In recent years, machine learning and artificial intelligence have become an integral part of many of our lives in use cases as diverse as finding cancer cells in an MRI and facial and object recognition during a professional basketball game. Over the course of just the four years between 2013 and 2017, machine learning patents alone grew 34%, while spending is estimated to grow to $57.6B by 2021 (https://www.forbes.com/sites/louiscolumbus/2018/02/18/roundup-of-machine-learning-forecasts-and-market-estimates-2018/#794d6f6c2225).
Despite its status as a growing technology, the term machine learning was coined back in 1959 by Arthur Samuel—so what caused the 60-year gap before its adoption? Perhaps the two most significant factors were the availability of technology able to process model predictions fast enough, and the amount of data being captured every minute digitally. According to DOMO Inc, a study in 2017 concluded that 2.5 quintillion bytes were generated daily and that at that time, 90% of the world's data was created between 2015 and 2017 (https://www.domo.com/learn/data-never-sleeps-5?aid=ogsm072517_1&sf100871281=1). By 2025, it is estimated that 463 exabytes of data are going to be created daily (https://www.visualcapitalist.com/how-much-data-is-generated-each-day/), much of which will come from cars, videos, pictures, IoT devices, emails, and even devices that have not made the transition to the smart movement yet.
The amount that data has grown in the last decade has led to questions about how a business or corporation can use such data for better sales forecasting, anticipating a customer's needs, or detecting malicious bytes in a file. Traditional statistical approaches could potentially require exponentially more staff to keep up with current demands, let alone scale with the data captured. Take, for instance, Google Maps. With Google's acquisition of Waze in 2013, users of Google Maps have been provided with extremely accurate routing suggestions based on the anonymized GPS data of its users. With this model, the more data points (in this case GPS data from smartphones), the better predictions Google can make for your travel. As we will discuss later in this chapter, quality datasets are a critical component of machine learning, especially in the case of Google Maps, where, without a proper dataset, the user experience would be subpar.
In addition, the speed of computer hardware, specifically specialized hardware tailored for machine learning, has also played a role. The use of Application-Specific Integrated Circuits (ASICs) has grown exponentially. One of the most popular ASICs on the market is the Google Tensor Processing Unit (TPU). Originally released in 2016, it has since gone through two iterations and provides cloud-based acceleration for machine learning tasks on Google Cloud Platform. Other cloud platforms, such as Amazon's AWS and Microsoft's Azure, also provide FPGAs.
Additionally, Graphics Processing Units (GPUs) from both AMD and NVIDIA are accelerating both cloud-based and local workloads, with ROCm Platform and CUDA-accelerated libraries respectively. In addition to accelerated workloads, typical professional GPUs offered by AMD and NVIDIA provide a much higher density of processors than the traditional CPU-only approach. For instance, the AMD Radeon Instinct MI60 provides 4,096 stream processors. While not a full-fledged x86 core, it is not a one-to-one comparison, and the peak performance of double-precision floating-point tasks is rated at 7.373 TFLOPs compared to the 2.3 TFLOPs in AMD's extremely powerful EPYC 7742 server CPU. From a cost and scalability perspective, utilizing GPUs in even a workstation configuration would provide an exponential reduction in training time if the algorithms were accelerated to take advantage of the more specialized cores offered by AMD and NVIDIA. Fortunately, ML.NET provides GPU acceleration with little additional effort.
From a software engineering career perspective, with this growth and demand far outpacing the supply, there has never been a better time to develop machine learning skills as a software engineer. Furthermore, software engineers also possess skills that traditional data scientists do not have – for instance, being able to automate tasks such as the model building process rather than relying on manual scripts. Another example of where a software engineer can provide more value is by adding both unit tests and efficacy tests as part of the full pipeline when training a model. In a large production application, having these automated tests is critical to avoid production issues.
Finally, in 2018, for the first time ever, data was considered more valuable than oil. As industries continue to adopt the use of data gathering and existing industries take advantage of the data they have, machine learning will be intertwined with the data. Machine learning to data is what refining plants are to oil.
