The first thing I noticed, and perhaps the most exciting, was how much of a focus there was on machine learning. From Werner Vogel’s keynote speech (for those of you who have not already watched this, I would highly recommend doing so), the type of ML services and features that were released, and the large number of ML talks that I attended, it is quite clear what AWS is attempting to do in this space. They are attempting to make machine learning an accessible part of any developer’s toolkit, rather than just something that hard-core data scientists can use and benefit from. This is a hugely ambitious goal, and it remains to be seen if they will be successful, but if they are successful, then this could have an enormous benefit for businesses as well as society in general. By abstracting the complexity of the science behind machine learning, and unlocking that capability for common developers, we might finally be able to make use of the enormous quantities of data that we are currently gathering but largely ignoring.
It is also clear that AWS sees voice as the next major milestone on the road towards fully naturalising our interactions with machines, and making technology truly accessible for everybody. Again, Vogel mentions this in his keynote speech, and there were several releases that support this vision. Amazon Transcribe, Amazon Translate, and Amazon Comprehend were announced, which round out their voice offering by providing the capability to convert speech to text, translate that text to another language, and draw insights from that text. Amazon FreeRTOS, and AWS IoT Device Defender were announced, which will make it easier for manufacturers to build voice enabled IoT edge devices, and for organisations to securely manage those devices once they are in use. I think it will take a while for the voice ecosystem to develop and for voice interactions to become widely adopted, but it is easy to see that AWS views this as an important next step, and isn’t afraid of developing new services to help push the pace of innovation in this area.
Security was a theme that featured quite heavily in general at re:Invent this year, and as a company that prides itself on having a security first approach, it is validating to see that we may finally be entering an era where both developers and businesses see security as a benefit, rather than an inconvenience. Another unexpected topic that came up this year, was the relatively little known discipline of chaos engineering, a practise that involves actively breaking your system in certain ways and monitoring how it reacts. In other words, rather than just testing until it works, you are testing when it doesn’t work, to see how fault tolerant it is and in what ways. This is something that I think should be utilised far more often, especially for highly business critical applications and infrastructures.
Finally, I’d like to give an honourable mention to Lige Hensley, the CTO of Ivy Tech Community College of Indiana, for his amazing talk on how they are using big data analytics and machine learning to benefit their students. For me personally, this was the most fascinating session that I attended throughout the entire conference, and if you have an interest in ML or big data analytics, then I would highly recommend watching it on YouTube.
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