MLOps & Industrial AI Are Progressing Quickly and Are Unstoppable
AI& MLOps are both unstoppable. Both will change the whole scenario of the industries, even the latest trends that we are observing. ML is one of the branches of AI, where other branches are Natural Language Processing, Artificial Neural Network (ANN), and Robotics. MLOPs will be expanded up to $4 billion by 2025, says Deloitte.com, in the year 2021, among all the use cases, the major focus was on improving customer experience (57%) The other ML use cases were customer retaining, fraud detection, building brand awareness, and others.
I always wonder what will be next, but I got my answer when I learned that AI & MLOps are both unstoppable. Both will change the whole scenario of the industries, even the latest trends that we are observing. The continuous evolution in DevOps with the combination of Machine Learning for corporate application development is a solid reason.
The real-time example is the healthcare sector. Many articles on the internet reported that patient experience and medicine practice have changed using modern-age techniques such as AI ML or chatbot in the healthcare industry. Another best example is the manufacturing sector that is progressing in Industry 4.0. Now, industries do not hesitate to use smart devices, sensors, and tech-oriented processes for smart manufacturing.
I am more excited to share with the readers the advantages of MLOps, such as:
For novice readers here, I will mention the basic definitions of AI & ML that will help make them more comfortable with the topic that we are reading here.
Most of the time, you will find Machine Learning and Artificial Intelligence together. The reason is ML is one of the branches of AI, where other branches are Natural Language Processing, Artificial Neural Network (ANN), and Robotics.
In simplistic terms, its definition can be understood as following the concept of ML; machines learn from the data instead of obvious programming. However, the process is complex and requires the efforts of Data Scientists and AI developers.
As per statistics published on Statista.com, in the year 2021, among all the use cases, the major focus was on improving customer experience (57%). The other ML use cases were customer retaining, fraud detection, building brand awareness, and others.
The definition of AI is straightforward to understand that making a machine act with human intelligence. It is one of the technologies that is progressing continuously and even a most popular topic of debate. The credit goes to Alan Turing and Jhon McCarthy. The ultimate goal of AI is to make a Machine Smart.
According to the current scenario, AI is not fiction or imagination. It is a reality that we can observe in our surroundings. Every day, AI researchers are trying to make the machine more intelligent than its previous version.
One of the fantastic websites to read about scientific facts and research-oriented news –ScienceDaily, published on 11th Feb 2022 that wearable armbands can help have the grip for persons living with prosthetic hands. Previously on 8th Feb, they published that self-sensing robots are now equipped with electrochemically driven pumps. In combination with Big Data & AI, wildlife preservation can happen, including larger data sets and smart tracking devices.
It is only one example; other examples also exist. These examples are not for filling the content or making this article lengthier. It is for the new readers to understand the 360-degree scope of AI. Later it will help them to co-relate with MLOPs and Industrial AI.
Not only the readers but also businesses are curious about MLOps. They are trying to find how industrial AI & MLOps can help them for future transformation. Before we proceed with core information here, I would like to mention the fact based on the report published by Deloitte. It says that MLOps will be expanded up to $4 billion by 2025.
