WEBINAR: Continuous Integration/Continuous Deployment for Machine Learning - 6th November 2019

Live Webinar: CI/CD for Machine Learning November 6th, 2019 @ 12pm EST CI/CD (Continuous Integration/Continuous Deployment) has long been a successful process for most software applications. The same can be done with Machine Learning applications, offering automated and continuous training as well as continuous deployment of machine learning models. Using CI/CD for machine learning applications creates a truly end-to-end pipeline that closes the feedback loop at every step of the way, and maintains high performing ML models. It can also bridge science and engineering tasks, causing less friction from data, to modeling, to production and back again. Join CEO of cnvrg.io Yochay Ettun as he takes you through how to create a CI/CD pipeline for machine learning, and set up continuous deployment in just one click. With a depth of knowledge in all the latest research, Yochay will share with you today's top methods for applying CI/CD to machine learning. Key webinar takeaways: Configure and execute continuous training and continuous deployment for ML Define dependencies and triggers Automatically connect data pipeline, machine learning pipeline and deployment pipelines Integrate model bias detection or fairness and accuracy validations Build monitoring infrastructure to close the data feedback loop Collect live data for improved model performances Unable to attend? Register for a recording of the webinar and copy of the presentation following the live event.

Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, IBM Watson by Olexander Kolisnykov via @topbots

The article will guide you through the best MLaaS platforms on the market and lists some infrastructural decisions to be made and some important considerations to keep in mind when choosing an MLaaS platform.

This has so much detail and is very very useful. This is worth a bookmark and if the platform allows applause of full cudos to the author Olexander.

3 Advanced Python Functions for Data Scientists by Dario Radečić via @TDataScience

Make your code cleaner and more readable by not reinventing the wheel.

Useful functions that are worth making note of and trying to use more in your python code.

Facebook Has Been Quietly Open Sourcing Some Amazing Deep Learning Capabilities for PyTorch by @jrdothoughts via @TDataScience

The new release of PyTorch includes some impressive open-source projects for deep learning researchers and developers.

Interesting new features that definitely call for some experimenting to see what they can really do.

The 3 Missing Roles that every Data Science Team needs to Hire by @kesaritweets via @TDataScience

In a mad rush to hire Data Scientists, most companies overlook three key roles and this often leads to failure of projects

The roles here will be missing for many organisations or strategies so I found it really interesting to read about them. Certainly, something to bear in mind and consider.

WEBINAR: Demystifying AI & ML: Making Your Data Talk - 31 October 2019

Data Science Central Webinar Series Event Demystifying AI & ML: Making Your Data Talk Join us for the latest DSC Webinar on October 31st, 2019 Learn the basics of AI and Machine Learning and understand how to improve your organization’s experience and data optimization with the power of Augmented Intelligence. Provided will be an overview of an AI strategy and you will learn about our roadmap in AI, Natural language and Machine learning. In this latest Data Science Central webinar we will review: Basics of AI and machine learning Importance of Augmented Intelligence vs Artificial Intelligence A unique approach to AI that allows you to make the most of your data and AI investments Featured Speaker: Vinay Kapoor, Director, Product Management -- Qlik Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Demystifying AI & ML: Making Your Data Talk Date:  Thursday, October 31st, 2019 Time:  9 AM - 10 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

Logs were our lifeblood. Now they're our liability by/via Normcore Tech

What happens when we collect too much data?

It's not like the old days - we need to mask certain data, even delete some data due to GDPR, but logs can be a great source of information too. I've worked on loading telephone call logs so you can see the number of phone calls a salesrep has made, how long they lasted, and who they were too. All useful data. Yes you need to archive them, make changes to them, but they can be great sources of information.

The New XLOOKUP Function for Excel + Video Tutorial by/via @excelcampus

Microsoft’s new XLOOKUP feature is getting a lot of attention from Excel users. Here is a great tutorial on how to use it.

I like the look of this function which I think over time could save time and be much easier.

We can’t trust AI systems built on deep learning alone by Karen Hao via @techreview

Gary Marcus believes that while deep learning has played an important role in advancing AI, it’s overhyped and the current overemphasis on it may lead to its demise. The article details how he thinks we could achieve general intelligence, why we should strive for it, and why it might make machines safer.

This was fascinating and a refreshing viewpoint on deep learning and AI which was more about the learning and less about the technical.

What a little more computing power can do for Deep Learning by Kim Martineau via @MIT

A deep learning model may need to see millions of photos before it can successfully identify a cat. The process is computationally intensive. But there may be a more efficient way - new MIT research shows that models only a fraction of the size are necessary.

An interesting viewpoint which could help to save money and time when developing this kind of model.

