Machine Learning and Deep Learning: What to Consider Before Applying Them

Not every data problem is an invitation for Deep Learning. And not every Machine Learning model is easy to sustain at scale. This is because Machine Learning (ML) and Deep Learning (DL) have structural differences that go beyond the algorithm. Therefore, deciding between one approach or another has direct implications for the project architecture, data demand, maintenance effort, and, most importantly, the viability of the result for the business. A study published by MIT Technology Review, in partnership with Databricks, revealed that 87% of AI (artificial intelligence) projects never leave the pilot phase. In many of these cases, the problem is not the technology itself, but the misalignment between the complexity of the chosen solution and the real challenge it sought to solve.

Data from October 21, 2025, 10-minute read. By: Skyone

1. Introduction: A technical choice that became a business decision

Not every data problem is an invitation for Deep Learning Machine Learning model is easy to sustain at scale. This is because Machine Learning (ML) and Deep Learning (DL) have structural differences that go beyond the algorithm. Therefore, deciding between one approach or another has direct implications for the project architecture, data demand, maintenance effort, and, most importantly, the viability of the result for the business.

A study published by MIT Technology Review , in partnership with Databricks , revealed that 87% of AI (artificial intelligence) projects never leave the pilot phase . In many of these cases, the problem is not the technology itself, but the misalignment between the complexity of the chosen solution and the real challenge it sought to solve.

This is where the choice between ML and DL ceases to be merely technical and becomes strategic. It requires clarity about the context, the available data, the maturity of the operation, and the company's objectives. After all, AI cannot be sustained solely through innovation: it needs to solve real problems efficiently and sustainably over time.

In this article, we provide a straightforward analysis of the practical differences between Machine Learning and Deep Learning , and why this distinction makes all the difference in the success of an AI initiative.

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2. What changes in practice between Machine Learning (ML) and Deep Learning (DL)

Machine Learning (ML) and Deep Learning (DL) share the conceptual basis of artificial intelligence, but they function very differently in practice, impacting everything from modeling to operation .

Machine learning (ML) works with algorithms that learn from organized data , typically structured in columns and well-defined variables. It's an approach that requires human intervention in the initial stages, such as choosing relevant features, and tends to have more predictable behavior over time.

DL, in turn, operates with deep neural networks that learn directly from raw, often unstructured data such as images, audio, or text. This autonomy allows for high levels of abstraction and precision, but it demands more: more data, more computing power, and more training time.

The infrastructure also changes : while ML can run in lighter, more distributed computing environments, DL demands robust architectures with intensive GPU usage and parallelism.

Another point is the transparency of the models . ML, by operating with simpler structures, tends to be more explainable. DL, on the other hand, delivers better performance in complex tasks, but is less interpretable, which can be a challenge in regulated environments or where the decision needs to be auditable.

These differences make it clear that ML and DL are different approaches, each with its own requirements, strengths, and technical limitations .

In the next section, we will understand how these differences translate into practical choice: when each approach tends to deliver more value, depending on the type of problem and the data available.

3. ML and DL: when each delivers better

The best way to choose between Machine Learning and Deep Learning is to start with the problem conditions , not the technology itself.

If the data is organized, with clear and well-defined variables, ML tends to be the more efficient choice. It works very well for tasks such as predictions, classifications, recommendations, and segmentations , especially when the model needs to be agile, easy to adjust, and simple to interpret.

DL , on the other hand, is more suitable when dealing with unstructured data (such as images, texts, or signals), and with problems that require the identification of more complex patterns . Its architecture allows learning with less human intervention, making it ideal in contexts of high variability and massive volumes of information.

It is also important to consider the available resources . ML requires less processing and offers results in shorter cycles. DL demands more computational power, more training time, and a team better prepared to handle its complexity.

The right choice depends on aligning these factors: data type, application objective, expected response time, and project sustainability capacity. This alignment is what determines whether AI will consistently generate value or get stuck along the way .

Next, we'll see how ML and DL can be combined in modern architectures, such as AI agents, which require different levels of intelligence working together.

4. How do ML and DL combine in AI agents?

AI agents are systems designed to make autonomous decisions based on different information sources, defined objectives, and constantly changing scenarios. To do this, they need to combine various types of intelligence. This is where Machine Learning and Deep Learning come together.

