With the widespread use of artificial intelligence (AI), “machine learning”, which is closely related to artificial intelligence, is beginning to attract attention and affect various fields. By applying machine learning, it is possible to create new business opportunities and increase operational efficiency. A mechanism called “pipeline” plays an important role in the implementation of this type of machine learning. In this article, we will provide a detailed overview of machine learning pipelines, their benefits, and how to learn them.
What is a Machine Learning Pipeline?
A machine learning pipeline is a system that allows data processing to be performed in several stages to create the “predictive models” required for machine learning. By connecting multiple processing programs and creating a pipeline, multiple operations can be executed automatically one after another without the need to perform each step manually.
Predictive models are mathematical formulas and rules that computers need to make decisions, and large amounts of data must be analyzed to create them. Machine learning pipelines play an important role in creating efficient and easy-to-use processing programs to perform this data analysis task.
what is machine learning?
Machine learning is an analytical technology that uses computers to analyze large amounts of data in order to learn and discover hidden patterns in the data. By memorizing the rules that match the learning data, it becomes possible for the computer to automatically make “predictions” and “decisions” based on the rules. This principle is called the “prediction model”.
Manually analyzing large amounts of data and making predictions takes a lot of time, but computers have the advantage of being able to process them faster. Moreover, since the data is processed mechanically, the accuracy of the work is also high.
The term “AI (artificial intelligence)” is often confused, but it can be said that machine learning is one of the technical elements required to realize artificial intelligence.
General Machine Learning Flow
Machine learning generally proceeds in the following steps.
- Collecting training data
- Data preprocessing
- Designing training data (for supervised learning)
- Getting Started with Machine Learning
- Estimate accuracy based on test data.
- Tuning hyperparameters (model settings)
- Deploying Machine Learning Models.
Step 4 applies to the task called “machine learning”. In step 2, we transform and process the data into data that machines can process, and remove unnecessary duplicate data. This process is called “data cleaning”. In addition, the numerical representations of data features are called “features”, and the preparation task involves selecting features that are highly relevant to the learning content.
What are the types of machine learning?
Machine learning includes various learning methods, such as “supervised learning”, “unsupervised learning”, “semi-supervised learning”, “reinforcement learning”, and “deep reinforcement learning”. Here we will introduce three common methods.
Supervised learning
Supervised learning is a method in which the input material and valid response data (supervised data) are created as a set and rules and features are learned to produce the correct output according to the correct answer.
The problems involved in supervised learning can be divided into two categories: classification and regression. In classification, classification is done according to rules such as “dog or cat” or “spam mail or not”, while in regression, continuous values such as “how to do sales and prices change” are classified.
Unsupervised learning
No valid response data is created for unsupervised learning. It analyzes and learns rules by finding patterns and structures from infinite input data. You can calculate the similarity and proximity between the data and understand the connections between the data and the group data.
The method of grouping similar items is called “clustering” and is used in the recommendation function of online shopping sites.
Reinforcement learning
Reinforcement learning is a learning method that allows an agent to perform actions that match the environment in a system consisting of two elements: the “agent” and the “environment”. Unlike supervised and unsupervised learning, data is not available at the beginning, but learning is done based on the results of repeated trials.
In reinforcement learning, an agent refers to something responsible for making decisions and taking action. Examples include AI which plays games by finding optimal solutions on its own and robots which learn how to act more consistently through trial and error.
8 Benefits of Machine Learning Pipelines
How does the machine learning process change using the machine learning pipeline mechanism? Here we will introduce eight specific benefits that can be achieved through pipelines.
1. Machine learning processes can be modularized.
Modularization refers to the method of dividing the whole into several units for ease of use.
Connecting machine learning to a pipeline allows you to modularize the learning process, isolating each step and developing, testing, and optimizing it separately. It makes the workflow easier to manage and maintain.
2. Repeatable
Learning using a machine learning pipeline is repeatable by clearly defining the steps and settings when creating the pipeline.
The advantage is that it can reproduce the entire process as it is and deliver consistent results.
3. Tasks are automated.
Connecting machine learning to a pipeline allows you to automate a number of tasks, such as preprocessing training data, evaluating models, and transforming data into something that machines can easily understand.
Many tasks can be completed mechanically faster than manually, and you don’t have to worry about human error, which saves a lot of time.
4. Highly scalable
When using large-scale datasets or when complex workflows need to be adapted, there may be cases where the process needs to be re-engineered to perform machine learning.
Machine learning pipelines are highly scalable and can flexibly respond to increasing resource requirements and workflow complexity, so there’s no need to redesign the entire process in these cases.
5. Flexible
A pipelined machine learning process provides the flexibility to change just a few steps and try different scenarios. You can quickly improve performance by changing the data preprocessing technology, changing the “feature” selection related to prediction accuracy, or trying different prediction models.
6. Deployment is seamless.
Creating a well-defined pipeline to train and test machine learning models makes it easy to integrate them into applications and systems. Therefore, machine learning pipelines have the advantage of being able to easily deploy machine learning models in a production environment.
7. Collaborating with data scientists, etc. is easy.
Machine learning pipelines have organized and documented workflows that make it easy to share knowledge within the team and for all team members, including data scientists and engineers, to understand the process. This allows us to create a system where everyone can easily coordinate and contribute to the project.
8. Easy version control
If you make changes to your pipeline’s code or configuration and need to revert later, a version control system makes it easy to revert to a previous version. Additionally, as mentioned above, it is possible to document the details of each step, making it easier to understand and manage information about each version.
History of Machine Learning Pipelines
Today’s formal machine learning pipelines have only recently emerged. The process of formalizing machine learning pipelines is closely related to the history of machine learning and data processing. Here, we will summarize the history of the respective technologies up to the present day.
