What Are Hyperparameters in Machine Learning?
For machine learning solutions to deliver business value, ML models must be optimized based on current conditions and desired outputs. Doing so requires the use of hyperparameters, which Collins defines as “adjustable parameters that govern the training of ML models.”
“Optimal ML model performance depends on the hyperparameter configuration value selection; this can be a time-consuming, manual process,” which is where AutoML can come into play, Collins adds.
By using AutoML platforms to automate key hyperparameter selection and balancing — including learning rate, batch size and drop rate — it’s possible to reduce the amount of time and effort required to get ML algorithms up and running.
Who Are the Major AutoML Providers?
While AutoML isn’t new, evolution across machine learning and artificial intelligence markets is now driving a second generation of automated machine learning platforms, according to RTInsights. The first wave of AutoML focused on building and validating models, but the second iterations include key features such as data preparation and feature engineering to accelerate data science efforts.
But this market remains both fragmented and complex, according to Forbes, because of a lack of established standards and expectations in the data science and machine learning (DSML) industry. Businesses can go with an established provider, such as Microsoft Azure Databricks, or they can opt for more up-and-coming solutions such as Google Cloud AutoML.
There are more tools around the corner. According to Synced, Google researchers are now developing AutoML-Zero, which is capable of searching for applicable ML algorithms within a defined space to reduce the need to create them from scratch. The search giant is also applying its AutoML to unique use cases; for example, the company’s new “Fabricius” tool — which leverages Google’s AutoML vision toolset — is designed to decode ancient Egyptian hieroglyphics.
Will AutoML Replace Data Scientists?
Technological advancements combined with shifting staff priorities are somewhat driving robotic replacements. According to Time, companies are replacing humans wherever possible to reduce risk and improve operational output. But that won’t necessarily apply to data scientists as AutoML rises, according to Collins.
“The skills of professional, well-trained data scientists will be essential to interpreting data and making recommendations for how information should be used,” he says. “AutoML will be a key tool for improving their productivity, and the ‘citizen data scientist, with no training in the field, would not be able to do machine learning without AutoML.”
In other words, while AutoML platforms provide business benefits, recognizing the full extent of automated advantages will always require human expertise.