9 key rules for transitioning a data and analytics strategy to the cloud by Pandian Muneeswara via @Infomgmt

Following are the nine key rules to be considered in getting an on-premises data and analytics environment successfully transformed into the cloud.

I really liked rule 6 and the comment that you should incrementally grow the cloud roadmap which I think is the right way to go - start small and grow it over time.

WEBINAR: Building Accessible Dashboards in Tableau - 15 October 2019

Data Science Central Webinar Series Event

Building Accessible Dashboards in Tableau Join us for this latest DSC Webinar on October 15th, 2019 When you create a dashboard, you want to ensure that everyone can see and understand the data. This means creating dashboards with accessibility in mind for people with a variety of disabilities, including various vision impairments, learning disabilities, limited movement, or a combination of these. In this latest Data Science Central webinar you will learn how to create and publish dashboards that are accessible to a wide variety of users. You’ll also learn about the Web Content Accessibility Guidelines and how to apply them in Tableau, so that your dashboards meet Section 508 and EN 301 549 requirements. Speaker: Kyle Gupton, Director, Product Management -- Tableau Hosted by: Stephanie Glen, Editorial Director -- Data Science Central   Title:  Building Accessible Dashboards in Tableau Date:  Tuesday, October 15th, 2019 Time:  9:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

WEBINAR: Forecasting Using TensorFlow and FB's Prophet - 17 October 2019

Data Science Central Webinar Series Event

Forecasting Using TensorFlow and FB's Prophet Join us for this latest DSC Webinar on October 17th, 2019 We live in a time where we are able to monitor everything--servers, containers, fitness levels, power consumption, etc. Making predictions on time series data is often just as important as monitoring is. In this presentation, we will learn about how InfluxDB can be used with TensorFlow and FB's Prophet to make predictions and solve data engineering problems. Speaker: Anais Dotis-Georgiou, Developer Advocate -- InfluxData Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Forecasting Using TensorFlow and FB's Prophet Date:  Thursday, October 17th, 2019 Time:  9:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

The Seven Patterns Of AI by Kathleen Walch via @forbes

AI use cases tend to fall into one or more of these seven common categories. Kathleen Walch explains in this article from Forbes.

This is a great list and I think could be used in order to work out what COULD be done and use it to plan a roadmap for the future.

DeepMind Has Quietly Open Sourced Three New Impressive Reinforcement Learning Frameworks by @jrdothoughts via @Medium

DeepMind has been releasing a series of open source technologies to help streamline the adoption of deep reinforcement learning (DRL) methods including the recent release of the OpenSpiel, SpriteWorld, and bsuite DRL stacks.

This is great news for anyone who wants or needs to try DRL  Definitely something to look at.

WEBINAR: Master Data Management at Scale - 29 October 2019

Data Science Central Webinar Series Event Master Data Management at Scale Join us for the latest DSC Webinar on October 29th, 2019 Master data management (MDM) software turned 15 years old this year. Originally launched in 2004 by SAP, master data management systems aimed to help resolve the data unification problem by creating a central source of standardized references to customers, products, employees, suppliers, physical assets and other data across their many IT systems. MDM is valuable, but it’s also slow, labor-intensive, and costly. As the scale of MDM projects increases to millions of entities and hundreds or thousands of data sources, the traditional methods often fail. Mike Stonebraker will share his view on how MDM technology and MDM organizations must change to fulfill the promise of MDM at scale. In this latest Data Science Central webinar, we will review: Why large enterprises need data management solutions that solve data mastering challenges at scale Why traditional, rule-based, data mastering options are struggling to keep up How Machine Learning can be used to address large-scale data mastering challenges Featured Speaker: Mike Stonebraker, CTO & Co-Founder -- Tamr, Inc. Hosted by: Stephanie Glen, Editorial Director -- Data Science Central Title:  Master Data Management at Scale Date:  Tuesday, October 29th, 2019 Time:  9 AM - 10 AM PDT Space is limited so please register early: Reserve your Webinar seat now

The good, the bad, and the ugly of becoming data driven by Cindi Howson via @infomgmt

It seems for all the investments that organizations make in gathering and generating data, few are actually able to turn that data into actionable insights.

Can they connect all of these different datasets, in many different formats, and can they then make sense of it - this is not simple nor is it quick.

As FTC cracks down, data ethics is now a strategic business weapon by Daniel Wu via @TechCrunch

Five billion dollars may not be much for a behemoth like Facebook, but it’s still the largest amount the Federal Trade Commission has ever levied for violating data privacy. With the FTC cracking down, many companies are taking a new look at their privacy protections.

This is a well thought out article that makes some very good points. I certainly think very hard about sharing any of my own personal data and don't trust Facebook as far as I could throw them (nowhere).