Amazon Wants to Make You an ML Practitioner— For Free by @anthonyagnone via @TDataScience

 The tech giant plans to speed up ML proficiency by publicizing its long-internal material.

This sounds like the kind of gift you can't refuse to take advantage of - therefore my opinion is to go there and take a look - at the least you will get an insight into how they do things, at the most, you can get some free training.

How Big Data Helps Drive Amazon Sales by/via @datafloq

The reliability of businesses on big data is becoming increasingly high every day. Businesses now fully rely on data, from the time it is generated, up to the moment it delivers valuable insight to online users. Hence, collecting, storing, processing, and analyzing data within a short period of time, has become necessary in order for a business to stay ahead of the competition.  Amazon, as the leading eCommerce platform, has achieved all its success by putting in hard work to remain at the top of the charts. It makes use of big data analysis to persuade customers to make more shopping choices that are pleasing. This stimulates more purchases from them, and thus more profits, but how do they use big data?

Interesting article that made me stop and think about the possibilities in other companies.

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.

WEBINAR: Managing the Machine Learning Lifecycle What's New with MLflow 6 June 2019

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Managing the Machine Learning LifecycleWhat's New with MLflowThursday, June 6, 2019 | 10 am PST Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To solve for these challenges, last June, we unveiled MLflow, an open source platform to manage the complete machine learning lifecycle. Most recently at Spark + AI Summit in San Francisco, we announced the General Availability of Managed MLflow and the upcoming release of MLflow 1.0. In this webinar, we will review new and existing MLflow capabilities that allow you to: Keep track of experiments runs and results across frameworks. Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs. Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker Featured Speakers Clemens Mewald, Director of Product Management at Databricks Hosted by: Cyrielle Simeone, Product Marketing Manager, Databricks SAVE MY SPOT

Amazon's gender-biased algorithm is not alone by Cathy O'Neil via @infomgmt

Internet giant Amazon recently ran into a problem that eloquently illustrates the pitfalls of big data: It tried to automate hiring with a machine learning algorithm, but upon testing it realised that it merely perpetuated the tech industry’s bias against women

I agree with Cathy here - Amazon should be congratulated for a) testing it properly and b) doing something about it when it was clear there was a problem. It cannot be acceptable to just use the excuse (for that is what it actually is) that you didn't know so cannot be liable. It really makes me mad when we all know that bias is a risk and we should all do the due diligence to test properly to make sure that we ensure it is no longer there. Please recognise bias as a risk and test carefully for it by using someone who is not on your team so they have fresh eyes.