Data processing before the spread of machine learning
The basic technology for data processing began to spread in the 1960s, and companies began using computers to process data. On the other hand, although the concept of artificial intelligence was born in the 1950s, machine learning did not become widely used until much later.
Before the 2000s, machine learning was used for specialized tasks such as data cleaning, transformation, and analysis, and was not central to the process. Also, at that time, the work was done manually.
Adoption of machine learning in various fields
In the 2000s, the rapid spread of the Internet and the dramatic increase in the amount of data processed gave rise to the concept of “big data.”
With the potential of using large-scale datasets, advances in computing power, and advances in machine learning algorithms, machine learning began to gain traction at this time. Machine learning has been used in various fields and has contributed to increasing the accuracy of data analysis.
Popularity of data science
“Data science” is a term that refers to a field that combines statistics, data analysis, and machine learning. Although etymology itself has been around for a long time, it gained importance in the late 2000s to early 2010s with the proliferation of big data analysis and machine learning applications.
During this time, data science workflows such as data preprocessing and model selection and evaluation were formalized. These are also the basic elements of machine learning pipelines.
Development of machine learning libraries
The development of machine learning libraries and tools in the 2010s made it easier than ever to create pipelines. Libraries and frameworks in programming languages such as Python and R that can be used in the field of data science, in general, would also begin to develop.
The development of data visualization tools and data analysis tools has progressed, and a variety of highly functional and easy-to-use tools have emerged.
Introduction to AutoML (Automatic Machine Learning) tools
One of the tools that emerged in the 2010s is “AutoML (Automatic Machine Learning)”. This tool allows you to automate the process of designing and building machine learning models.
In particular, everything from data collection to machine learning model building can be automated. Before the emergence of AutoML, these tasks were done manually and the process required advanced specialized knowledge and technology, which led to a shortage of labor. However, tools have made it easier for non-experts to use machine learning with tools that allow for automation, visualization, and tutorials.
The Emergence of MLOps (Machine Learning Operations)
In addition to the emergence of tools, another thing that has had an impact on facilitating machine learning processes is the emergence of MLOps (Machine Learning Operations). MLOps is a concept that aims to streamline operations and increase efficiency by creating pipelines for machine learning teams, development teams, and operations teams.
It is derived from “DevOps”, a software development methodology for rapid development. While DevOps focuses on general software development, MLOps is an approach specific to machine learning systems.
How to Learn Machine Learning and Pipelining?
Although useful tools have emerged, I think many people think, “First, I want to learn the basics of machine learning and pipeline processing.” Examples of important methods to work on:
Read specialized books and study independently.
There are a variety of specialized books on machine learning pipelines and related technologies, from introductory books to more challenging books. If you want to study on your own and get general information, we recommend reading specialized books first.
Specialized books are ideal for learning the basics, as they explain everything in an organized manner, starting from the basics and often covering a wide range of information. Also, if you carry a book with you, you can always look up what you don’t understand, just like a dictionary. It is a study method that can be used again and again and makes it easier to remember the basics.
Learn programming at school.
You can also learn about machine learning at programming schools. This method is suitable for those who want to acquire specialized knowledge and techniques. Although it may cost a certain amount, you can get direct guidance from experts.
There are many types of programming schools, each with different features. It is important to choose a course by considering the conditions that suit your goals and lifestyle, such as whether you can learn the subject you want to study, whether you can join while working, whether you have a history of change, or finding a job. Employment In recent years, there has been an increase in the number of schools that offer online courses and allow you to take courses at a time that suits you.
Learn practically using IT tools.
It is important to practice to build a solid understanding of machine learning knowledge and techniques.
A variety of IT tools are now available, including those that support machine learning. In most cases, these tools allow you to easily experiment with machine learning by providing the algorithms needed to create artificial intelligence. This way, you can practice hands-on even if you do not have the skills to build a machine-learning model yourself from scratch.
Also, if you choose a cloud-based tool, there is no need for server development, and the effort required for implementation is reduced.
Questions and answers about machine learning pipelines
Are there any disadvantages to AutoML tools?
When using tools to automate the construction of machine learning pipelines, it is important to note that the building material becomes a black box. Therefore, it is not suitable if you want to learn the construction method yourself.
When problems arise, they can also be difficult to detect and resolve. It is important to understand in advance what AutoML can and cannot do and check if it is suitable for the purpose of creating a machine-learning pipeline.
What are the use cases of machine learning pipelines?
Machine learning pipelines are used to increase business efficiency using AI.
In the case of a large logistics company, a machine learning model is created every month to predict the volume of work, and previously the process from data extraction to model evaluation was done manually. Since the workload was very high, we created a machine learning pipeline that automatically executes a series of these tasks and makes it possible to execute them according to a flexible schedule.
System development for Sky Corporation
Sky Corporation sends engineers and contracts for a wide range of system development tasks. We can provide technology at every step, from requirements definition to design, development, validation, operation, and maintenance.
In the field of data analysis, in addition to collecting data from cloud and edge devices, we also listen to the voice of the field and bring unique knowledge, experience, and expertise to the analysis content. Additionally, high-efficiency system developments such as efficient data analysis are possible using AutoML.
If you have any concerns regarding data analysis or usage, such as data analysis methods, how to use the information on the site, or how to choose detection/prediction models, please do not hesitate to contact us.
Summary
Machine learning pipelines have many advantages and are considered an important mechanism to facilitate the machine learning process. With the spread of data science, this has become formalized and there are now simple tools that can automate construction.
We hope that the content we present this time will be useful for those who want to “learn more about machine learning pipelines” or “actually apply them in their work”.