defining the value derived from AI and building a roadmap to get there Using ML technology to deliver business value and make an impact Building a business case, designing a usable experience, and iterating a final product through customer insights Speaker: Alyssa Simpson Rochwerger, VP, AI Data Evangelism -- Figure Eight Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Organize a Winning AI Team from Design to Deployment Date:  Wednesday, February 19th, 2020 Time:  9:00 AM - 10:00 AM PST   Space is limited so please register early: Reserve your Webinar seat now

Scale the value of analytics by Rita Sallam and Carlie Idoine.via @Gartner_inc

Manually identifying patterns in the quantities of data most organizations have is not just tedious; it’s inefficient. It’s also much too easy to find only what you were looking for—missing valuable, but unanticipated, insights. Augmented analytics uses ML and AI techniques to identify actionable insights.

Brief if you are not a subscriber but interesting to read.

5 Ways Julia Is Better Than Python by Emmett Boudreau via @TDataScience

Why Julia is better than Python for DS/ML

A nice short piece that should make you consider using Julia - even if it is not in your plans please at least install it and have a play.