7 Steps of Machine Learning Project: A Complete Guide

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 keyword 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 any machine learning project, ranging from small-scale process automation to enterprise-level fraud detection. In our article, we will describe the most common processes and provide all the ins and outs of a step-by-step machine learning project that could help you achieve your goals.

What Is Machine Learning?

Machine learning (ML) is like teaching a computer to learn from experience, just like humans do. By analyzing data, it gets better at tasks like:

• Predicting things: Movie recommendations, stock prices, or even the weather!

• Organizing information: Grouping emails as spam or sorting photos.

• Discovering patterns: Finding trends in sales data or medical records.

• Creating new things: Imagine writing poems or painting pictures with machines!

 It's everywhere, from online shopping to self-driving cars, helping us solve complex problems and make better decisions.

The 7 Steps Of Machine Learning Project

Now, let’s delve into the 7 basic steps of machine learning project you’ve to take into account whenever you are going to use machine learning for your business. Surely, you’ll rock it after.

Problem Definition

The first stage in the DDS Machine Learning Framework is to define and understand the problem that someone is going to solve. Start by analyzing the goals and the why behind a particular problem statement. Understand the power of data and how one can use it to make a change and drive results. Asking the relevant questions is always a great start.

A few possible questions:

• What is the business?​

• Why does the problem need to be solved?

• Is a traditional solution available to solve the problem?

• If probabilistic, then does available data allow to model it?

• What is a measurable business goal?

Data Collection

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 collection can proceed from a multitude of sources. The following are some examples. 

• Your data: The most obvious and quick-to-access source is your 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 basic engineering 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. As an example, Google can provide you with a search 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.

Data Preparation

Okay, now you’ve collected all the available data for your project. Yet, 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.

Model Selection

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.

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.

Supervised and unsupervised learning are the two most common ways to train a machine learning model. 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 determine how the data elements are connected and organize the objects. This type of learning can be used for dimensionality reduction or association rule learning.

Model Evaluation

Once you've produced multiple basic models, you should analyze them to see which one best meets your goals. Setting up a variety of measures can help you make the best decision. The measurements will vary from project to project, based on your objectives and the issues to be addressed. A classification mode may be quantified in terms of precision or accuracy, but a regression model requires the Mean Absolute Error (MAE) metric. 

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.

Model 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.

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 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. Additionaly, 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.

Machine Learning Tools

Machine learning tools are your playground for building smart systems that learn and grow over time. It enables computers to not only automate data processing but also to "learn" from experiences and context rather than basic code — much like people learn.

As we know, ML, like any AI-powered system, requires algorithms to serve as a form of guide for the system, which is produced using machine learning tools and software. A machine learning model is taught using an algorithm to spot patterns and make predictions. And when fresh data is put into these algorithms, they learn and improve, gradually creating intelligence.

There are hundreds of algorithms that computers may use depending on characteristics like data quantity and diversity, but they can be divided into four categories based on the amount of human contact required to evaluate their accuracy over time. 

So here's the next question! What actions should be taken to accomplish all of the steps in machine learning and choosing appropriate tools? When selecting a machine learning tool, you should consider your requirements; what you want your machine learning model to do, and what adjustments are required throughout development. Not all tools are the same; some may excel in training models in a specific area of machine learning, like deep learning or data science.

But let’s take into account that choosing the right too may be challenging. Thus, we have prepared some considerations you will surely need to pay attention to:

• Decide if you want supervised, unsupervised, or both for your model's training. Choose a tool that supports your chosen approach.

• Consider the specific requirements of your model, input types, desired outputs, and potential complexity. Ensure the tool can handle these needs.

• Think about how your data will be analyzed and scaled. Will it be processed on hardware, software, or in the cloud? Choose a tool compatible with your chosen processing platform.

• Remember, there's no magic bullet in machine learning. Different tools excel in different areas.

Finally, to better understand the tools, you can have a look at the most popular ones:

• Colab: Google's Colab, short for Collaboratory, is a cloud service that assists developers in creating machine learning applications utilizing the PyTorch, TensorFlow, Keras, and OpenCV libraries.

• Azure Machine Learning: Provides developers with everything they need to build, test, and deploy machine learning models, with a focus on security. 

• Openn: OpenNN is a software library that implements neural networks, which are an important field of deep machine learning research. It is built in C++, and the full library is available for free download on GitHub or SourceForge.

Why choose Yellow for Machine Learning Projects

Yellow boasts a team with years of experience and expertise in machine learning. Our knowledgeable team can guide you through every step of your project, from conception to implementation. We understand that every machine-learning project is unique. So, our experts will work closely with you to understand your specific goals and requirements, offering tailored solutions to meet your needs effectively.

Our team constantly stays up-to-date with the latest advancements in machine learning technology. By leveraging cutting-edge tools and techniques, we can deliver innovative solutions that give you a competitive edge.

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.

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