Automated machine learning is one of the trendiest and most popular areas of enterprise AI software right now. With vendors offering everything from individual automated machine learning tools to cloud-based, full-service programs, autoML is quickly helping enterprises streamline business process and dive into AI.
In light of the rise of autoML, analysts and experts are encouraging enterprises to evaluate their specific needs alongside the intended purpose of the tools -- to augment data scientists' work -- instead of trying to use autoML without a larger AI framework.
Whether your enterprise has a flourishing data science team, citizen data science team or relies heavily on outsourcing data science work, autoML can provide value if you choose tools and use cases wisely.
AutoML and data scientists
Enterprises are applying automated machine learning in a diverse range of use cases, from developing retail insights to training robots. Whatever the environment or the business process being automated, experts said the real promise of autoML is the ability to collaborate with data scientists.
"Make sure that you're using [autoML] for the right intended purpose, which is automate the grunt work that a data scientist typically has to do," said Shekhar Vemuri, CTO of technology service company Clairvoyant, based in Chandler, Ariz.
AutoML tools are being used to augment and speed up the modeling process, because data scientists spend most of their time on data engineering and data washing, said Evan Schnidman, CEO of natural language processing company Prattle, based in St. Louis.
"The first ranges of tools are all about how [to] streamline the data ingestion, data washing process. The next ranges of the tools are how [to] then streamline model development and model deployment. And then the third ranges are how [to] streamline model testing and validation," he said.
Still, experts warned autoML users not to expect automated machine learning tools to replace data scientists.
AutoML and augmented analytics do not fully replace expert data scientists, said Carlie Idoine, senior director and analyst of data science and business analytics at Gartner.
"This is an extension of data science and machine learning capability, not a replacement," she said. "We can automate some of the capabilities, but it's still a good idea to have experts involved in processes that may be evaluating or validating the models."
Intention equals value
If an enterprise intends to automate or augment a part of the data science process, it has a chance to succeed. If it intends to replace data science teams or expects results overnight, autoML technology will disappoint. Choosing the tool or program will depend heavily on the intention, goal and project for which the enterprise is solving.
"A key realization should be that we're using autoML to essentially gain scale and try out more things than we could do manually or hand code," Vemuri said.
Carlie IdoineSenior director and analyst of data science and business analytics at Gartner
Schnidman echoed the sentiment, calling autoML a support tool for data scientists. Businesses that have a mature data science team are poised to get the most net value, because the automated tools are an extension of data scientists' capabilities.
"[AutoML works for those who say,] 'We've done this manually and taken it as far as we can go. So, we want to use these augmented tools to do feature engineering, maybe take out some bias we have and see what it finds that we didn't consider,'" Idoine said.
If enterprises intend for autoML to replace their data science team, or be their only point of AI development, the tools will give limited advantages. AutoML is only one step of many in an overall AI strategy -- especially in enterprises that are heavily regulated and those affected by recent data protection laws.
"Regulated industries and verticals have all these other legal concerns that they need to keep in mind and stay on top of. Make sure that you're able to ensure that your tool of choice is able to integrate into your overall AI workflow," Vemuri said.
Limitations of tools
The biggest limitation of automated machine learning tools today is they work best on known types of problems using algorithms like regression and classification. Because autoML has to be programmed to follow steps, some algorithms and forms of AI are not compatible with automation.
"Some of the newer types of algorithms like deep neural nets aren't really well suited for autoML; that type of analysis is much more sophisticated, and it's not something that can be easily explained," Idoine said.
AutoML is also wrapped up in the problem of black box algorithms and testing. If a process can't be easily outlined -- even if the automated machine learning tool can complete it -- the process will be hard to explain. Black box functionality comes with a whole host of its own issues, including bias and struggles with incomplete data sets.
"We don't want to encourage black boxes for people that aren't experts in this type of work," Idoine said.