1. Machine Learning for SQL Use Cases
  2. Overview
  3. Machine Learning Overview

Machine Learning Overview

Machine learning is a subset of Artificial Intelligence (AI) that focuses on building systems that learn or improve performance based on the data they consume.

What Is Machine Learning?

Machine learning is a technique that discovers previously unknown relationships in data.

Machine learning and AI are often discussed together. An important distinction is that although all machine learning is AI, not all AI is machine learning. Machine learning automatically searches potentially large stores of data to discover patterns and trends that go beyond simple statistical analysis. Machine learning uses sophisticated algorithms that identify patterns in data creating models. Those models can be used to make predictions and forecasts, and categorize data.

The key features of machine learning are:

  • Automatic discovery of patterns

  • Prediction of likely outcomes

  • Creation of actionable information

  • Ability to analyze potentially large volumes of data

Machine learning can answer questions that cannot be addressed through traditional deductive query and reporting techniques.

Benefits of Machine Learning

Machine learning is a powerful technology that can help you find patterns and relationships within your data.

Find trends and patterns - Machine learning discovers hidden information in your data. You might already be aware of important patterns as a result of working with your data over time. Machine learning can confirm or qualify such empirical observations in addition to finding new patterns that are not immediately distinguishable through simple observation. Machine learning can discover predictive relationships that are not causal relationships. For example, machine learning might determine that males with incomes between $50,000 and $65,000 who subscribe to certain magazines are likely to buy a given product. You can use this information to help you develop a marketing strategy. Machine learning can handle large volume of data and can be used in financial analysis. Some of the benefits include stock price predictions (algorithmic trading) and portfolio management.

Make data driven decisions - Many companies have big data and extracting meaningful information from that data is important in making data driven business decisions. By leveraging machine learning algorithms, organizations are able to transform data into knowledge and actionable intelligence. With the changing demands, companies are able to make better decisions faster by using machine learning techniques.

Recommend products - Machine learning results can also be used to influence customer decisions by promoting or recommending relevant and useful products based on behavior patterns of customers online or their response to a marketing campaign.

Detect fraud, anomalies, and security risks - The Financial Services sector has benefited from machine learning algorithms and techniques by discovering unusual patterns or fraud and responding to new fraud behaviors much more quickly. Today companies and governments are conducting business and sharing information online. In such cases, network security is a concern. Machine learning can help in detecting anomalous behavior and automatically take corrective actions.

Retail analysis - Machine learning helps to analyze customer purchase patterns to provide promotional offers for target customers. This service ensures superior customer experience and improves customer loyalty.

Healthcare - Machine learning in medical use is becoming common, helping patients and doctors. Advanced machine learning techniques are used in radiology to make an intelligent decision by reviewing images such as radiographs, CT, MRI, PET images, and radiology reports. It is reported that machine learning-based automatic detection and diagnosis are at par or better than the diagnosis of an actual radiologist. Some of the machine learning applications are trained to detect breast cancer. Another common use of machine learning in the medical field is that of automated billing. Some applications using machine learning can also point out patient's risk for various conditions such as stroke, diabetes, coronary artery diseases, and kidney failures and recommend medication or procedure that may be necessary.

To summarize, machine learning can:

  • easily identify trends and patterns
  • simplify product marketing and sales forecast
  • facilitate early anomaly detection
  • minimize manual intervention by "learning"
  • handle multidimensional data

Define Your Business Problem

Enterprises face problems such as classifying documents, predicting the financial outcomes, detecting hidden patterns and anomalies, and so on. Machine learning can help solve such problems provided that you have clear understanding of the business problem with enough data and learn to ask the right questions to obtain meaningful results.

You require skills in preparing data, applying ML techniques, and evaluating results. The patterns you find through machine learning may be very different depending on how you formulate the problem. For example, rather than trying to learn how to "improve the response to a direct mail campaign," you might try to find the characteristics of people who have responded to your campaigns in the past. You can then classify if a given profile of a prospect would respond to a direct email campaign.

Many forms of machine learning are predictive. For example, a model can predict income level based on education and other demographic factors. Predictions have an associated probability (How likely is this prediction to be true?). Prediction probabilities are also known as confidence (How confident can I be of this prediction?). Some forms of predictive machine learning generate rules, which are conditions that imply a given outcome. For example, a rule can specify that a person who has a bachelor's degree and lives in a certain neighborhood is likely to have an income greater than the regional average. Rules have an associated support (What percentage of the population satisfies the rule?).

