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How To Build A Machine Learning Filing System To Classify Books

Having recently successfully developed complex neural networks for classifying books for one of the largest Eastern Europe publishing houses, we at DevTeam.Space gained valuable insight into how machine learning (ML) filing systems are helping organizations to stay ahead of the competition while dramatically improving their in-house operations.

Machine learning has the potential to deliver massive value to companies of all sizes, something I have already explained in “Machine learning in future software development”.

Are you wondering how your organization can develop an ML-powered efficient filing system? If so, this guide on how to build a machine learning filing system to classify books etc. is exactly what you need.


Growing volume of high-value content: A good problem to have!
Machine learning: A brief introduction
ML algorithms: What they are
Use cases of ML and its global market
Document classification: A key use case of ML
Building a machine learning filing system to classify books
Planning to launch an ML filing system to classify books, etc.?

Growing volume of high-value content: A good problem to have!

Some problems are good to have! Your business or organization is growing rapidly. You have a tremendous amount of content that delivers value to your customers, and your team is adding more content regularly.

Your customers are happy with the high-quality content you provide, however, you are struggling to manage this growing volume of content! Sounds familiar? Well, first of all, congratulations for serving your customers well with valuable content!

Your organization isn‘t the only one facing the challenge of content overflow, as you can read in “A new generation of content management problems”. It‘s possible to manage your growing content, and Machine Learning (ML) is here to help you.

Machine learning: A brief introduction 

Machine learning (ML) is a discipline within Artificial Intelligence (AI), the interdisciplinary branch of computer science and technology. The core foundational premise of ML is that computers can learn from data, and they can identify patterns from it.

Aided by this capability, computers can then make decisions, as explained in “Machine learning | what it is and why it matters”. In other words, ML is a method to analyze data, which enables computers to build analytical data models automatically.

The key differentiator in ML is that computers “learn” without any explicit programming to train them. This learning takes place with the help of ML algorithms, therefore, let‘s understand what they are.

ML algorithms: What they are

ML algorithms are the building blocks of this technology, and they are of the following types:

  • Supervised learning: You use supervised learning algorithms when you have known input and output data. Such algorithms “train” computers to respond to questions based on labeled data.
  • Unsupervised learning: If you have data where the answers to questions aren‘t known, then you need to use unsupervised learning algorithms. There are no labeled data, therefore, the computer “learns” to identify hidden patterns and structures from the data.
  • Semi-supervised learning: Semi-supervised learning algorithms use a mix of labeled and unlabeled data.
  • Reinforced learning: These algorithms train computers using a trial-and-error approach. Computers learn from experience and improve their decision-making accuracy based on feedback.

You can read more about these in “Machine learning types and algorithms”.

Use cases of ML and its global market

ML has a wide range of use cases, e.g.:

  • Enterprises can use ML in conjunction with rule-based automation to achieve Intelligent Process Automation (IPA) of complex tasks like insurance risk assessment.
  • Businesses can optimize their sales and marketing functions with ML since it helps in predictive lead scoring, intelligent ad placements, etc.
  • Chatbots can learn to solve more customer queries with the help of ML, thus achieving greater efficiency.
  • ML can strengthen cybersecurity solutions with predictive analytics and behavioral analytics.
  • This technology can enhance real-time language translation and provide intelligence from images.

Read more about these use cases in “Top 5 use cases for machine learning in the enterprise”.

Given its importance, it‘s no surprise that the global market for ML is poised to grow significantly. A MarketsandMarkets report pegs the market for ML at $8.81 billion by 2022, growing from $1.41 billion in 2017. This report estimates that the global market for ML will see a CAGR of 44.1% during this period.

Document classification: A key use case of ML

Now that you have a fair understanding of ML, let‘s understand how it can help to build a filing system to classify books. Document classification has emerged as a key use case of ML, and it uses Natural Language Processing (NLP), a key AI capability.

An ML-powered filing system classifies text, which helps in assigning one or more categories to a document. As a result, your organization will find it easier to manage and sort documents. Any business or organization dealing with a lot of content can benefit from this, and examples of such businesses are publishers and news sites.

ML-powered systems for document classification might use text classification capabilities, and such a system might work at different levels, e.g.:

  • Document-level;
  • Paragraph level;
  • Sentence level;
  • Sub-sentence level.

You can read more about documentation classification using ML in “Document classification using machine learning”.

Document classification using ML involves the following steps:

  • Selecting an appropriate dataset with a sufficiently large number of documents;
  • Pre-processing, which could involve assigning different weight to words based on their importance;
  • Using an appropriate classification strategy and suitable ML algorithms.

Building a machine learning filing system to classify books

I will now explain the steps you need to take to build a machine learning filing system to classify books. These steps are as follows:

1. Agree on a project scope

As the first step, you need to induct a competent project manager (PM) in your team and work with various stakeholders to define the project scope. You also need an IT architect and business analysts.

Together, they should work to define the project scope. I recommend that you start with building a web app with ML-powered document classification as its key functionality.

