Artificial intelligence, data science, and big data in 2019: what really mattered


The techlash hasn’t died down – it’s simply change into normalized. Barely a day passes with no new scandal rising, from questionable surveillance to racist AI algorithms. Nevertheless it hasn’t all been unhealthy: whereas negatives get a variety of consideration (and so they need to – the implications of tech will be deadly, each societally and actually), there was nonetheless a lot to get enthusiastic about. And for these working within the information occupation – as analysts, scientists, and engineers, there have been a number of necessary tendencies that actually helped to outline the place we are actually from a purely sensible perspective – in addition to hinting at the place we would go sooner or later.

With just some weeks left to go of the 12 months (and the last decade!), let’s have a look at among the key issues that outlined this 12 months within the subject of information science and information engineering.

The expansion of PyTorch

TensorFlow is undoubtedly the preferred deep studying framework. You would possibly even say that its function in popularizing deep studying and synthetic intelligence has been understated. However whereas TensorFlow has held its place for a while, 2019 was the 12 months when issues began to vary. Look, for instance at this Google Traits graph (and sure, I do know it’s not in any method scientific):

PyTorch v. TensorFlow Google Trends

As you possibly can see TensorFlow hit its stride fairly early on. It’s solely within the final 12 months or in order that PyTorch has been narrowing the hole.

One of many causes for that is the truth that PyTorch 1.Zero was launched on the finish of final 12 months. This has been the muse that has spurred its progress over the past 12 months, successfully asserting its ‘official’ arrival on the scene. With Fb (PyTorch’s creator) constructing on this basis all year long with a number of small however necessary releases. PyTorch 1.3, for instance, which was launched on the PyTorch Developer Convention in October, included numerous ‘experimental’ new options, together with named tensors and PyTorch Cellular.

Another excuse for PyTorch’s progress this 12 months is that it’s discovering traction within the analysis subject. This text gives some laborious information that proves that PyTorch is beginning to develop on this space, citing the device’s comparable simplicity, API and efficiency as the explanations that it’s undermining TensorFlow’s utter dominance of the sector.

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TensorFlow 2.0

Whereas PyTorch has grown considerably in 2019, TensorFlow is however nonetheless holding its place on the prime of the deep studying rankings. And TensorFlow 2.Zero has undoubtedly cemented its place. With the alpha launch getting builders excited since March, the complete launch of two.Zero marked an necessary milestone for the venture.

The important thing distinction between TensorFlow 2.Zero and 1.Zero is in the end accessibility and ease of use. Regardless of its large reputation, TensorFlow 1.Zero all the time had a popularity for being just a little tougher to make use of than many different deep studying instruments. The crew had been clearly conscious of this and have finished lots to make life simpler for TensorFlow builders.

“With tight integration of Keras into TensorFlow, keen execution by default, and Pythonic perform execution,” the crew write within the launch notes, “TensorFlow 2.Zero makes the expertise of creating purposes as acquainted as doable for Python builders.”

When positioned alongside the thrilling improvement of PyTorch, it’s clear that these two instruments are going to be defining deep studying within the 12 months – or years – to return.

TensorFlow 2.0 Quick Start Guide cover image

Stand up so far with what’s new in TensorFlow 2.Zero with TensorFlow 2.Zero Fast Begin Information.

Coping with massive portions of information in real-time is now the cutting-edge of huge information. It’s for that reason that this 12 months we’ve began to see stream processing achieve headway within the mainstream. Though it’s been an necessary method for organizations with data-intensive wants, the usage of cloud and hybrid options – in addition to an general consciousness of the alternatives of real-time information – has change into really mainstream.

In flip, that is giving new prominence to a spread of stream-processing platforms. Kafka, Spark, and Flink are simply three of essentially the most well-known names on this area, however the market is undoubtedly rising.

One other key driver right here is Nvidia – as one of many main {hardware} corporations, it deserves a variety of credit score for serving to to make large processing energy accessible to organizations that wouldn’t have had an opportunity just some years in the past. With CUDA, Nvidia’s parallel programming paradigm for GPUs, the corporate helps all kinds of customers to leverage stream processing in numerous methods.

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Information evaluation on the cloud

Though I’ve already talked about how influential TensorFlow was in popularizing deep studying, right this moment public cloud goes even additional. It’s making synthetic intelligence and analytics accessible to new roles (considering right here about instruments like Azure Machine Studying Studio and Amazon SageMaker), in addition to making it simpler to construct and deploy machine studying fashions in purposes and merchandise.

In latest weeks, Microsoft has made one other step in its bid to eat into AWS’s market share with Azure Synapse. Primarily a subsequent era Azure SQL Warehouse, Synapse is designed to bridge the hole between information lake and information warehouse – so, providing large scale, and bettering analytical velocity.

Will probably be fascinating to see how this performs with the broader market. AWS would possibly reply with one thing comparable – however the onus stays on Microsoft to shift mindshare; AWS will wish to consolidate its highly effective place.


It could be fallacious to recommend that safety is a brand new challenge on the earth of information engineering and analytics. However in 2019 it’s change into virtually unimaginable to consider the 2 domains as separate from each other.

This cuts two other ways: on the one hand the emphasis on securing information and defending privateness has by no means been better. Then again, synthetic intelligence and machine studying have began to play a vital half in the best way that we monitor and determine threats to our methods.

To a sure extent this expresses the double bind that information poses: the quantity of information at our disposal is a nightmare from a governance and architectural perspective, however it’s, on the similar time, a method of mitigating that very nightmare.

All in all, then, a little bit of a vicious cycle, however however a reminder that nevertheless massive our information will get, and nevertheless a lot we attempt to automate, there’ll all the time be a necessity for people to suppose creatively and strategically about how we really go about fixing issues.

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For extra know-how eBooks and movies to arrange you for 2020, head to the Packt retailer.

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