Understanding the Treasures Behind Automated Machine Learning

by January 31, 2019 0 comments

There has been an increasing hot debate whether the new age of automated data scientists has arrived. While it may take some time to register, but welcome to an era of automated machine learning which represents a fundamental shift in the way organizations of all sizes will approach machine learning and data science in the years to come. So why is automating ML such a trend? Applying traditional machine learning methods to real-world business problems has become resource-intensive, time-consuming and challenging, requiring domain experts in several disciplines, including data scientists who come with a fat paycheck and are the most sought-after professionals in the job market right now.

Is it easy to automate the skills that data scientists possess? No is the answer, as the person-specific skills are hard to automate, and people who seek to buy automated AI should be aware of what exactly they can automate, and what they cannot, with the present technology.

We have spoken and written a lot about the Automated ML but what is this technology that is making people sit up and take notice?

 

A Deep Dive into AutoML

 

Training an ML model

Isn’t a machine learning model trained automatically? Partly yes because of which it is called as machine learning, where the algorithms learn and relearn by themselves, based on the data inputs. However, the most tedious and time-consuming tasks are the ones that are traditionally undervalued when publishing amazing results and have nothing to do with the actual training of the model.

 

Assimilating the Meta-Knowledge from ML Experiments and its Application

One reaches to a good model from ML application, when some insights into the data are analyzed by a more experienced data scientist, for a long term gain. The data scientist has understood the properties of the ML problem and has subconsciously recognized some patterns to reuse his/her knowledge and provide a quicker, more direct solution and, when the organization is confronted with a similar problem in the future.

 

Knowledge Transfer across Learning Tasks

In the path towards Automated ML, if meta-learning is concerned with creating meta-knowledge, then transfer learning is all about discovering the correct ways to use it. There are a host of applications that have successfully used transfer learning, which includes an agent that learns skills for Minecraft and a deep neural network that plays different Atari games.

 

Steps closer to Automate Machine Learning

How to automate Machine Learning, here are a few steps that will help:

 

Hyper-Parameter Optimization

In data science, the knobs on an algorithm are called hyper-parameters, a search which is performed by the data scientists as they test different combinations of those hyper-parameters, different ratios between their ingredients.

Hyperparameter search can be automated for deep neural net training. It is the equivalent of the open-source Python library Spearmint or Google Tensorflow’s Vizier. Some startups, like SigOpt, are focused solely on hyperparameter optimization with different kinds of search algorithm, like grid search, random search and Bayesian methods.

 

Selection of Algorithm

To determine which algorithm can learn best on the given data is to run it on the same data through several algorithms whose hyper-parameters are set by default, a task often undertaken by AI vendors. At the end of the entire process, they select the winner. However, this process has its own limitations, notably in the range of algorithms that are chosen to run in any given race, and how well they are tuned to fit into different models.

 

The Final Words

Automated machine learning changes the present scenarios, making it easier to build and use machine learning models live by running systematic processes on raw data and selecting models that pull out the most important information from the data which is often referred to as the signal in the noise. Automated machine learning brings together the best practices of machine learning from the top-ranked data scientists to make data science more accessible across the organization. The Holy Grail to automate machine learning has begun with many machine learning vendors, including Google to start-ups like H2O.ai and DataRobot claiming that they can automate machine learning, which sounds great! This puts hiring managers, in a comfortable position where they won’t go chasing after data science talents who come with a hefty price tag in a bidding war.

Automated machine learning has been under the watch-list of AI over a couple of years. Although large companies like Google and Amazon have already launched their AutoML tools, which has attracted significant interest in terms of research and development which makes significant positive steps ahead towards understanding the nature of machine learning problems. AutoML till then is still a niche term, reserved for the avant-garde of the data science community.

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