machine-learning-steps-explained-for-business
Machine Learning

6 Machine Learning Steps Explained for the Business

Imagine where you could take your business if you knew the next product a customer will buy or if a transaction is fraudulent. This is what Machine Learning (ML) promises.

Machine Learning allows businesses to challenge the status quo, creating tremendous disruption. Already, many companies are using Machine Learning to develop new business opportunities.

Are you determined to bring Machine Learning into your organization? To get better business results.

First, you must understand what Machine Learning is and how it can help you. Then you will need business processes to evaluate and realize Machine Learning projects.

In this article, we will show you the 6 Steps of Machine Learning explained for the business. Here is the table of contents. Feel free to jump to any section that interests you:

What is Machine Learning from a Business Perspective?

Machine Learning is a technology that uses algorithms and statistics to find patterns in massive amounts of data. Knowing these patterns allows you to predict future behaviors.

Until now, if you wanted to predict these future behaviors, you had to write an algorithm into the computer to do the task. For example, to flag possible fraudulent transactions for further inspection, you could write an algorithm that detects operations coming from unusual locations.

With Machine Learning, first, you would feed the computer some transactions. Then, you would test and tune different know Machine Learning algorithms to find the one that makes better fraud predictions. Ultimately, you get a software component that tells you when a transaction is fraudulent.

By using Machine Learning, computer systems can learn and improve. The implications are staggering.

There are three types of Machine Learning:

  • Supervised learning: It means labeling the training data fed to the machine. For example, you can mark transactions as fraudulent or not fraudulent. In this way, the computer learns which conditions lead to each outcome.
  • Unsupervised learning: The data is not labeled. The machine reads the data and looks for whatever patterns it can find.
  • Reinforcement learning: The machine tries out different things. The aim is to reward or penalize it depending on whether it reaches an objective. As a result, the computer learns what to do to get the desired outcome.

The Machine Learning Process Flow in a Nutshell

The Machine Learning process is simple: First, you find patterns in your business data. Then, you apply the model to check the subsequent transactions and take the desired action.

However, as we will see, even though the process is simple, the tasks involved and the skills required are not.

To establish a business process and bring Machine Learning to your organization, you will need the following:

  • Guidelines to determine if Machine Learning can solve a given problem.
  • Database specialists to find and extract the data from your databases. Also, you will need procedures to check data quality and find missing values.
  • Data Scientists test different Machine Learning models and find the best ones to solve specific problems.
  • Machine Learning engineers turn these models into software solutions. Then, integrate them with other systems and business processes in your organization.

Although Data Science and Machine Learning are related fields, they are different. Check out our post about Data Science vs AI.

To use Machine Learning, you must choose between diferent algorithms and statistical techniques. These techniques include classification, regression, clustering, dimensionality reduction, and others.

Your new team will need to take each business problem and work it through the stages of your workflow. The diagram below shows a typical Machine Learning process:

machine-learning-steps-process-flow

These steps are iterative; at crucial points in the process flow, you will need to revisit previous stages and review them.

For example: While evaluating the results of your prediction service, you may go back to model development to make changes.

We will describe each step of the Machine Learning process flow in detail.

6 Steps of Machine Learning

1.- Define the Problem

Gathering Requirements

The Machine Learning workflow begins when the stakeholders approach you with a challenge or problem they want to solve.

What you need to do first is to gather their requirements. The process is not very different from what you would do in your typical IT Project.

Ask stakeholders what challenges they are facing and what is the expected outcome. For example:

  • The business problem they need to solve
  • What is causing it?
  • The impact on the organization.
  • What is the desired outcome?
  • How would they define the success of the project?
  • How do they intend to use the solution?

Determine If Machine Learning Is the Best Solution

It would be best not to take Machine Learning as a silver bullet to solve every problem.

After you have a clear understanding of the problem, consider the following to determine if Machine Learning is the best solution:

  • The problem is persistent, not a temporary challenge.
  • Manually doing the task is not cost-effective.
  • When developing a computer program (coded solution) is difficult. For example, when there are too many branching business rules involved.
  • You can develop a coded solution, but scaling would be complex.
  • The prediction output depends on each case (You need to personalize it).
  • The prediction function could change over time.

Another thing to consider is data availability: Do you have enough data? If you do not, is it challenging to get or create it?

Formalize the Machine Learning Problem

Formalizing the problem is a job for the Data Scientists you have committed to your project. It means reducing it to a well-known Machine Learning problem. For example:

  • Supervised Learning:
    • Classification.
    • Regression.
  • Unsupervised Learning:
    • Clustering.
    • Anomaly detection.
  • Reinforcement Learning:
    • The criterion of optimality.
    • Brute force.

Note: These are just broad categories. You can further classify Machine Learning problems into more subtypes.

You need to answer questions, such as whether it is a supervised learning problem or a classification or a regression problem.

A well-defined problem is as critical for the project’s success as solving the problem itself.

2.- Collect and Prepare Data

Data collection and preparation is an essential part of a Machine Learning Solution.

In most projects, data collection and preparation represent 80% of the effort. This step requires a great deal of human intervention.

There is no absolute about how much data each particular project needs.
Collection and preparation involve three sub-steps: Sourcing, cleaning and preparing data.

Sourcing data involves:

  • Find the source of all the data necessary for developing the model.
  • Joining multiple data sources and rationalizing them into one dataset.
  • Identify features. Features is another name for the attributes of your data.
  • Analyze data to look for trends.

Cleaning Data

Databases are prone to have missing or incorrect values. The more time the system has been in service, the worse the problem can be. It is advisable to run quality tests to find missing and inconsistent values.