2. Choose an appropriate methodology for the project

It‘s now time to strategize the project. You need to choose the right methodology for this project, and I recommend the Agile methodology. Experts contend that the deployment of AI and ML systems benefit from the Agile methodology, as you can read in “5 ways to improve AI/ML deployments”.

3. Project planning and estimation

A detailed project plan is a key to the success of your project, moreover, you also need a budget-quality estimate for the project. The business stakeholders in your organization need these to provide the necessary green signals for the project.

Your PM and architect can consult our guides for this, e.g., they can read “AI development life cycle: Explained” to get a good grasp of the AI development lifecycle. You can also consult “How much does it cost to develop an AI solution for your company?”, which will help you with the project estimation.

4. Determine a development approach for the project

Your project team should adopt the following approach for this project:

  • Use a managed cloud service so that you can focus on development, and not IT infrastructure management.
  • Expedite the project with an NLP software development kit (SDK) or application programming interface (API).
  • Utilize a test automation aid to enhance test coverage.

You can read “What is the best development approach to guarantee the success of your app?” to understand why this approach is useful.

5. Build your project team

You now need to induct the remaining roles for your project team, and these are as follows:

  • UI designers;
  • ML/AI developers with Python skills;
  • Web developers with Node.js skills;
  • Testers;
  • DevOps engineers.

ML is a niche skill, therefore, you can expect this project to be a complex one. I recommend that you induct a field expert development team for such projects, as I have explained in “Freelance app development team vs. field expert software development teams”.

6. Use a managed cloud services platform

You can expedite the development of the proposed web app with NLP capabilities by using a managed cloud services platform. I recommend that you use a Platform-as-a-Service (PaaS) platform since you can get several advantages:

  • Reputed PaaS providers manage cloud infrastructure, networking, storage, operating system, middleware, and runtime environment. This frees you up, therefore, you can focus on development.
  • You can easily scale your web app when using a reputed PaaS platform since they provide robust application performance monitoring (APM) and auto-scaling solutions.
  • It‘s easy to integrate database and 3rd party APIs when you use a PaaS platform.
  • Well-known PaaS providers have robust DevOps tools, therefore, you can take advantage of continuous integration (CI) and continuous delivery (CD) capabilities.

You can read “10 top PaaS providers for 2019” to learn more about the advantages of using a PaaS platform.

AWS has excellent cloud capabilities, and it offers AWS Elastic Beanstalk, i.e., its PaaS platform. I recommend that you use it in this project.

7. Find an NLP SDK/API solution

Using an SDK/API solution for implementing the NLP capabilities could expedite your project, and I recommend that you use Amazon Comprehend. This is an NLP service from AWS, and it uses ML to find insights and relationships in texts.

Amazon Comprehend has several valuable features that will help you to build an ML filing system to classify books, e.g.:

  • Keyphrase extraction;
  • Sentiment analysis;
  • Syntax analysis;
  • Entity recognition;
  • Relationship extraction;
  • Custom entities;
  • Language detection;
  • Custom classification;
  • Topic modeling;
  • Multiple language support.

Read more about these in “Amazon Comprehend features”.

There is extensive documentation for Amazon Comprehend, e.g.:

  • Amazon Comprehend developer guide;
  • SDK documentation.

There are Amazon Comprehend SDKs in all popular languages, e.g., Java, Python, PHP, JavaScript, Ruby, .Net, and Go. You can also access videos that explain how to use Amazon Comprehend. Visit “Amazon Comprehend developer resources” to access all of this documentation, moreover, you can install the SDK of your choice from here.

If you have more questions, you can check out the “Amazon Comprehend FAQs”. The pricing for Amazon Comprehend depends on the features used and resource consumption, and you can view “Amazon Comprehend pricing” for more information.

8. Sign-up for a test automation aid

The proposed web app should work with all browsers, therefore, you need to test it against different browsers and multiple versions of them. It‘s not easy with open-source test automation framework, however, Experitest provides a robust solution for this. You can use the Mobile device & browser lab from Experitest, which offers a wide range of browsers to test with.

Test reports and analytics are important for effective testing. Experitest offers its SeeTest Reporter, which offers excellent test reports and analytics. I recommend that you use it.

9. Use an effective project management tool

I recommend that you use the scrum technique to manage this project since it‘s a proven technique to manage Agile projects. You should build scrum teams. These are small, cross-functional teams where developers and testers work together.

Your PM should perform the scrum master role, and the team should work on sprints, i.e., iterations. There are various activities for effectively managing a scrum team, e.g., sprint planning, daily stand-up meetings, sprint review meetings, and sprint retrospective meetings.

You can read more about scrum in “How to build a scrum development team?”.  I recommend that you use a robust PM tool to manage this project. Asana is a good choice.

10. Developing the web app

Node.js is a popular choice for developing web apps. This open-source runtime environment facilitates creating performant and scalable web apps, and it has a vibrant developer community. I recommend that you use it to code the web app.

You can use IntelliJ IDEA for coding the app, which is a popular integrated development environment (IDE). This requires you to use a Node.js plugin, and you can access it in “Node.js and NPM”.

Developing this web app requires the following steps: