Businesses have started implementing Artificial Intelligence at an alarming rate. There are many types of Business AI Applications including: Predictive Analytics, Chat Bots, Recommendation Systems, and Computer Vision. Businesses have a growing reliance upon algorithms that can adapt, learn, and improve with their experience. The advances in Artificial Intelligence are a result of two major technologies: Machine Learning and Deep Learning.
These two terms are often used interchangeably, but that is incorrect. They are two different approaches with their respective uniqueness, advantages, and complexities. Knowledge of the difference between Machine Learning and Deep Learning can allow an organization to make better informed and more realistic technology-defining decisions for the development of AI-powered Solutions.
This article will highlight the available definitions of Machine Learning, the available definitions of Deep Learning, the key differences between the two, and the importance of those differences to organizations looking to Artificial Intelligence and Deep Learning for their next digital projects.
Defining Machine Learning
Machine Learning is a subset of AI focused on the construction of algorithms that allow an AI to ‘learn’ from the data, rather than the programmer dictating a specific set of processing instructions. Rather than following a set of instructions, a model/algorithm will find and learn patterns encapsulated in the dataset to make a prediction or an informed decision.
Take a look at e-commerce websites, for example. They can implement ML models to predict customer needs, group similar products together, or recommend items they previously searched for based on activity. The system continuously learns and improves its predictive abilities as it gathers more information about the factors that encourage a customer to spend.
The majority of time, the structured data and its datasets containing defined inputs and outputs are available. From the raw information, data scientists create features, decide a fitting algorithm, such as a linear regression or decision tree, and train the system to identify specific relationships.
Training should be the focal point of companies providing ML development services. The result should be the construction of predictive applications that enhance automation and make smarter decisions. Machine learning systems can usually accomplish systems that moderate amounts. However, the higher the complexity of the problem, the more likely the system will be faced with clipped domains, such as images, audio, and text, which may involve more advanced systems.
In terms of deep learning
The term deep learning refers to the level of mechanization of some areas in machine learning. It consists of systems that are based on multilayer neural networks. The more systems that are added, the more features of a model are deployed to create layers that may be derived in more advanced ways.
Different from other types of Machine Learning, deep learning models perform feature extraction automatically. Instead of needing to manually choose which pieces of a dataset should be looked at, deep networks figure out which features to focus on automatically, and learn while training. This lets deep learning models outperform other types of models in image recognition, natural language understanding, and speech recognition.
In image analysis for instance, deep learning networks can determine, in their first layers, the basic building blocks of images such as shapes, edges, and shades, and then in their deeper layers, can determine complete images or scenes. In text understanding, i.e. in sentence translation, and in language modeling, recurrent and transformer-based architectures, such as LSTMs and BERT can determine the meaning of words in their context, and of their relationships in a sentence to derive meaningful results without the use of any hand crafted linguistic features.
In learning such complex abstractions, deep learning models require a lot of data and computational power. Companies with enough training data, and high performance computing infrastructure can leverage deep learning to build high accuracy and high performance adaptable AI systems which is a complex task on traditional systems.
Key Differences Between Machine Learning and Deep Learning
While both are subsets of artificial intelligence, machine learning and deep learning differ in important ways. Each of the methods has its strengths, weaknesses, and ideal use cases. Defining these allows companies to figure out what technology is the right fit for their objectives.
1. Data Dependency
Machine learning algorithms used to be okay with small data sets. While they would learn from the data provided, the more complex the problems became, the more the algorithms would plateau. Deep learning networks handle massive data sets, learning from the examples and data complexity.
Machine learning is practical for problems like fraud detection or customer segmentation, while deep learning is more suitable for applications like image classification, speech recognition and self-driving cars, which require lots of raw data.
2. Computational Requirements
Machine learning systems are less resource intensive, allowing them to be run on standard hardware. Deep learning on the other hand, needs more powerful hardware, like GPUs, to run.
Organizations struggle on which algorithms to choose based on the computational infrastructure they have. Luckily, in most cases, cloud services and powerful GPUs increasingly allow even small to medium sized organizations to deep learning.
