AutoML: Where Machine Learning Is Headed Next

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AutoML Where Machine Learning Is Headed Next
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Machine learning is not a new concept in cutting
edge tech society and is used in a wide variety
of advanced tech applications, namely, targeted
advertising and data management. Until recently,
a popular shopping app in Japan, called Mercari,
used machine learning in order to classify
photographs. However, a new system, called
automated machine learning, or AutoML, rendered
the apps original methods a thing of the past,
achieving a whopping 15 increase in accuracy,
and motivating Mercari to make the full switch
over to AutoML
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What Is Automated Machine Learning Exactly?
Machine learning has made significant changes in
fields such as healthcare, retail financial
services and even transportation, commonly being
used in research and development, as well as
enterprising. However, traditional machine
learning requires a significant amount of human
power, leaving many businesses unable to make use
of this powerful tool. Its here where AutoML
shines, as AutoML automates the entire process it
would take to apply machine learning to a problem
or task, opening the door for smaller companies
and even non-experts in the field. Traditional
machine learning requires four steps from start
to finish reading and merging, preprocessing,
optimization, and application. AutoML focuses
mainly on data collection and prediction,
effectively the first, and last step of
traditional machine learning, as these are easily
automated already. In doing this, AutoML delivers
a model thats already optimized and more
accurate than older methods.
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Is There a Need for AutoML?
As stated previously, many businesses struggle to
supply the human power needed to apply
traditional machine learning, despite the clear
benefits it could provide them. Not only does
machine learning require a team of experienced
data scientists, but also requires a decision on
what model of machine learning would work best
for the business, which means that the data
scientists, who already claim a premium salary,
would need more experience with the business in
order to be effective. The main benefit of AutoML
is that it fills the demand for a system that can
be used off the shelf by those with less
experience while still offering the same boost to
their business.
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What Are the Advantages of AutoML?

• There are three major advantages that AutoML has
  over traditional methods
• Increased Productivity By automating repetitive
  tasks, data scientists are able to focus more on
  the problem that the model is trying to solve,
  rather than the model itself.
• Avoids Errors Manual data entry inevitably
  comes with manual errors. Automating this
  pipeline helps to avoid most of these problems.
• Democratization In cutting down the required
  workforce required to use machine learning,
  AutoML makes the platform available to a much
  wider user base.

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7 of the Most Popular AutoML Frameworks Available

• MLBox or Machine Learning Box. This is an
  automated Python library that uses machine
  learning to read and distribute data. The
  features of MLBox include the following.
• Gives you a robust and large feature selection
  including accurate hyperparameter optimization
  and leak detection,
• Can preprocess, clean, and format data via fast
  reading and distribution,
• Has modern predictive models like LightGBM,
  Stacking, and Deep Learning for classification
  and regression,
• Can provide predictions based on model
  interpretations.

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7 of the Most Popular AutoML Frameworks Available

• Has been tested on Kaggle with a rank of 85/2488
  which is excellent. MLBox comes with 3
  sub-packages which includes pre-processing for
  reading and processing data, optimization for
  testing and cross-validating, and prediction
  capabilities. It can be installed on Linux at the
  moment but Windows and MacOS support will be
  included in the future.
• Auto-Sklearn. This automated machine learning
  package frees up the user from having to make
  algorithm selections or do any type of
  hyperparameter tuning as it is built on top of
  Scikit-learn. Features of this package include
  numeric standardization, one-hot coding, and uses
  models for classification and regression
  problems. It works by creating a pipeline and
  using Bayesian hyperparameter optimization for
  meta-learning and automated ensemble construction
  for configurations. Unfortunately, it cannot be
  applied to deep learning systems on large
  datasets. It only works with Linux machines at
  the moment.

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• Tree-Based Pipeline Optimization Tool or TPOT for
  Short. This automated machine learning tool is
  Python based, uses and optimizes on machine
  learning pipelines that use genetic programming.
  It extends on the Scikit-learn framework but uses
  its own classifier and regressor methods. It
  works by exploring thousands of pipelines and
  uses the best one for data. Due to this, it
  cannot process natural language inputs
  automatically and it cannot process categorial
  strings as these must be integer-encoded first
  before being passed as data.
• H2O. This is an open-source machine learning
  platform from H20.ai that uses in-memory machine
  learning with support for R and Python. It has
  support for the most widely used machine learning
  and statistical algorithms, including deep
  learning, gradient boosted machines, and
  generalized linear models. The automatic machine
  learning module within H20 will use its own
  algorithms to build a pipeline after performing
  an exhaustive search of its own engineering
  methods and model hyperparameters. This is how it
  creates an optimized pipeline for your data.
  This platform is popular because it can automate
  some of the most difficult machine learning
  workflows and data science, including model
  validation, model tuning, and model deployment as
  well as model selection.

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• AutoKeras. Another open-source automated machine
  learning library which is based on Keras deep
  learning framework by Data Lab. It gives users
  the ability to automatically search for
  hyperparameters and architecture for deep
  learning models. It is an easier and simpler
  platform to use as it is based on Scikit-learn
  API but simplifies the process through automated
  neural architecture search algorithms. It is
  compatible with Python.
• Cloud AutoML. The Cloud AutoML is from Google and
  is for developers who have limited machine
  learning knowledge. It is to aid developers in
  creating high-quality models specific to business
  needs. It utilizes transfer learning and neural
  architecture search technology. It is simply to
  use as it has a graphical user interface that
  gives developers the ability to train, evaluate,
  improve, and deploy models based on unique data.
  Unfortunately, this program is not open-source
  and the price point varies based on which package
  you choose.

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• TransmogrifAI. This is another open-source
  automated machine learning library and is an
  end-to-end library for structured data that is
  written in Scala. This machine learning library
  powers Einstein, the flagship product of
  Salesforce and it runs on top of Apache Spark. It
  is extremely good at training machine models with
  minimal tuning and it can build modular machine
  learning workflows. It requires Java and Spark to
  run though.

What is the Future of AutoML?
Put simply, AutoML was built to automate
repetitive tasks such as pipeline creation and
hyperparameter tuning, just like robots in a
factory, for the purpose of allowing data
scientists to focus on the issue at hand and how
the model is affecting it, rather than the model
itself. An additional bonus to this is that the
technology is available to a larger network of
users, rather than just the businesses with
enough resources to commit to it. Ultimately,
AutoML will play a large part in the future of
machine learning, especially if it continues to
advance and achieve breakthroughs.
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