If you find missing values, there are some actions you can take to clean your data, for example:

  • Going back to the data source and input the missing values.
  • Remove rows with missing values.
  • Replace the missing value with another.
  • If the missing values are numerical, replace them with the mean or median of the dataset.

Preparing Data

Preparing data involves transforming it into the format best suited for the Machine Learning model.

Tools like Tensorflow and Python have libraries for data preprocessing.

Some examples of data preparation are:

  • Normalizing numerical data to a standard scale.
  • Changing data formats.
  • Simplify redundant data.
  • Randomize the order of data rows to ensure that data order doesn’t affect the model.

Preparing data also involves splitting the data into two subsets. These are the training data and model evaluation data. We don’t want to use the same data used to develop the model to test it.

Data mining specialists have the job of extracting and joining sources of data. Also, Data Scientists are responsible for making decisions on data cleaning, preparation, and normalization.

3.- Develop a Machine Learning Model

In this step, you develop and train your Machine Learning model:

  • It means choosing an established Machine Learning technique or defining a new approach.
  • Python and Tensorflow have extensive libraries with many ML algorithms you can use.
  • ML Cloud services, like those provided by Microsoft or Amazon, make choosing and using an ML model even more effortless.
  • You can always develop a new model. However, you often will frame your ML problem into a known one (See step one).

Once you choose a model, you will begin training it. In this step, we use our data to develop our model’s ability to make predictions.

For example, training a supervised learning model would look like this:

  • You already know the target values you are trying to predict. For instance, if predicting fraudulent transactions, you already know which ones are fraudulent.
  • Run the model over your training data to predict those values for your training data.
  • Try different model parameters until the prediction error is below a certain threshold.

Data Scientists are in charge of developing the Machine Learning model. The process is one of experimentation and incremental change. You should expect to spend time refining and modifying your model to get the best results.

At the end of this step, you will get a “Learner.” A learner is a Machine Learning algorithm trained over some data and adjusted to fit it as best as possible.

4.- Test and Refine The Machine Learning Model

Every learner is at risk of becoming biased. A biased Learner will make great predictions over the training dataset. However, give it a different dataset, and it will underperform.

Testing the learner with a separate dataset is necessary. Here, the “model evaluation” dataset we set aside in Step 2 comes into play.

Consider the following when testing and evaluating the learner:

  • We can determine if it will perform well in the real world by feeding the model data it has not yet seen.
  • We work on testing data like we did when training the model. We feed it to the model, then compare its predictions with actual values. As always, we seek an error below a predefined threshold.
  • During testing, we can change operations and settings (hyperparameter tuning).

After testing, you may need to return to model development to change training parameters. As we mentioned in Step 3, Machine Learning involves much experimentation. Many iterations between model development and testing may be necessary.

It may even be necessary to go back to Step 2 to change the dataset structure and Step 1 to change the model.

Once you have tested the Machine Learning model, you move on to production deployment.

5.- Deploy Machine Learning Prediction Service

Deploying the model is a task performed by Machine Learning Engineers. Their job is to turn the ML model into a software component that can receive requests from other applications.

  • When developing the prediction service, consider the many channels and audiences required. Determine the needs of all software applications that will interact with the service.
  • In some projects, you could recommend a limited rollout. This trial period will allow business users to work and provide feedback. After the trial, you can deploy the prediction service to a broader audience.
  • Machine Learning Engineers are experts in the tools to create prediction services. Hire a team of them, and have them develop the best solution that fits your needs.
  • Production and training data distribution must be similar. Otherwise, the model will make unreliable predictions.

The Cloud as an Option for Prediction Service Hosting

Companies like Microsoft, Google and Amazon have democratized access to Machine Learning resources on the cloud. They provide the tools to upload and use ML models as prediction services.

Usually, these software providers charge by the number of transactions you send. As a result, optimizing your applications is essential to avoid making redundant requests.

6.- Manage Prediction Service

Once your prediction service is online, performance monitoring is paramount. You must have the means to compare predictions with actual outcomes over time. If deviations occur, you need to retrain the model with new data.

Production data distribution may constantly change, making constant model retraining necessary. You might need to schedule monthly, weekly and even daily retrains.

By keeping track of prediction successes and failures, you gain valuable insights. Constant feedback can help you a great deal to fine-tune parameters for subsequent versions.

For better step execution, use software tools for service management and version control.

The Cloud as an Option for Prediction Service Hosting

In this article, we have reviewed 6 Machine Learning Steps that give you a great framework to establish a workflow in your organization.

To set this workflow in place, you will need professionals from different disciplines: Data Scientists, Machine Learning Engineers, Data Mining Specialists, domain experts from the business and more.

Check out this blog post for some examples of data mining in real life.

Use the 6 Machine Learning steps to set a common language. Start conversations in your company about what business processes, organizational structures and roles you need to realize the potential of Machine Learning.

What are the Machine Learning Steps in your company? Would you add any steps or sub-steps to this article’s 6 Steps of Machine Learning? Leave your thoughts in the comments section.

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References

Chandrakesan, A. Machine Learning Process Flow Made Easy — 6 Jars of ML.

Google Cloud Docs. Machine Learning Workflow.

Hao, K. What is machine learning? We drew you another flowchart. Published in the MIT Technology Review.

Information Age. 7 Steps for a successful AI implementation.

Sanjeevi, M. Different types of Machine learning and their types.

Yufeng G. 7 Steps of Machine Learning. Published in Towards Data Science.

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