3. Feature Engineering
In traditional ML, Feature Engineering determines the model performance. Data scientists must decide the influential variables, and how the data needs to be transformed to increase performance of the model. With deep learning, this is automated by the layered neural structure.
Each level of a deep neural network learns how to capture relationships in a more complicated way. This process becomes more automatic, making feature design less necessary. While this increases workflow simplicity, it adds to model complexity and interpretability challenges.
4. Training. Time and Interpretability
Machine learning models in general have more rapid training times as well as more easily interpretable results. This is because businesses can easily trace custom ML models to see how specific parameters drive predictions, enabling more transparent decision making.
On the other hand, deep learning networks have longer training times, require large-scale computation, and are more expensive, but they are also more accurate and performant when it comes to more complex tasks. Their black box nature makes them less explainable and is thus a concern in finance, healthcare, and other regulated industries where accountability is critical.
5. Problem Complexity
Machine learning is best suited for simple and more straightforward analytical tasks for structured or sometimes, semi-structured data. Where traditional ML falls short, deep learning shines when it comes to detecting more abstract or complex non-linear patterns in a given data set, especially in data related to images, textual description or audio.
6. Maintenance and Scalability
Compared to other ML solutions that often require continual feature engineering, the adjustment of parameters and other ML techniques as the data and problem evolve, deep learning systems require more computational resources to maintain and train on new data. Deep learning systems, on the other hand, more easily adapt to new data and evolution of the problem, but require a lot of computational resources to perform effectively.
The Impact of Machine Learning and Deep Learning on Business
The use of artificial intelligence is no longer only in the hands of tech companies and research organizations. Machine Learning and Deep Learning technologies have seeped into large and small businesses in almost every field. For example, modern businesses use ML and DL technologies to automate and analyze business processes, tasks, and data, as well as tailor offerings to individual preferences and promote efficiency.
With the help of machine learning, companies are able to analyze structured data (digitally stored information) such as sales transactions, customer information, and readings from various sensors to discover hidden information. Machine learning predictive analytics tools assist in the identification of problems and business opportunities (trends) and the optimization of marketing strategies.
In contrast, deep learning enables businesses to analyze and gain intelligence from unstructured data (data that does not have any fixed format) such as images, sounds, videos, and text. Deep learning models can be embedded in applications to automate quality control, customer service chats, and visual search.
With machine learning and deep learning businesses can have data intelligence for all their functions, from marketing to logistics.
Examples of Business Applications
To understand the differences between the use of these technologies, let’s look at the examples across various industries.
Retail and E-commerce
In the retail industry, businesses are using machine learning applications for real-time pricing, product recommendations, and predicting inventory availability. In addition, deep learning enables businesses to use computer vision to automatically classify and improve search results, and to create virtual fitting rooms.
Healthcare
Machine learning tools assist in analyzing medical data to determine important risk scores associated with patients. Deep learning models interpret X-rays, MRIs, and even genetic data to identify potential early-stage conditions that doctors may overlook.
Manufacturing
Predictive maintenance, utilizing machine learning, is aided through the analysis of data generated by machine sensors. Deep learning amplifies this field with real-time visual inspection systems that identify assembly line defects in products.
Finance
Tools such as machine learning assist in evaluating numerical data to determine credit scores, identify fraudulent transactions, and forecast stock price movements. Deep learning models advance this final area by integrating other text-based data such as social media and news-feeds to improve the accuracy of investment predictions.
Transportation
Demand forecasting and route optimization are areas machine learning is capable of handling. Deep learning provides additional assistance in technology such as self-driving cars as well as features like obstacle detection and traffic analysis.
Choosing Between ML and DL for a Project
When businesses are considering different AI projects, the first step in choosing the technology is determining the data availability. This is followed by budget, infrastructure, and the goals of the project. These criteria assist in determining whether machine learning or deep learning is the better fit for the project.
Use machine learning when:
- – There is a moderate data volume volume.
- – There is a well-defined structure.
- – Interpretability is necessary for regulation or other business reason.
- – The project is focused on predictions, classifications, or pattern analysis in numerical or tabulated data.
Use deep learning when:
- – There is unstructured data (text, audio, or video).
- – Interpretability is a lower priority while accuracy and adaptability are goals.
- – Accessible computational resources are in surplus.
