Machine learning (ML) is an application of artificial intelligence (AI). Machine learning equips the systems with the ability to automatically learn and make improvements from experience without being explicitly programmed. The ML algorithms employ statistics to find patterns in massive patterns of data and use them to learn for themselves.
The goal of ML is to allow computers to learn automatically without any intervention or input, or assistance from humans. The data used for learning comprises numbers, images, words, etc. According to a recent study, 77% of the devices that we use today utilize ML facilities.
The platforms using ML are search engines like Google and Baidu, recommendation systems of Netflix, YouTube and Spotify, voice assistants like Siri and Alexa, and social media feeds like Facebook and Twitter.
The principle of ML comprises collecting as much data as possible and using it for learning and guessing what thing you must like next. ML finds a pattern and applies the knowledge gathered to use by suggesting the next options for the concerned person.
The trends keep evolving in this fast-paced new world of technology with new developments happening all over the world. Here, we predict what the future holds with the top machine learning solutions.
Table of Contents
Top Machine Learning Solutions for 2022
1. Cutting Edge Model Availability
Since the time ML is becoming more widely adopted, a parallel trend with open access to models is also witnessing a rise in its popularity and development. The large companies developing ML are raising the bar for model performance in parallel as well. This is possible due to the large and comprehensive datasets that are available with them, which they use to train models by dedicated ML practitioners.
However, not all companies possess the capital or research technology to build such models from scratch. Hence, they are using the help of transfer learning wherein they can build upon or repurpose models that have undergone extensive training to develop high-performance models. Meanwhile, even the large enterprises have recognized the importance and benefits of such contributions from the outside for the development of their models.
The open-access models or public models can be used by students too who are experimenting with ML. Similarly, hobbyists and other groups can also use these base models. The successful experiments may contribute to these models and, at the same time, enhance their career growth.
Hyper-automation supports the idea of almost anything inside a company can be automated. It has been gaining popularity for some time around the world now, but with the pandemic last year, its necessity and emphasis on it has increased even further. Intelligent process automation and digital process automation has experienced a boost.
The driving force for hyper-automation is ML and AI, which are its key segments. The essential requirement for automated business processes to proceed is that they must be able to adapt according to the changing conditions and also react to sudden circumstances when the time comes.
Related: Top Machine Learning Applications
3. Superior Supporting Tools for ML
In today’s times, producing a working ML model that makes fairly good predictions is not enough. The ML practitioners require model interpretability wherein they understand why predictions are being made before deciding whether the model should go into production. This is often important in the case of enterprises where the predictions are scrutinized for societal factors such as social justice, ethics and fairness.
A powerful tool for model development is the use of model cards that are design documents that formally describe all aspects of a model. The aspects include the following details-
- Detailed overview consisting of a summary of the model’s purpose.
- Logistics about the author links to additional documents, license, date, etc.
- Specifications about neural networks or types of layers, inputs and outputs.
- A summary about its limitations and considerations, including information regarding ethical and privacy issues, speed and accuracy constraints.
- A target and actual performance metrics that is basically expected versus actual accuracy.
Visualization is another key tool. An invaluable aspect is the ability to visualize a model during design, training and even during the audit.
The model cards can be used by team members to constantly evaluate the model performance against what is specified on a card.
4. Business Forecasting and Analysis
ML can contribute towards business forecasting and help in making important, informed decisions related to business. The experts gather and screen a set of data over a fixed period of time, which is then utilized for making smart decisions. Once ML is trained with diverse data sets, it can provide conjectures with accuracy as high as approximately 95%.
We predict that organizations would fuse recurrent neural networks and obtain high-fidelity forecast results. One of the main advantages of using ML is finding the hidden patterns that may have been missed out upon. The best example for its use is in insurance firms to identify potential frauds that could be very costly. ML might assist in discovering hidden patterns and make accurate forecasts accordingly.
5. ML and Internet of Things (IoT)
Economic analyst Transforma Insights has forecasted that the IoT market will develop 24.1 billion devices in 2030, leading to $1.5 trillion in income throughout the world due to its rapid development.
The utilization of machine learning and the Internet of Things is intersected. Production of IoT devices utilizes ML, AI and deep learning to make the services smarter and more secure. In a similar manner, networks of IoT sensors and devices provide gigantic volumes of data for ML and AI for them to work effectively.
6. ML at the Edge
It is predicted inference at the edge will grow substantially throughout 2022. Among the various factors contributing to this growth, the main two are the growth of IoT and a greater reliance on devices for doing remote work.
Enterprise-oriented and consumer devices like Google-mini employ cloud-backed ML. Basically, cloud-backed ML collects data by conjuring up images of tiny devices with internet access and sends it to the cloud for inference. It is necessary in many situations like detecting fraud by banks and in cases where longer latency is not an issue. But, in the case of edge devices, they are gaining the processing power required to perform interference at the edge.
An example of such technology at the edge is Coral by Google. It possesses an onboard tensor processing unit (TPU) and handles numerous IoT use cases (eg. analyzes voices and images). This shows that inference is now possible without any internet connection and cloud back end with the technology packed into a small form factor. The added advantage that ML at the edge offers is security by keeping the collected data on the device itself.
Technically the above-mentioned deployments demand smaller ML models that are transferred quickly and fit into embedded devices with limited storage. Here, quantization is the solution to reduce the model’s size.
According to the statistics provided by Gartner, ML is being used in some form or the other in approximately 37% of all companies for their business that was reviewed. It is also estimated that around 80% of the modern advances will be founded on ML and AI by the year 2022.
There is a surge in demand and interest in ML with various new patterns and technologies ascending with the increasing number of useful applications.
Also Read: Machine Learning Projects for Beginners
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