Seven Main Steps to Follow for Machine Learning Project Development

Machine learning offers abundant business opportunities. A properly-built ML solution can benefit your business in a wide range of areas. For example, with machine learning on your side, you will be able to adapt to changes faster, identify emerging trends in your business area, and provide your clients with top-level customer service.

As you have probably already guessed, the key word here is “properly-built.” To keep everything running like clockwork, you and your team will have to follow a detailed strategy. The information in this guide can be applied to basically any machine learning project, ranging from small-scale process automation to enterprise-level fraud detection. We will describe the most common processes used in creating machine learning projects that could help you achieve your goals.

Some machine learning stats to consider

Before we start discussing the necessary steps for ML projects, let’s go over some specific numbers just in case you’re still unsure about the benefits. These statistics will help you see how profitable machine learning can be and the way these investments pay off for businesses.

• In 2017, a machine learning algorithm for personalized recommendations helped Netflix save $1 billion. (Forbes)

• Amazon has reduced its “click to ship” time from 60 to 15 minutes thanks to machine learning. (Forbes)

• 60% of those surveyed in the pharmaceuticals industry by Statista stated that artificial intelligence improved their quality control. (Statista)

• At the same time, 58% of financial industry respondents stated that fraud detection is the biggest advantage they gain from AI. (Statista)

• 56.5% of marketing specialists use AI and ML for content personalization. (Deloitte)

• 85% of executives think machine learning will help their companies acquire and maintain a competitive advantage. (Think with Google)

• In 2019, machine learning projects received $42.9 billion in total investment, surpassing all other AI investments made that year. (Statista)

These statistics prove that machine learning can open up new opportunities for almost any kind of business. Next, let’s move on to the main points: What steps should you take to build a successful machine learning project?

Step #1: Setting up your strategy

Before jumping into the actual development process, you should start with the discovery stage. This is the step where you identify what problem you’re looking to solve with machine learning and the scope of work needed to achieve this (including aspects such as formulating a hypothesis and defining your data sources). The discovery stage is critical since it helps you and your team structure their work and documentation properly and keep clear goals in mind.

For example, perhaps your business focuses on financial services, and you want to upgrade your data security measures so that your team will be able to identify threats and frauds faster and deal with them more efficiently. In this case, machine learning can be used to detect and even predict possible threats to assist your team in solving issues.

In addition to clarifying your goals and organizing the work to be done, the discovery stage also helps you see if the data you have is good for the task. This in turn allows you to predict the most likely risks in advance and prepare for them.

Step #2: Collecting the data

Data is the fuel required for any machine learning project. When you have developed your strategy, defined your goals, and clarified the problem, you can start acquiring the necessary data.

Data can be acquired from a multitude of sources. The following are some examples.

• Your own data: The most obvious and quick-to-access source is your own database. In addition, it is the most relevant data for your specific business. To extract it, you can ask your data engineers for assistance or use the necessary tools yourself.

• Open-source databases: These resources will be helpful if you want to work on a problem common to a large number of businesses. A lot of services allow you to find and download the datasets required for your project. For example, Google can provide you with a searching tool for these datasets. There are also plenty of industry-specific databases available to the public such as Earthdata, CERN Open Data, and even the FBI's Crime Data Explorer.

• Paid databases: If publicly available datasets are insufficient or you cannot find the right data at hand, you can also consider paid databases. There is an abundance of these resources available.

At this stage, your goal is to accumulate as many relevant data samples as possible. This will make your machine learning process more precise and efficient. Also, be sure to ensure you are complying with all legal regulations regarding data usage and anonymity.

Step #3: Data preparation

Okay, now you’ve collected all the available data for your project. However, this initial data is usually disordered and messy, so the next step is preparing the data in a format your machine learning model can use to train itself. At this stage, there are several time-consuming but crucial tasks you should perform.

• Filter the data: You will need to refine your data so that only the highest-quality samples that are relevant to your business and market are included.

• Label the data: If your project is based on a supervised machine learning approach, you will have to manually label the historical data for your ML model to learn from. 

• Conduct the EDA: Exploratory Data Analysis (EDA) will help your engineers understand the data. This process will provide answers to questions like “What features are we going to input?” and “Are there any outliers that should be discussed with the domain expert?”

• Identify the unusable entries: If there are damaged or inaccurate samples, they must be deleted.

• Fill in the missing values: If some samples have missing values, these must be filled in. If there is no method for the restoration, the samples should be removed.

• Take a sample: You don’t have to provide your ML model with all the data you have at once. To speed up the exploration, you can extract a sample from the main dataset to start with. However, make sure that this sample is balanced. Otherwise, you are risking repeating the failure of the Amazon employment tool.

• Convert the data: The data must be converted into a suitable format for machine learning.

• Split the dataset: In addition to taking a sample, your team will need to divide your data into at least two subsets: the training data (for the actual training process) and the test set (for evaluating the model’s capabilities). Usually, these two subsets make up around 80% and 20% of the data, respectively.

