Over the past few decades, the modern world has seen the emergence of deep learning services and how innovative technology can transform the corporate world. According to Gartner, the job vacancies for Deep Learning (DL) experts have reached around 41,000 nowadays. The main growth drivers behind DL are big tech giants e.g., Facebook, Apple, Google and Microsoft. Such corporations utilise DL models to solve various challenges in domains such as speech & image recognition, 3D object recognition and Natural Language Processing (NLP).
Deep Learning Services: A Quick Insight
DL is a subset of machine learning technology which can train machines to learn by each data entry and upgrade the database to produce high-quality results. Experts designed innovative technology to mimic the human brain. However, a more suitable explanation is that DL models learn in layers. This enables a computer to create hierarchies of complicated concepts.
Real-life examples of deep learning technologies are driverless cars, voice control in smartphones and hands-free speakers. Hence, DL services are achieving results that were not possible with traditional machine learning techniques.
According to Markets & Markets research, the deep learning market size will achieve a financial worth of around 18.6 billion by 2023, showing a CAGR of 41.7% from 2018 to 2023.
The deep learning market in North America has the largest market share in terms of revenue. Rising demands for DL applications such as image & signal recognition will contribute to the industry’s growth. Moreover, industries like aerospace & defence, automotive, healthcare and IT will also play a vital role in the development of the concerned sector.
In the APAC region, the deep learning industry is flourishing because the innovative models are in smartphones, tablets and PCs. Moreover, advanced DL algorithms are also present in medical and automotive products. Major economic growth in countries located in the APAC market will be a key market driver of the deep learning industry.
Best 5 Perks of Using Deep Learning Services
Why are big giants like Google & Microsoft opting for DL technologies? To understand, let’s take a look at 5 advantages of deep learning models for modern-day enterprises which are below:
- Utilising Unstructured Data
A report from Gartner has found that a big percentage of a company’s data is unstructured because the information exists in texts and pictures. For traditional ML algorithms, it becomes challenging to analyze unstructured data. This is where the utilisation of deep learning services can help experts to process the information accurately.
Professionals can use various data technology formats to train deep learning algorithms and extract valuable insights from the information. For instance, deep learning services can help specialists to make sense of the relationship between industry analysis, and social media chatter. DL models can also help experts make predictions about upcoming stock prices.
- Eliminating the need for Feature Engineering
In machine learning technologies, feature engineering is a fundamental duty because it improves the accuracy of results. One of the most interesting perks of deep learning services is that they can perform feature engineering independently. This way, algorithms can process data to identify characteristics that have a strong connection with each other. It fosters faster learning without Human Intervention (HI). The ability facilitates data scientists to streamline various tasks.
- Delivering Top-Notch Results
Humans naturally feel hungry, get tired or make errors at work. Nonetheless, deep learning models do not have such limitations. After proper training, deep learning services can easily execute repetitive tasks within a few seconds. In comparison, workers take longer to achieve results. When it comes to the high standard of output, DL services maintain it. The quality only declines when the raw data is not up to the mark.
- Reducing Expenditures
Recalling company products because of mistakes can be costly for various organizations. With the support of deep learning services, minor defects such as mistakes in product labelling can be easily dealt with.
Deep learning models can also identify errors that are challenging to find otherwise. Deep learning services can help experts highlight variations in image data and extract valuable insights to streamline the inspection process.
- Achieving Results Without Data Labelling
Data labelling is costly and time-consuming. With deep learning services, experts can forgo data labelling because the latest algorithms can excel at learning with external supervision. Other models of machine learning are not as advanced as deep learning algorithms.
The Bottom Line
Keeping in perspective the interesting perks of deep learning services, the positive impact of DL models is very clear on Advanced System Architecture (ASA) or the Internet of Things (IoT). This way, advanced algorithms can make valuable contributions to the corporate world.
Deep learning technology is becoming a critical aspect of the age of the fourth industrial revolution. It can secure a competitive advantage and help corporations achieve business milestones timely.