- – Real time recognition or natural learning is needed for the application.
A professional AI development team helps businesses through these decisions, detailing what is realistic, and helping choose what type of model is the best fit.
How Businesses Benefit from Deep Learning Services
Through absolute deep learning services, businesses are able to provide systems that are more flexible and more automated. They are able to start learning to understand and compute complex data. This ventures into smart automation, customization, and prediction that helps to make decisions quicker and more accurately.
For example, an inspection time and error reduction could be achieved for a manufacturing company with image recognition systems using convolutional neural networks. Deep learning, on the other hand, can be applied on i-commerce platforms to create tags for products, make recommendations, and efficiently search to boost customer engagement.
There is more than just automation. Deep learning allows businesses to continuously improve. With the new information coming into the system, the model continues to improve its accuracy. This serves to give immense advantage to businesses in rapidly changing markets.
Integrating ML Development Services for Scalable Solutions
With trusted ML development services, organizations can create flexible predicting models from data that are able to adapt to new patterns. These services are usually inclusive of:
- Data collection and data preprocessing
- Algorithm selection and model training
- Evaluation of performance, deployment, and ongoing monitoring
Companies beginning with Machine Learning get an initial foundation for AI without the highest cost. These companies will eventually progress to Deep Learning when the organization has the data, processing power, and technology to do so.
Moreover, machine learning systems help support the automation of logistics, risk and marketing processes. These systems are flexible to support the automation of the aforementioned tasks also because they will not disrupt the workflows of the organization.
Challenges and Considerations
Potential during machine learning and deep learning adoption is also accompanied by an array of technical challenges.
Data Management
A deep learning model hinges on the premise of the data provided for learning. If the data is not of high quality, model reliability and accuracy will not be attained. This results in loss. Businesses should focus on data quality in the management and governance frameworks in place.
Model Explainability
When deep learning models are created, it becomes increasingly difficult to explain how the results were achieved. This becomes a problem especially for sensitive industries where the trade-off between explainability and accuracy is a problem.
Infrastructure Costs
To develop and deploy deep learning models requires a lot of computing power, and therefore, finances. To help with offsetting some of the cost, using tools such as cloud-based AI systems help with the flexibility of systems while maintaining cost.
Skilled Expertise
The adoption of machine learning and deep learning requires an array of skilled personnel to be in the organization. These personnel must be skilled in the area of algorithms involved, how to preprocess data and best practices for model deployment. The involvement of specialized development organizations helps to ease this process and lowers the potential risk.
The Road Ahead: Blending ML and DL
The most likely scenario for AI’s future is the incorporation of ML and DL to develop hybrid AI systems. These systems would be able to work with both structured and unstructured data and adapt to solving business problems much faster.
Technologies currently being developed and deployed, such as reinforcement and transfer learning, are further enhancing the intelligent systems businesses can utilize, as they allow for systems not just to predict, but to make other intelligent decisions based on prior experiences.
With cheaper computing power and increasing amounts of data available, even the smallest of small and mid-tier businesses will be able to participate in the DL boom. ML will still be the driving focus for many businesses, as it will provide the basis for faster and more explainable systems as ML becomes entrenched in different industries.
Final Thoughts
The difference between machine learning and deep learning is not just in the label; it carries significant weight in the order of magnitude in which businesses can leverage AI. Analytics automation, prediction, and business intelligence are the primary benefits machine learning can provide. Deep learning builds on these benefits by adding unstructured data analytics, adding significant value in advancing business systems.
From a business leadership standpoint, understanding these differences will make it easier to achieve the right alignment of technology to goals, available resources, and timelines. The scope of each of these methodologies is wide, whether it is the building of predictive systems or the incorporation of advanced AI capable of significant self-modification. The scope of benefits of both approaches is different but equally wide in their own right.
At WebClues Infotech, our AI and data team crafts tailored machine-learning offerings, including advanced and deep learning models, based on the user’s profiling of their specific needs. We develop and facilitate the implementation of fully functional models which help organizations monetize their data.
Harnessing deep learning will help you automate your processes. Reach out to WebClues Infotech to learn more about advanced machine learning and AI development to help you operate your business more intelligently.