The list may seem long and tedious, but these processes are essential for a successful ML model deployment. If you skip over or miss any of these, the risk of the model outputting incorrect results increases.

Step #4: Modeling

If data is the fuel, then the model is the engine. By this stage, your data should be completely prepared for your model to use. So, how do you build up this engine? Here are the most important steps in the modeling process.

• Experimenting

If you want your ML project to succeed, your team should have some room for experimenting. Creating several hypotheses and testing multiple algorithms is a mainstream practice in machine learning. This allows you to see beyond the limits of one solution and choose what will perform best in the end. 

• Model training

This is the primary process for creating an ML model. It consists of presenting the prepared training dataset to the machine learning algorithm and allowing it to carry out the processing. Afterward, the algorithm gives you a model that can find what you need in new data. Accordingly, your objective at this stage is to build the model itself.

The two most common ways to train a machine learning model are supervised and unsupervised learning. The first approach requires working with labeled data, as mentioned in the previous step. Supervised learning can be used for predictive analysis or solving classification problems. In the second option, the algorithm analyzes unlabeled data. The purpose here is to find out how the data elements are connected to each other and organize the objects. This type of learning can be used for dimensionality reduction or association rule learning.

• Model evaluation

When you have completed several simple models, you should evaluate them to decide which of them addresses your needs better. Setting up a series of metrics will help you make the right choice. The metrics will differ from project to project depending on your goals and the problems to be solved. For example, a classification model can be measured by precision or accuracy and a regression model will require the Mean Absolute Error (MAE) metric. Also, this is where your test dataset comes in handy.

Step #5: Fine-tuning

Even after you have prepared your shortlist of the most efficient models, each model can still be calibrated and enhanced. This can be done with the help of hyperparameters and fine-tuning methods like genetic algorithms, grid searches, and ensemble learning. All of these techniques are meant to improve the models’ results, and you can combine them to reach maximum efficiency. Also, note that all your models should be exposed to all the data you have to avoid overfitting.

After completing this process, you’ll have a fully-prepared model that is ready for the next step: deployment.

Step #6: Deployment

We are finally here. Your machine learning model is almost ready to see the light of the day. Why almost? Because there is some preparation work your team needs to complete before the actual release. For example, your data engineers should prepare the performance and optimization requirements for production. They should also suggest timelines for model retraining.

Now you can put your model into production. Your machine learning project is finally up and running at this point. Technically, this is the last step in your model’s life cycle. However, that doesn’t mean your work is done.

Step #7: Monitoring and maintenance

Any software project, whether it’s data-driven or not, requires monitoring once it’s launched. It may not seem as serious as the previous stages, but continuous post-launch maintenance can actually take the most time and money of all. New data can affect the model in all kinds of ways, so your team needs to keep an eye on how it functions. In addition, your model will need to grow and evolve to meet your changing business needs.

You can keep up with these environmental changes by retraining your model. When your team spots any issues with the way the model works, they should isolate the issue, retrain the model to correct it, and then deploy an update. Even if there is no obvious degradation in the model, it’s useful to retrain it from time to time to ensure it keeps up with the latest data.

The perfect team for a machine learning project

This article will always cover the kind of dedicated team and specialists you should consider for your machine learning project. Let’s take a look at the specific team members you will need to complete a model. Sometimes their responsibilities overlap, but there are still some differences.

• Data analyst

This specialist is involved in machine learning projects from the very beginning and works closely with business owners and managers. An analyst’s primary task is to gather insights from the existing data and help solve business problems. They are also responsible for visualizing insights and presenting them in an understandable way. Data analysts may specialize in various domains such as risks or marketing.

• Data scientist

Data scientists are frequently mentioned in the tech industry. Generally speaking, their duties are to collect, analyze, structure, and interpret data. You may wonder what makes them different from data analysts. Analysts work with existing data that usually comes from one source while scientists also build models and algorithms for ML projects and work with multiple possibly unrelated data sources.

• Data engineer

Data engineers are responsible for the data infrastructure. This means they create ways to collect, store, process, and distribute data. They also closely monitor how this infrastructure works and whether it needs optimization.

• Machine learning engineer

Their tasks can sometimes be similar to data scientists or data engineers, but machine learning engineers work more closely with the machine learning model itself. These specialists are responsible for deploying and monitoring the model as well as for working on updates and scaling.

• Business domain expert

This team member does not work with the model directly, but their professional opinion can be valuable for the project. In a nutshell, a business domain expert is a person with expertise in the specific business area that your ML project is focused on. You can actually be the domain expert for your own machine learning project, or you can hire a specialist to work with you if needed.

If you’re interested in hiring an experienced team of ML experts that will help you turn your idea into reality, feel free to drop us a line.

Conclusion

The strategy described above is common to almost every machine learning project. Your success is based not on the number of steps or the project’s scale, but on the way you and your team approach each step. While this can be time-consuming, paying more attention to the processes mentioned in this article will enable your project to achieve more precise final results.

If you are looking for a machine learning partner, feel free to contact our specialists. We have substantial experience in working with ML projects for various industries such as real estate and agriculture.