Machine Learning Development != Software Development
A few things to consider before moving from Code to Models
Although sharing the same goal, machine learning and software development differ significantly in processes and approaches. Machine learning code is designed to find a solution from data, while software development code is designed to implement a solution from logic.
In this article, we will explore some examples of how they differ.
1. Data Dependency
Unlike traditional software where the logic is explicitly defined, in ML, the model learns from data patterns and adjusts itself. In machine learning, the data used for training the model is crucial as the performance and accuracy of the model heavily rely on the quality and quantity of the data. Understanding and managing data dependency is critical in machine learning development. It involves not only collecting relevant and diverse data but also continuously monitoring and updating the data to ensure that the model remains effective in real-world scenarios. Data quality and data governance practices play a vital role in the success of machine learning projects.
Here are some key points to keep in mind on data dependency in machine learning:
1.1. Lack of Perfect Rules
In traditional software development, developers can explicitly define rules and logic based on their understanding of the problem. In contrast, the complexity of some problems in machine learning makes it difficult to create perfect rules manually. The model learns to generalize from the training data, and its effectiveness depends on the diversity and representativeness of that data.
1.2. Data Preprocessing
Before training a ML model, it is common to perform data preprocessing tasks such as cleaning, normalization, and feature engineering (a feature is an individual measurable property or characteristic of a phenomenon being observed). These steps are crucial for ensuring that the input data is in a suitable format for the model to learn meaningful patterns.
1.3. Quality and Quantity of Data
The performance and generalization ability of an ML model are heavily influenced by the quality and quantity of the training data. If the data is noisy, biased, or not representative of the real-world scenarios the model will encounter, the model’s predictions may be inaccurate, biased, or with low quality metrics.
2. Model Interpretability and Debugging
Model interpretability and debugging in machine learning refer to the challenges associated with understanding how a model makes predictions and identifying issues when the model behaves unexpectedly. Unlike traditional software where the logic is explicitly written, many machine learning models, especially complex ones, are challenging to interpret their decisions.
Here are some key points to keep in mind on model interpretability and debugging:
2.1. Bias and Fairness Concerns
Bias and fairness concerns in machine learning refer to the potential for models to exhibit biased behavior or to produce unfair outcomes. Understanding, detecting, and mitigating bias is a crucial aspect of responsible ML development.
Two of the main sources of bias include training data bias and feature bias.
If the training data used to train a machine learning model is biased, the model may learn and perpetuate those biases. For example, historical data might reflect societal biases and inequalities, leading the model to replicate and amplify those biases in its predictions.
Bias can also be introduced through the features used in the model. If certain features are correlated with sensitive attributes (such as gender, race, or age), the model may inadvertently learn to use these features to make biased predictions.
Detecting and mitigating biases is a crucial aspect of model interpretability and debugging. It involves analyzing the data used for training, assessing the fairness of predictions across different demographic groups, and taking corrective measures to address any disparities.
2.2. Black Box Models
Some machine learning models, such as deep neural networks, are often considered “black box” models. This means that understanding the internal workings and decision-making processes of the model is not straightforward. The model might have millions of parameters, and it can be difficult to trace how specific features contribute to the final prediction.
2.3. Interpretability Techniques
Interpreting models is beneficial for developers, data scientists, and business stakeholders within an organization, providing them with a thorough comprehension of their machine learning models. This capability is instrumental for debugging, elucidating predictions, and facilitating auditing to ensure compliance with regulatory standards.
Several techniques exist to make machine learning models more interpretable. These include feature importance analysis, partial dependence plots, and model-agnostic methods like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). These techniques help in understanding the impact of individual features on the model’s predictions. [link]
In the context of model interpretability, tools such as Microsoft’s InterpretML [link] and Google’s What-If Tool [link] are valuable resources for gaining insights into machine learning models. InterpretML is an open-source library developed by Microsoft that provides a set of tools for interpreting machine learning models. It supports several types of models, including tree-based models, linear models, and black-box models like deep neural networks. The What-If Tool is developed by Google and is part of the TensorFlow Extended (TFX) toolkit. It is designed to explore and understand the behavior of machine learning models, especially in the context of fairness and bias.
3. Algorithm Selection and Tuning
Unlike software development where the logic is well-defined, in ML development the selection of algorithms and fine-tuning these models is more of an art, requiring an understanding of the problem domain, and experimentation paired with careful evaluation to develop models that perform well on real-world tasks. Algorithm selection and tuning are critical steps in the machine learning workflow, and they involve choosing the right algorithm for a given task and optimizing its performance.
Let’s delve into these concepts in more detail:
3.1. Algorithm Selection
Algorithm selection involves choosing the most suitable machine learning algorithm for a specific task or problem. The choice of algorithm depends on the nature of the data (different algorithms perform better on certain types of data), the type of problem (e.g., classification, regression, natural language processing, computer vision), and model interpretability (some algorithms, like decision trees or linear models, are more interpretable, making them suitable for scenarios where understanding the model’s decision-making process is crucial).
Algorithm selection is often an iterative process. Trying multiple algorithms, evaluating their performance, and refining the choice based on results is common in machine learning development.
3.2. Hyperparameter Tuning
Hyperparameter tuning involves adjusting the hyperparameters of a chosen algorithm to optimize its performance. Hyperparameters are configuration settings external to the model that influence its learning process. Tools and libraries, such as scikit-learn’s GridSearchCV or automated machine learning (AutoML) platforms, can assist in automating the hyperparameter tuning process.
3.3. Overfitting and Underfitting
Overfitting and Underfitting are the two main problems that occur in machine learning and degrade the performance of the machine learning models. Overfitting occurs when a model performs well on the training data but poorly on new, unseen data. It often results from a model being too complex or having too many parameters. Underfitting occurs when a model is too simple to capture the underlying patterns in the data, leading to mediocre performance on both training and new data.
Conclusions
While this article is quite long it just touches the surface of how Machine learning development != software development. When deep diving into machine learning models’ development one will find out that it involves acquiring new skills, changing the thinking paradigm, understanding the nuances of working with data, and appreciating the iterative and experimental nature of developing models. Domain expertise in the specific problem being solved becomes crucial, and collaboration with data scientists and domain experts is often required for successful ML projects.
Are there any other distinctions between machine learning development and software development? Share your thoughts in the comments below!
