Deep Learning vs. Machine Learning

Deep Learning is a category of machine learning with special advantages for some tasks and disadvantages for others.

Machine learning workflows begin by identifying features within data sets. For structured information with relatively few columns and rows, this is straightforward. Most practical business predictions such as classification and regression fall into this category.

Unstructured data, such as image and voice, have vast numbers of “features” in the form of individual pixels or wave forms. Identifying those features to structured AI algorithms is tedious or impossible. Deep Learning is a technique where the AI algorithm itself extracts progressively higher levels of feature recognition, passing information through potentially hundreds of neural network layers. Deep learning algorithms power image and speech recognition for driverless cars and hand-free speakers

Plusses of Deep Learning

  • Scale – Deep learners can handle vast amounts of data, and they always improve with more data. Shallow learning converges and stops improving with additional data.
  • Dimensions – Deep learners can move past the limitations of a few hundred columns to perform well on very wide structured data set.
  • Non-Numeric – Deep learning brings AI into the human realm of speech and vision, which serve people in new and valuable ways.

Minuses of Deep Learning

  • Training Data – Deep learners need labeled data from which to learn. Amassing sufficient examples for recognition accuracy to be learned can be daunting.
  •  Not for Small Data Sets – Data sets that are too simple or too small cause deep learners to fail by overfitting
  • Resource Consumption – Deep learning on vast data stores can require days or weeks of processing on a single problem. 

The take-away: Deep learners are great for unstructured data and may be useful for classification with large and detailed structured data sets. Squark includes deep learners in the stack of algorithms it uses for AutoML. You will know from the Squark Leaderboard whether deep learning was a winner.

Classification Types and Uses

Classifications are the most frequently used—and most useful—prediction types.

Classifications are predictions that separate data into groups. Binary Classification produces “yes-no” or “in-out” answers when there are only two choices. Multi-Class Classification applies when there are three or more possibilities and shows probabilities for each.

Binary Examples

  • Churn – Which customers are in danger of leaving? If you knew, you could implement targeted retention tactics such as special offers or customer service outreach.
  • Conversion – Which prospects are most likely to be ready to move to the next stem in the buying cycle? Knowing means focusing sales resources on the best leads.
  • Risk – Which populations are likely to experience negative outcomes? Understanding helps guide actions to mitigate risks.

Multi-Class Examples

  • Cross-Sell/Up-Sell – Which customers are most likely to buy which additional products or services? Targeting them with the right offers lifts sales with high efficiency.
  • Personalization – Which content will resonate with which person? Optimizing websites, social media, and email is easy when you know.
  • Ad Targeting – Which prospects are most likely to respond to your multiplicity of ads and media? Spending is more effective when you know your audiences.

The take-away: Classifications are among the most accessible and highest-return prediction types for AutoML. Predictions on each row include not only the classes, but the probabilities associated with each. Think of a burning classification question and Squark can help you begin predicting right away.

Operationalizing AutoML: ML Ops

Predictions are interesting on their own. They are valuable when put into production.

Operationalizing AutoML – often called “ML Ops” – means putting AutoML predictions into regular workflows to change business outcomes. Here are a few ways do it.

Graphical Interface
Saas AutoML tools have a GUI that enables training data ingestion, data prep, feature engineering, and model building. Once an optimal model is made, predictions are created on production data. Simply exporting the predictions from the GUI delivers a data file for people or other systems to act upon. For example, predictions the prioritize sales lead follow-up could be handed to sales ops as a daily call list, or could be imported to a marketing automation system as an email segmentation list.

API
Application Programming Interfaces (APIs) are specifications that describe how dissimilar systems can communicate reliably. AutoML APIs can be hooked to enterprise systems to automate export of predictions to eliminate manual file handling.

Model Export
AutoML produces executable code, typically Java bytecode, that can be run wholly outside the AutoML tool itself. Models so exported can be run again and again on production data as often as required. When models are improved based on new training data, ML ops can simply replace the executable code with the new models.

Custom Deployment
Some applications, such as real-time predictions, require close integration with cooperating systems. Customized data pipelines can be created to manage these processes.

The take-away: You can begin using AutoML results to improve business performance right now. As needs expand, there are many options for blending ML Ops into automated workflows, and Squark can help with all of them.