Delivering a successful machine learning project can be challenging. It often requires an expert in data science, statistical techniques, and collaboration with research, engineering, and product teams.
Implementing the proper machine learning process can make the difference between success and failure. This blog outlines the stages of a successful machine learning project. The stages include planning, data collection, model exploration, testing, and deployment.
This ML process will ensure you have a strong foundation for your machine learning process and model. Below, we’ll walk through how machine learning works and describe a common scenario for ML project implementation.
The most important step is to define the scope, prioritize features, and set up an expected timeline. To effectively utilize machine learning, first, identify the problem or business goal you are trying to solve. What specific objectives do you have for the project?
With a clear understanding, you can begin to explore how machine learning can be leveraged to assist in achieving them.
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One of the most common tradeoffs in an ML model is accuracy versus speed. Popular ML models prioritize speed and go with a model with a high validation score and a comparatively lower overhead.
It’s best interest to utilize this stage and set up a concrete problem statement along with the approach.
The next machine learning step is to gather comprehensive and relevant data. High-quality data is vital, and every data step is crucial to the success of your machine learning project. You should prioritize collecting, cleaning, sorting, labeling, and preparing data for analysis.
Machine learning pushes developers to use computer logic and drive value through applications. Since the cost of data labeling increases with the volume and scope of the project, it’s best to prioritize the features and correlations of your raw data based on the predicted outcomes.
According to this Cloud factory report [1] backed by analyst firm Cognilytica, almost 80% of an AI project’s time is spent on collecting, organizing, and labeling data.
Keep these stats in mind when allocating time to this stage.
ML Tip: The training dataset should be large enough to represent the data the model will encounter in production.
Data processing is collecting, cleaning, transforming, and analyzing data to extract meaningful insights. It is a critical step. Preparing data for model training involves several steps.
These steps include organizing the data, removing duplicates, and converting it into valid formats. The reason for these steps is that the data is often disorganized, redundant, or has missing parts.
Read More: Machine Learning Integration: A Guide for Business Leaders
Data visualization can be a very useful tool. Data visualization aims to explore the model, discover correlations between variables, and further understand the project's goal state.
With initial requirements in mind, today's ML models can easily be analyzed using graphs, charts, and quality checks to deem them fit before deployment. Regarding model exploration, the concept of data sufficiency draws the spotlight.
Before beginning, make sure to establish performance baselines. The goal here is to have a running system in place, and you can further build it out and perfect it during the next refining phase.
Typically, processed data includes labeled and unlabeled datasets, so reviewing existing documentation and literature surrounding similar models and doing a rudimentary gap analysis before proceeding is important.
For an application-based project, plotting data-oriented graphs can help you visualize correlations otherwise lost within bulky tables. Using simple bar graphs and pie charts, it becomes much easier to plot data points and explore the model, including any outliers.
Below are some of the most common, including:
They can show how data changes over time, compare different data groups, and identify relationships between variables.
They are used to visualize geospatial data, such as the location of customers, the spread of a disease, or the distribution of a product.
Used for visualizing hierarchical data, such as a company's organization, plant or animal taxonomy, or a machine learning model structure.
Used to visualize relationships between entities like social networks, the internet, or transportation systems.
In addition to these general categories, many other types of data visualization can be used for specific purposes.
Because every machine learning project is designed for the future, make sure that some aspects of your dataset meet those standards. Consider the data's quality, ability to handle growth, and how complicated it is. Before you start testing, take time to improve and fix any issues in the project to fit your requirements better.
Data underfitting and overfitting is another issue you should address by performing an error analysis, adding or reducing regularization, and tuning the hyperparameters.
It’s no surprise that your ML model’s architecture is fundamentally one of the most important aspects that you should be looking to perfect, so refining steps like hyperparameter tuning is ideal. [2]
Debugging your model significantly improves performance, making it a great way to uncover failure modes and adjust for outlier data. Refining the project also ensures your model’s predictions are accurate.
With this step, you should thoroughly evaluate the trained model under real-life conditions to validate model quality. Monitoring your project’s production readiness can help you double-check offline and online metrics correlations.
In the following machine learning guide, [3] Jeremy Jordan lists 5 subdivisions for which you should have a versioning system in place.
Splitting your data into training and testing sets allows you to predict your model’s accuracy and check if your model is scalable, repeatable, and upgradable. There are various tests you can run based on the nature of the outcome and the feature priority list you created during the planning phase.
Keep in mind that the training process can take some time, depending on the size and complexity of your dataset.
After training your model, it is important to assess its performance using a separate test set. This will help you gauge how well your model can handle new data that it has not been exposed to before. If the model's performance is unsatisfactory, you may have to consider retraining it with more data or experimenting with a different algorithm.
For example, a hospital is working on a machine learning model that can diagnose diseases. They plan to evaluate the model's performance by using specific metrics to compare it to human doctors. If the model's performance falls short of human doctors, the hospital can improve it by providing more training data or switching to a different algorithm.
It is crucial to evaluate the machine learning model to ensure good performance and generalization to new data.
After you have tested your model and are happy with its performance, you can deploy it to production. This may require integration with a software application or making it available as a web service.
When you're putting your ML model into action, it's smart to try a 'shadow mode.' Instead of asking, 'Does my model work?' you should also ask, 'Does my model work really well?.' This can help you fine-tune it better with future upgrades in mind.
How to use machine learning can be a complex process that requires careful planning and execution. The different stages include data collection and labeling, model exploration, model refinement, testing, and deployment. Using high-quality data and the right tools and frameworks is important throughout the process.
With the right framework, a dedicated team of developers, a thoughtful project roadmap, and specific mitigation strategies, you can extract the most potential out of your machine learning model.
[1] CloudFactory (n.d.). Data Labeling Guide for Machine Learning. [online] www.cloudfactory.com. Available at: https://www.cloudfactory.com/reports/data-engineering-preparation-labeling-for-ai
[2] Jordan, J. (2017). Hyperparameter tuning for machine learning models. [online] Jeremy Jordan. Available at: https://www.jeremyjordan.me/hyperparameter-tuning/.
[3] Jordan, J. (2018). Organizing machine learning projects: project management guidelines. [online] Jeremy Jordan. Available at: https://www.jeremyjordan.me/ml-projects-guide/