ML helps these agents identify patterns in structured data, predict behaviors , and adapt rules based on historical data. DL comes into play when the data is more complex: interpreting an email , understanding a conversation, classifying an image, or recognizing a pattern in natural language, for example.

These functions do not occur in isolation. In many cases, AI agents use ML to organize and filter information , and DL to better understand the context . The result is more precise and responsive performance, capable of connecting raw data to concrete decisions, even in scenarios with low predictability.

This integration between ML and DL requires a robust technological base capable of coordinating different models in an orchestrated way. This is what enables, for example, agents that combine traditional algorithms with generative AI , connected to corporate data sources.

In the next section, we'll see how this combined intelligence is already being applied in the day-to-day operations of companies. Stay tuned!

5. The concrete impact of ML and DL on companies today

Much of what we've discussed so far is already in operation in the day-to-day operations of companies , even if not always with visible labels. Machine Learning and Deep Learning have been increasingly applied to strategic and operational decisions, with a direct impact on efficiency , customer experience , and risk reduction .

In Retail , for example, ML plays a central role in recommendation systems, customer segmentation, and demand forecasting . DL, on the other hand, enables more precise virtual assistants , capable of interpreting questions in natural language and responding with context.

Financial sector , ML models monitor behavioral patterns in real time to prevent fraud and support credit decisions. DL, in turn, is already used in more complex tasks, such as contract analysis or anomaly detection in communications .

In Industry and Logistics , ML assists in routines such as predictive maintenance and intelligent routing , while DL appears in the automation of visual inspections —a good example of how it expands the ability of machines to "see" scenarios previously limited to the human eye.

These applications show that ML and DL are not just technical concepts, but practical tools with real impact when applied judiciously and aligned with business

objectives So let's check out the trends that are reshaping this landscape, and what this means for companies that want to evolve intelligently.

6. Current trends shaping the use of ML and DL

The advancement of Machine Learning and Deep Learning in companies is less related to the arrival of new trends and more to the maturation of concrete uses. In the coming years, some transformations are already beginning to reshape how these technologies are applied in practice.

Next, we highlight four movements that deserve attention:

Autonomous agents ( agentic AI ) as a central trend.

Gartner 's Top Strategic Technology Trends for 2025 report identifies agentic AI , and adapt to objectives with less human intervention.

Governance, security, and transparency become prerequisites

More powerful ML and DL models imply greater risks (of bias, error, misuse), and therefore, empowering organizations to audit, monitor, and explain models becomes as important as training them. Gartner also emphasizes governance platforms as a strategic trend in 2025.

AI infrastructure is no longer a luxury, it's becoming critical infrastructure

According to ITPro , global investment in AI infrastructure, such as GPU-enabled servers and optimized architectures, is expected to exceed $2 trillion in the coming years. This shows that ML and DL depend not only on the model, but also on the technical foundation that supports it. Without this, not even the best algorithm can handle production or scale.

Specialized models for specific sectors are growing in importance (vertical integration)

Consulting firms like McKinsey already indicate that the greatest gains from AI come from models tailored to specific domains (such as Healthcare, Finance, or Manufacturing), where ML and DL are "tuned" to handle business particularities, regulatory constraints, and industry-specific datasets.

At Skyone , all of this is no longer just a possibility: it's already part of our development. With Skyone Studio , we offer a platform where companies can orchestrate ML and DL in an integrated, processed, secure, and scalable , connecting everything from corporate data to AI agents that operate autonomously to solve real-world cases.

If you want to understand how these trends can be concretely applied to your business, talk to a Skyone specialist ! Together we can design an AI strategy, with ML and/or DL, that connects to what your company needs, today and for the future.

7. Conclusion: Choosing the right balance between ML and DL is what makes AI viable and scalable

Technology alone delivers nothing. Machine Learning and Deep Learning are tools . Powerful, yes, but still tools. What transforms them into concrete impact is the conscious decision of how, when, and why to apply each approach.

AI maturity in companies comes not only from technical sophistication, but from the ability to choose precisely . This requires more than hype : it demands familiarity with the context, a practical business vision, and clarity about the limits and potential of each choice.

This awareness is what separates solutions that survive the pilot phase from those that become part of the company's engine.

Want to see more examples of where this shift is already happening? Expand your reading with another piece of content from our blog : Intelligent Operations: The Evolution of Industry 4.0 with Applied AI .