Understanding the difference between machine learning and deep learning is important because your choice affects the timeline, cost, explainability, and final performance. In this article, we will define all the approaches, note the main differences in their technology and provide the main attributes of decisions, as well as common instances of using them, and conclude with a simple checklist that will allow you to select the correct method for your next project.

Machine learning is a wide term that refers to a wide range of methods to learn structured features, including linear or logistic regression, decision trees and gradient boosting, which can frequently need meticulous feature engineering. Deep learning is a branch of machine learning that applies multi-layer neural networks to extract hierarchical features out of raw data. It can work with unstructured data such as images and text and does not typically require fewer labeled data and less computing resources.

Key technical differences: Data requirements

• Machine learning is effective with small to medium-sized data that is labeled when you are able to develop strong features out of your understanding of the field.  
• Deep learning typically requires a large amount of labeled data to achieve optimal performance, but transfer learning and pre-trained models can reduce the need to label data.  

Feature engineering and representation

• Machine learning is based on constructed features, which are a result of domain knowledge.  
• Representations are automatically learned by deep learning, and hierarchical features which humans may find difficult to create. 

Compute and infrastructure

• Machine learning models are less frequently cumbersome to train. They can be easily implemented on general-purpose processors, and this reduces the cost of infrastructure and accelerates iterations.  
• Deep learning typically has hardware requirements of reasonable training and serving durations; it might also have increased memory and storage of model outputs.  

Interpretability and compliance

• Such models of machine learning as decision trees and linear models are more interpretable and can be audited. This is relevant in areas such as health, finance, and regulated industries.  
• Deep learning models are typically regarded as less transparent. Deep learning should be applied in sensitive areas; teams need to consider additional validation, tools of explanation, and governance.  

When to choose machine learning

• There is limited labeled data and strong domain features are available to you.  
• Regulators, customers or other internal stakeholders require explainability and auditability.  
• Rapid prototyping and low-cost of operations are significant.  
• The issue primarily concerns structured or tabular data having definite predictive indicators.   

When to choose deep learning

• Your input data is not structured, such as images or audio, or raw text, and you must learn complicated patterns all the way through.  
• There are large labeled data sets or high-quality pre-trained models at your disposal.  
• The additional computing and engineering investment will be mostly warranted by the anticipated performance benefits.  
• Inference costs and latency could be reduced with investment by your team in production techniques.   

Practical deployment and maintenance considerations

• Machine learning processes aim at feature pipelines, cross-validation, and feature model serving. Monitoring observes changes in the distribution of features, and the accuracy of models.  
• Deep learning processes emphasize data augmentation, effective data pipelines, hyperparameter search and resource management. Problems in production are model size, optimization of inferences and periodically retraining with changing data.  

Representative real-world examples

• The applications of machine learning would be in credit risk scoring, demand forecasting through structural customer data, and predictive maintenance, which incorporates sensor statistics.  
• Applications of deep learning in medical image analysis to provide diagnostic assistance, large conversational systems based on natural language understanding, and automatic speech recognition systems based on raw audio are all examples of its use.  

Simple decision checklist

1. Establish success metrics: predictive performance, latency, interpretability, and total cost of ownership.  
2. Available data in the audit: determine the amount, quality and cost to label additional examples.  
3. Answer Prototype a robust baseline with the help of machine learning and domain features. Providing the data scale is not prohibitive, compare the results with a proof of concept with deep learning.  
4. Approximate the infrastructure and cost of operation, such as training software and optimization of inferences.  
5. Think about hybrid approaches: e.g. apply deep learning to generate representations and feed them into less capable machine learning models to make final predictions.  

Final Thoughts

Deep learning and machine learning are complementary tools. Prepare a definite statement of the problem and a solid starting point through machine learning techniques. Graduate to deep learning where the nature of the data, magnitude of labels, and the possibility of better performance is worth the incremental expense and complexity. By making the correct decision at the outset, risk is reduced, and measurable value is delivered in the shortest time possible.Ready to find out which approach suits your project and get a practical roadmap? Visit Geirelays to book a free strategy session.