Source: Deep Learning on Medium
Machine learning will be a must for every business
I believe that machine learning (ML) is a generic term that includes the subfields of deep learning (DL) and classic machine learning (CML). It is seen as a subset of artificial intelligence. For this last,it is anything that pretends to be smart.
For Deep learning (DL) is a class of machine learning algorithms that utilize neural networks.
The table below gives the definition and domain of each field:
Some technologies become widespread and commonly used, while other simply fade away. Recall that just a few short years ago 3D movies were expected to totally overtake traditional films for cinematic release. It did not happen.
It is important for us to continue to monitor the ML and DL technologies closely.
It remains to be seen how things will play out, but ultimately, we can convince ourselves about the viability of these technologies by experimenting with them, building, and deploying our own applications.
Challenges and Concerns
As with any IT initiative, there is an opportunity cost associated with implementing it, and the benefit derived from the initiative must outweigh the opportunity cost, that is, the cost of forgoing another potential opportunity by proceeding with AI/ML.
These strategies, summarized below, are even available to small organization and individual freelance developers.
Data Science Platforms
If you ask business leaders about their top ML objectives, you will hear variations of the following:
• Improve organizational efficiency
• Make predictive insights into future scenarios or outcomes
• Gain a competitive advantage by using AI/ML
• Monetize AI/ML
You wish to create a recommendation engine for visitors to your website. You
would like to use machine learning to build and train a model using historical product description data and customer purchase activity on your website. You would then like to use the model to make real-time recommendations for your site visitors. This is a common ML use case ML Monetization, One of the best reasons to add ML into your projects is increased potential to monetize.
You can monetize ML in two ways: directly and indirectly.
- Indirect monetization: Making ML a part of your product or service.
- Direct monetization: Selling ML capabilities to customers who in turn apply them to solve particular problems or create their own products or services.
The table below shows some recent statistics :
These CAGRs represent impressive growth. Some of the growth is attributed to DL.However, you should not discount the possible opportunities available to you with CML,especially for mobile devices.
As a conclusion, the data show that ML for mobile apps has approximately triple the funding of the next closest area, NLP. The categories included show that many of the common DL fields, such as computer vision, NLP, speech, and video recognition, have been included as a specific category. This allows us to assume that a significant portion of the ML apps category is classic machine learning.