Other forms of machine learning identify groupings in the data. For example, a model might identify the segment of the population that has an income within a specified range, that has a good driving record, and that leases a new car on a yearly basis.

What Do You Want to Do?

Multiple machine learning techniques, also referred to as "mining function", are available through Oracle Database and Oracle Autonomous Database. Depending on your business problem, you can identify the appropriate mining function, or combination of mining functions, and select the algorithm or algorithms that may best support the solution.

For some mining functions, you can choose from among multiple algorithms. For specific problems, one technique or algorithm may be a better fit than the other or more than one algorithm can be used to solve the problem.

The following diagram provides a basic idea on how to select machine learning techniques that are available across Oracle Database and Oracle Autonomous Database.

Figure 1-1 Machine Learning Techniques

Description of Figure 1-1 follows
Description of "Figure 1-1 Machine Learning Techniques"

OML provides machine learning capabilities within Oracle Database by offering a broad set of in-database algorithms to perform a variety of machine learning techniques such as Classification, Regression, Clustering, Feature Extraction, Anomaly Detection, Association (Market Basket Analysis), and Time Series. Others include Attribute Importance, Row Importance, and Ranking. OML uses built-in features of Oracle Database to maximize scalability, improved memory, and performance. OML is also integrated with open source languages such as Python and R. Through the use of open source packages from R and Python, users can extend this set of techniques and algorithms in combination with embedded execution from OML4Py and OML4R.

Discover More Through Interfaces

Oracle supports programming language interfaces for SQL, R, and Python, and no-code user interfaces such as OML AutoML UI and Oracle Data Miner, and REST model management and deployment through OML Services.

Oracle Machine Learning Notebooks (OML Notebooks) is based on Apache Zeppelin technology enabling you to perform machine learning in Oracle Autonomous Database (Autonomous Data Warehouse (ADW), Autonomous Transactional Database (ATP), and Autonomous JSON Database (AJD)). OML Notebooks helps users explore, visualize, and prepare data, and develop and document analytical methodologies.

AutoML User Interface (AutoML UI) is an Oracle Machine Learning interface that provides you no-code automated machine learning. When you create and run an experiment in AutoML UI, it automatically performs algorithm and feature selection, as well as model tuning and selection, thereby enhancing productivity as well as model accuracy and performance. Business users without extensive data science background can use AutoML UI to create and deploy machine learning models.

Oracle Machine Learning Services (OML Services) extends OML functionality to support model deployment and model lifecycle management for both in-database OML models and third-party Open Neural Networks Exchange (ONNX) format machine learning models through REST APIs. The REST API for Oracle Machine Learning Services provides REST API endpoints hosted on Oracle Autonomous Database. These endpoints enable you to store machine learning models along with its metadata, and create scoring endpoints for the model.

Oracle Machine Learning for Python (OML4Py) enables you to run Python commands and scripts for data transformations and for statistical, machine learning, and graphical analysis on data stored in or accessible through Oracle Autonomous Database service using a Python API. OML4Py is a Python module that enables Python users to manipulate data in database tables and views using Python syntax. OML4Py functions and methods transparently translate a select set of Python functions into SQL for in-database execution. OML4Py users can use Automated Machine Learning (AutoML) to enhance user productivity and machine learning results through automated algorithm and feature selection, as well as model tuning and selection. Users can use Embedded Python Execution to run user-defined Python functions in Python engines spawned by the Autonomous Database environment.

Oracle Machine Learning for R (OML4R) provides a database-centric environment for end-to-end analytical processes in R, with immediate deployment of user-defined R functions to production environments. OML4R is a set of R packages and Oracle Database features that enable an R user to operate on database-resident data without using SQL and to run user-defined R functions, also referred to as "scripts",in one or more database-controlled R engines. OML4R is included with Oracle Database and Oracle Database Cloud Service.

Oracle Machine Learning for SQL (OML4SQL) provides SQL access to powerful, in-database machine learning algorithms. You can use OML4SQL to build and deploy predictive and descriptive machine learning models that can add intelligent capabilities to applications and dashboards. OML4SQL is included with Oracle Database, Oracle Database Cloud Service, and Oracle Autonomous Database.

Oracle Data Miner (ODMr) is an extension to Oracle SQL Developer. Oracle Data Miner is a graphical user interface to discover hidden patterns, relationships, and insights in data. ODMr provides a drag-and-drop workflow editor to define and capture the steps that users take to explore and prepare data and apply machine learning technology.