Machine Learning or Deep Learning… Or A Blend of Both?

Two startup founders, Daniel and Emma, struggle to find the right AI approach for their business.

Emma wants a real estate app that predicts house prices based on locality and future market trends. She has detailed spreadsheets for house listings and sale prices but requires a system to predict prices based on data.

On the other hand, Daniel wants to build a yoga app that analyses the real-time yoga poses to provide immediate feedback. He has a vast collection of yoga session videos but needs a system to recognize each pose.

Both of their needs? Different.

For Emma, the machine learning approach is ideal, whereas for Daniel, the deep learning approach is the right choice. Why?

To understand this, let’s first look at what makes Machine Learning different from Deep Learning, and which AI approach is the ‘right choice’ for your product.

Machine Learning vs Deep Learning: The Subtle Difference That Matters

Both of these terms fall under the umbrella of AI, but they are not the same. Understanding the difference between machine learning and deep learning is essential to make the right technical choices.

But what difference does it make? Let’s find out:

Machine Learning

Machine Learning is a system that runs on a set of defined algorithms. These systems learn from data without any explicit instructions, which makes them ideal for business models that aim to detect patterns, make data-driven predictions, and spot abnormalities.

Machine learning relies heavily on structured data and manual engineering. It’s easy to set up, and the most common use case of machine learning can be seen in facial recognition systems used by organisations to identify employees.

These systems automatically evaluate facial features and compare them to the stored database.

The Key Features of Machine Learning:

• Low Processing Cost
• Manual Engineering
• Small Datasets Availability

Deep Learning

Deep Learning is a subset of Machine Learning. It uses neural networks to identify patterns and specific features automatically. These models can effectively work with raw and unstructured data.

Unlike traditional ML systems that rely on manual engineering, DL models can learn complex relationships across different datasets, without any human intervention. The Key Features of Deep Learning:

• High Processing Cost
• Automated Learning
• Large Datasets Availability

Choosing Between Machine Learning and Deep Learning for Your Business

Choosing the right model depends on problem complexity, data availability, and your business-user needs. This is where expert AI development services can guide your decision-making process.

Problems that follow a clear path should be run on ML, whereas problems with abstract relationships require deeper analysis and should run on DL.

When to Choose Machine Learning?

Let’s go back to Emma’s real estate app, her data was limited and structured in a spreadsheet. Her goal was to build a reliable model for price prediction.

For Emma’s business model, the machine learning model is the ideal choice. Machine learning works well on well-structured tabular data. If the budget is limited, ML offers efficient solutions without any hardware costs.

Choose Machine Learning when you have:

• Budget constraints
• Small to medium-sized datasets
• Limited Computing resources

When to Choose Deep Learning?

Remember when we chose the deep learning model for Daniel? Daniel’s yoga app requirements were real-time feedback for yoga poses.

He has a collection of yoga session videos and requires a model that can understand movements, identify body parts and differentiate between asanas.

Deep Learning can run on massive datasets that would be impractical to process manually. It can automate feature extraction, eliminating the need for manual feature engineering. This makes it particularly useful for applications requiring high accuracy, such as image recognition.

Choose Deep Learning when you:

• Require feature extraction from raw data
• Have access to significant computational resources
• Require human-like perception or pattern recognition.

Choosing the right AI approach for your product can be a toughie, but with the right AI development services, you’ll easily get there.

When to Combine Machine Learning with Deep Learning?

Let’s say you want to build an app for investing in cryptocurrencies, machine learning can track transaction patterns for fraud detection, while deep learning can predict consumer behavior through unstructured data sources like emails and calls.

Hybrid models are efficient for business models with readily available structured and unstructured datasets. This reduces computational costs while maintaining high performance.

A hybrid model like this requires careful architecture and domain expertise, something the best AI development company, like Infutrix, is well equipped to handle.

Conclusion

Selecting the right approach requires balancing accuracy, scalability, and cost effectiveness

If your data is structured and your goal is speed and simplicity, machine learning is the way to go. But if your challenge involves images, video, natural language, or unstructured data, use deep learning for powerful results.

At Infutrix, a leading AI software development services provider, we understand the strengths and trade-offs of both approaches.

Whether you’re a business founder like Daniel or Emma, trying to decide between machine learning and deep learning for your startup, you know where to find us.

For more resources check Infutrix Insights.