Automated Machine Learning (ML) automates the steps in your ML workflow, including preparing the data, training the model, evaluating the model, tuning parameters, and generating predictions. This makes your work easier, less onerous, less time-consuming, cheaper, and more accurate.
Auto ML is an emerging trend in high tech, with some conspiracy theorists warning it will eliminate your tech job. No worries! Careers in data science are here to stay, and automation just gives you more opportunities!
The Purpose of Automated Machine Learning
Automated ML automates every part of your ML pipeline, from data preparation to product deployment. Features include:
-
Cleaning the data - includes removing duplicate or relevant information, dealing with missing values, fixing structural errors, and handling outliers.
-
Feature engineering - injects the model with features that make it more likely to give you the predictive results you want.
-
Model selection - chooses one of many candidate models for a predictive modeling problem.
-
Hyperparameter tuning - selects the best parameters for the model's architecture.
-
Model deployment - integrates the model into the production environment and verifies that it produces desired results.
Data Preparation
Auto ML identifies your type of data - - Boolean, discrete, continuous, or text. It also performs task detection. For example, it explores whether the data represented is binary; what is the classification? What about regression or clustering and ranking? Finally, Auto ML examines if your data is ready accordingly.
Feature Engineering
Once the data has been cleaned and is ready for training, data scientists have the tedious task of preparing a suitable predictive model. Auto ML does all that work for you in minutes.
-
Feature selection - chooses the best set of features for your model to help it predict as required.
-
Data preprocessing - converts the raw (original) data into a readable format.
-
Feature extraction - retains only the critical features and data that your model needs to become useful, eliminating anything redundant or irrelevant.
-
Skewed data detection eliminates or corrects skewed data (namely outliers that appear in the raw data, which will distort your data if you keep them).
-
Missing values detection - fills in missing data (for example, if participants have omitted a survey question in the data fed to the model, the model inserts a 0).
Model Selection
Model selection includes finding the best type of model to use and the specific structure most suitable for a given data test. This is followed by model evaluation, where automation helps you scrutinize the entire process, from validation procedures to error-rooting, analysis, and configuration.
Hyperparameter Tuning
Hyperparameters are your best guesses for approximate model parameters. Done manually, this can take a while, requiring familiarity with algorithms and their strengths and weaknesses. The work needs to be thorough and carefully designed. Unsurprisingly, there are few data scientists available for this critical step. Nevertheless, Auto ML does the task at a fraction of the cost and time and fewer errors!
Deployment
Auto ML helps you deploy the model as a web service to predict new data without writing code. It also allows you to test its generated predictions and fine-tune results.
Use Cases
Auto ML is most commonly used for the following functions:
-
Proof of concept - To help you decide whether the design is feasible. For example, whether to proceed with a specific software application.
-
Baseline model - Using a good-enough model for decent results, for example, testing on a previous project to guide you in your task.
-
Deploy to production - Auto ML is used as an end-to-end tool to expedite, improve and automate your labor.
Tools
The most popular Auto ML applications are:
-
RapidMiner - Free student version available
-
Dataiku - Free community version available
-
DataRobot - Commercial
-
H20 Driverless - Commercial.
Google Cloud AutoML
Google Cloud AutoML has a range of services that include the following:
-
AutoML Vision for object detection.
-
Video intelligence API for classifying video segments and object tracking in videos.
-
AutoML Natural Language and Auto ML Translation for translating textual data.
-
AutoML Tables for prediction and classification from structured data, like databases or spreadsheets.
Wrap-up
Auto ML typically provides faster, more accurate outputs than hand-coded algorithms, saves companies money on training staff or hiring experts, and makes ML more accessible to novitiates or organizations that lack the funds to hire skilled data scientists. That said, Auto ML is here to improve your data efficiency, not replace it. So, although you no longer need to be involved in the step-by-step ML process, you will still want to evaluate and supervise the model.
Let's Connect!
Leah Zitter, Ph.D., has a Masters in Philosophy, Epistemology, and Logic and a Ph.D. in Research Psychology.
