Data, Automation, Machine Learning: Does AutoML Integrate them all? – Analytics Insight

  • Lauren
  • February 11, 2021
  • Comments Off on Data, Automation, Machine Learning: Does AutoML Integrate them all? – Analytics Insight

Organizations often struggle with building machine learning (ML) models as it is difficult and time consuming, and only few engineers and scientists have the necessary experience. Moreover, machine learning isn’t possible without data, and sometimes tools for working with data aren’t adequate. While, one of the key advantages of ML approaches is its learning cycle, it still needs to react dynamically to changing variables. Though automation may sound like a safe option, it is not the solution.
Automation in machine learning is based on the core principle of instrumentation i.e. it works with the data it possesses already but doesn’t produce any new insights. And automation refers to machines replicating human tasks, unlike machine learning, which also emphasizes mirroring human intelligence and learning. Current ML systems and capabilities may be astonishing, the systems are far from flawless. They need quality data and training.
Meanwhile, as automation is fixed solely on repetitive, instructive tasks, machine learning takes these tasks and layers them in an element of prediction. For instance, while automation software helps a business send email alerts to its customers, machine learning helps in identifying the best time to send those email blasts. So, yes it is possible to automate machine learning when it involves the same activity again and again; and leverage machine learning in automation. At the same time, given the dynamic data demands and needs, machine learning models must be able to function independently and with different solutions to match different demands. And this may overkill many processes that can be automated.
Machine learning was designed to work with computers, therefore, it is an ideal sidekick in the push for digitalization in enterprises. However, traditional ML approaches are quite labor-intensive, require a large amount of time from specialists such as Data Scientists or domain experts and offer no guarantee of success. The chances of success is minimal because data scientists follow a sequential approach of data mining, analyzing, filtering the raw data, selecting the algorithm that can be trained, tuning and testing the algorithm and repeating the entire process to find the best algorithm. And to find the ‘best algorithm’ they invest much of their time in training and testing individual algorithms until they find a suitable one.  Also, data scientists invest 60% of their time in cleaning and organizing data sets and 19% of the time in collecting relevant datasets.
Fortunately, today, various stages of the machine learning pipeline are becoming automated through the use of ML techniques, giving rise to Automated Machine Learning (AutoML) tools, both commercial (e.g., DataRobot, Dataiku DSS, Google Cloud HyperTune) and open source (e.g. Auto-WEKA, autosklearn, H2O, TransmorgrifAI, and TPOT). Basically, it ensures end-to-end automation of the ML algorithm and model. AutoML also incorporates best machine learning practices from top-ranked data scientists to make data science more accessible across the organization. AutoML allows businesses to achieve accurate and precise results by conducting automated data analysis.
It helps organizations meet their goals of optimizing workflows and removing productivity barriers by automatically handling tedious time-consuming tasks. This objective is synonymous with that of machine learning and automation. Further, even if AutoML can carry some of the machine learning workflows without depending on data scientists, it doesn’t imply that the data science skill set will become obsolete. In fact, data scientists who embrace AutoML will be able to expand deeper into machine learning capabilities and become even more effective at what they do. Besides, by enabling the crafting of actionable analytics that inform and improve decision-making, AutoML’s importance will rise in future. Also, while the focus of AutoML tools has been automating model selection and hyperparameter optimization, it doesn’t necessarily mean automation and machine learning are joining hands forever – an often misunderstood concept.

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