Use This Guidance to Help You Determine Which Subset of AI Your Project Requires
If you’ve just begun exploring artificial intelligence (AI), or you recognize that an AI solution will elevate your new projects for today’s tech marketplace, an initial understanding of machine learning and deep learning is critical.
A basic fluency with these two subsets of AI will help you:
Refine your project concept
Determine your project’s needs
Understand the depth of experience required for successful deployment
Present and future technologies will depend on AI. Widespread integration is already moving quickly, with a market size that will see robust growth by 2030. Your investment in understanding AI will bring you a step closer to bringing technologically relevant and desirable projects to market.
What Is Machine Learning?
Machine learning is the process by which a computer learns to make decisions. These decisions, typically in the form of predictions, are informed by data fed to it by AI developers. After the computer has been fed a sufficient amount of data, it will demonstrate its learning when processing data it has never seen before.
Drivers use a common example of machine learning every day: a rearview camera. If developers provide the machine learning model with enough data about objects a driver should avoid, such as other cars, humans, and major obstacles, the backup camera system, when faced with a new object, will trigger an alarm for the driver. The backup camera system has essentially “learned” what it should avoid.
What Is Deep Learning AI?
A backup camera may also be able to learn independently. When an AI model experiences patterns that it has not been initially trained on, but that cause a driver to have a collision or to suddenly brake, it could potentially understand the patterns related to these new obstacles to avoid future collisions. When AI learning becomes more complex and independent, and it starts showing human-like learning capabilities, developers define this as a second subset: deep learning AI.
Deep learning is a significant step forward from machine learning that requires a basic understanding of another AI concept: neural networks. A neural network consists of multiple layers that process and learn from data, with a similar structure to a human brain. The more layers, the more computational capability and complex learning that occurs as the model synthesizes a wider range of data and reacts to what it has learned.
A machine learning model also learns from data, but does not use neural networks. Traditional machine learning algorithms are effective for very specific, generalized tasks. However, deep learning achieves better performance on prediction and decision-making due to its learning capability using neural networks. Deep learning’s hidden layers make that learning more like intelligence than computing.
One characteristic that distinguishes human intelligence is our ability to make decisions based on multiple factors at once. To return to our backup camera example, humans are able to consider weather conditions, daylight, our speed in relation to another car’s speed, and the distance between our vehicle and another, as well as our experiences with successful and unsuccessful driving. With effective engineering and resources, a deep learning model could potentially help drivers identify the relative danger of accelerating a car in reverse under rainy weather conditions at night as a car slightly out of view approaches behind the vehicle at a high speed - and alert the driver accordingly. ”
However, we need to recall that deep learning is a subset of machine learning, rather than a separate branch. Although some engineers may wish to pit machine learning against AI or prove the superiority of generative AI over machine learning, remember that deep learning is only possible because of machine learning’s fundamentals.
Does Your Project Require AI Machine Learning or Deep Learning?
Summarizing the differences between machine learning and deep learning is best framed in the context of a particular project that you intend to develop. Start by conducting a needs assessment for your project’s AI requirements.
Traditional machine learning techniques are best used when:
The data structures you want to model are straightforward patterns or well-behaved data structures.
You only require modeling for a relatively small amount of data.
You wish to start your project with prototyping or proof of concept to demonstrate your use case.
Traditional machine learning algorithms already in existence will lead to a successful application, since they have been already tested over the years.
Your project does not require significant computational resources or the target system is resource-constrained
Deep learning models are best used when:
The solution must learn complex data sets, including significant audio and video data.
The application must teach itself complex information without human supervision.
You require your solution to generalize a wide variety of scenarios.
You have access to a considerable amount of data for training your models.
Your solution needs to resemble aspects of human intelligence, as opposed to computation.
No matter what form of AI development your project needs, engineering support exists to help bring your project to market, so your business and your customers can fully benefit from machine learning and deep learning.
What Is the Market Outlook for Machine Learning and Deep Learning Projects?
Corporate leaders, business development teams, and software developers are uniquely positioned to make an impact on their respective industries by incorporating AI machine learning and deep learning into their applications.
End-users of the projects they develop expect that the new product lines you create will effectively incorporate AI - or your competitor will. According to the Computing Technology Industry Association (CompTIA), technology companies continue their push toward developing AI applications for their products.
22% report they are in full pursuit of AI integration.
33% are implementing AI on a limited basis.
45% are exploring AI as a potential tool for their business.
62% of respondents intend to make a “moderate” to “significant” investment for future AI adoption.
The opportunities to lead the way with AI-based solutions are ripe for the taking. Therefore, whether you represent or design for a B2B or B2C company, your prospects of finding significant interest in AI products is highly favorable.
Review CompTIA’s 2024 report for more insights on widespread adoption of AI among tech companies to inform your decisions as you harness AI-based applications for your business’s benefit.
How Do You Find an Engineering Consulting Team for Your AI Machine Learning or Deep Learning Project?
Software developers and tech businesses alike may find that they require consultation services to implement AI. A partnership with a well-versed team provides the expertise you need to accomplish what you know is possible with this technology.
Consider using the following points as guidelines as you pursue a partnership with a machine learning and deep learning engineering team. The successful implementation of your AI-based solution depends on the strategic relationships you build.
A superior team for AI engineering support should provide:
A depth of experience with the latest tools and frameworks that have enabled AI, including systems-on-chip (SoCs).
In-house machine learning and deep learning development.
Dedicated support and concentration on your project.
Knowledge on selecting the right data set for your project.
Expertise with deep learning AI model training and integration.
A commitment to optimizing your AI-based solution.
Machine learning operations (MLOps) for the full lifecycle of your project.
An emerging portfolio of AI use cases.
The AI engineering and consulting services firm you choose should have robust experience, the resources to fully dedicate themselves to your project, and a demonstrated commitment to leveraging cutting-edge technology for your benefit.
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