AutoML (Automated Machine Learning) is considered as one of the newest tools in the field of artificial intelligence. The technology is being used to eliminate barriers and roadblocks to entry that hold back enterprises’ ML ambitions. AutoML provides with flexibility while being user-friendly, easily operated by employees having a limited background in data science.
The technology performs on the idea of automating the machine learning process to make more enterprise-friendly delivering faster inputs. The companies which have limited data science teams can use AutoML tools to produce desired results.
With all the competition prevailing in the market, experts are encouraging simple and steady approach – Problem Evaluation, Tools & Providers Evaluation, then selecting the right tool to address the issue. In context to AutoML tools, enterprises often tend to seek for popular vendor platforms. Such tools are present in a variety, differing widely based on needs. Therefore, it becomes necessary to know your need first and then choose the right tool.
Below are the categories divided by Carlie Idoine, senior director and analyst of data science and business analytics at Gartner, through which one can evaluate AutoML tool.
Building Suitable Platforms On Your Own
In this approach, data science teams at enterprises build platforms on their own allowing themselves to control parameters, tuning, machine learning operations, and model assessment through individual open source or proprietary code. Such a platform enables hyper-personalized augmentation, means one can have SigOpt for parameter tuning and Algorithmic or ParallelM for MLOps.
Commercial AutoML Platform
Although it is an extension of the traditional ML platform with AutoML abilities, this category adds some augmented artificial intelligence capabilities. It gives you more breadth and depth of capability along with features to support collaboration between experts and non-experts.
Augmented Platforms Addressing Specific Problems
These platforms enable citizen data scientists to develop and deploy their ML applications to address specific problems. However, because of less customization, they can be limited in the range of problems they can tackle. DataRobot, Aible, Big Squid and Tazi are some leaders of the augmented platform.
APIs: Cloud ML Services
These platforms are essentially APIs inclined toward application developers and people who wish to integrate AI into existing applications. Such platforms are purpose-built and tend to focus on specific tasks, say – computer vision, language processing, and translation. Amazon Web Services, Google Cloud and Microsoft Azure are top vendors in this category.
What Is Best To Opt: Build Or Buy?
The quality and quantity of data science team, its capabilities and ultimate goals are major factors to consider while determining whether to build or to buy an AutoML platform. If an enterprise has a small data science team and wishes to offload some of the model building work onto a platform vendor, then it can choose a commercial or augmented platform. The tool and vendor must be selected only after evaluating and confirming that it can address an enterprise’s machine learning goals.
Evan Schnidman, CEO of natural language processing company Prattle said – “There are a lot of service providers that are primarily doing the grunt work by cobbling together a suite of open source packages. If you’re going to work with a service provider, asking what tools they’re using is just as important as going out and figuring out what tools you can use in-house.”
On the other hand, the companies with the great data science team and evolved AI strategy can go for their in-house tools to address issues. But one should remember that selecting one’s parameters and personalized model building is not always a simple and or quick beginning into AutoML platform architecture.
Key Features To Evaluate For AutoML Platforms
Analyze capabilities of platform around their pattern modeling and automation specificity, and match them to the current needs.
In some cases, these platforms are quite an entrance point into ML and AI without any requirement of heavy investment in data scientists. In case, enterprises only need a place, to begin with, Idoine and Schnidman suggest citizen data scientist platform.
According to Idoine – “A benefit to the citizen data scientist platform is that it’s expressly for citizen users, so it’s easier to get going faster.”
The enterprise seeking to augment their data scientists’ work, speed and efficiency of an AutoML platform is a great deal, suggested by Schnidman. And for those who already have a team of data scientists, experts encourage collaboration between the users of autoML tools and those who will oversee the machine learning process. Such platforms and tools must be focused on augmenting capabilities rather than not replacing workers.