Machine learning (ML) has become an increasingly popular tool for solving complex problems in various industries, from finance to healthcare to transportation. A successful machine learning project requires a structured approach, with each stage playing a critical role in the success of the project. In this blog post, we will be exploring the various stages involved in a machine learning project, from understanding the problem and collecting data, to model training and deployment. Whether you are a seasoned machine learning professional or just starting out, understanding the stages of a machine learning project is an essential part of delivering successful results.
In this stage, you should first understand the problem that you aim to solve using machine learning, and define the problem statement.
Data collection and exploration is the next step. Checking the quality of your data is important as bad quality data can negatively impact the performance of a machine learning model. Data quality investigation involves identifying missing values, incorrect or inconsistent data, and outliers.
After ensuring the quality of data, the next step is to understand the data by finding patterns and relations between variables. This includes visualizing the data and calculating summary statistics to get a sense of the data distribution.
Domain knowledge plays a crucial role in a machine learning project. It is important to involve domain experts who have a deep understanding of the problem to provide insights that may not be immediately apparent. They can also help identify any potential biases in the data and provide additional context.
This stage can also reveal the need for additional data sources. For example, if certain variables or data points are missing, additional data may need to be collected to ensure that the model is well-informed.
Data preprocessing involves changing the raw data into a format that is more suitable for analysis. This may involve converting data types, handling missing values, normalizing data, transforming skewed data, creating new features through aggregation, and so on. The goal is to clean and prepare the data for modeling.
Dimensionality reduction and feature selection are techniques used to select a subset of the available features in a dataset, reducing the number of variables used in a model. This helps to improve model performance and reduce the risk of overfitting. Dimensionality reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) can be used to reduce the number of features, while feature selection techniques such as Recursive Feature Elimination (RFE) can be used to select the most informative features.
The process of creating train/validation/test sets involves dividing the data into three distinct subsets. The training set is used to fit the model and train the parameters, the validation set is used to fine-tune the model, and the test set is used to evaluate the model’s performance. The validation set is critical in the model selection process and helps to ensure that the model generalizes well to new, unseen data. The test set provides an estimate of the model’s performance on new data. It is important to keep the data for each set independent and non-overlapping.
This is where you train and evaluate your models. We recommend to select a simple baseline model to quickly iterate on your training datasets. This means that based on the performance of your baseline model you can go one step back and try different approachs for data preprocessing as the features you select and how you transform them, plays an essential role in the model performance.
Model evaluation refers to the process of measuring the performance of the model on a new dataset. This is done using metrics such as accuracy, precision, recall, F1 score, etc. and should be relevant to the problem that you are solving.
Model selection is the process of choosing the best model based on predefined acceptance criteria. Acceptance criteria are the performance metrics that the model must meet to be considered suitable for deployment.
Now that you have selected the most effective model, you need to deploy it for inference. Inference refers to the process of using a trained model to make predictions or decisions on new data.
It is very important to monitor your model inference and the data input to ensure high performance of your model on a continuous basis. In production the model performance might change due to data drifts. This requires retraining the model so that it is up-to-date and continues to perform well.
For many ML applications, a user interface is needed in order for the user to interact with the model’s predictions. For example, for a model that detects suspicious behavior of pigs in a pig farm, a user dashboard with relevant data visualizations and overview of anomaly detections can help caregivers investigate a problem and take action.
Last but not least, for a machine learning solution to be successful, a cultural change in the organisation is required to ensure tha the new technology is trusted and embraced. People, after all, are driving change.
From data preparation to model deployment, each stage is important to the overall success of the project. Understanding the key activities involved in each stage, you can develop a machine learning solution that is effective and meets your business needs.
Original article on Medium