All You Need to Know About Machine Learning Investments
Tags: webdev, programming, beginners, discuss
Published: 2022-04-06 05:59:38


Artificial Intelligence and Machine Learning technologies have been in use in this industries for over a decade, and every year their adoption is increasing exponentially. In the past two years, after the COVID-19 outbreak, the era of remote working has taken a place where productivity and security of business have become a controversial topic. And perhaps that is the prominent reason behind the sudden acceleration in Machine Learning investments and AI investments.    

Hence, before investing in this cutting-edge technology, organizations should analyze some important statistics and need to make more promising decisions. Let’s first understand the term machine learning in-depth.   

**What Is Machine Learning?**

Machine learning is a concept of training machines to work as humans and predict outcomes by analyzing data and user behaviors extensively. It contains learning models, like supervised, semi-supervised, unsupervised, and many others. It uses database-related algorithms and analytics such as big data & predictive analytics to create a way to develop AI. Hence, we can call ML an integral approach to building process automation, such as smart chats, speech recognition, and digital transformation.    

Machine learning tools, like Google’s graph-based ML tool, help to determine customers' needs, market trends, business insights, and potential risks through a semi-supervised learning method. Nowadays, ML is the key to achieving business automation, intelligence and increasing operational efficiency. 

**How Machine Learning Usages are Increasing Industry-wise?**

As per the latest report circulated by Industry Research, the global ML market is expected to rise by USD 11.16 Billion at the CAGR of 39% during the forecast period of 2020 – 2024. According to the report of 2021 enterprise trends in machine learning, around 83% of companies’ surveys show 76% increment in AI/ML market size and the number of data scientists per year.   

The aftereffect of the 2020’s lockdown on businesses made organizations rethink their current IT strategies. However, this pandemic has not just helped industries to adopt AI/ML technology but also doubled their overall productivity and profitability. According to the survey conducted by Techerati, over 43% of businesses (400 business leaders) found COVID-19 pandemic and AI/ML adoption as a major helping hand to continue their business workflow even in the remote working set up. Also, they are not just expanding but also growing the enterprise ML use cases with their automation and learning capabilities.  

**Let’s have a look at how industries are using machine learning:**

**Healthcare Industry:**

When it comes to the utilization of machine learning abilities for potential developments, the healthcare industry is leading in this field. They use ML algorithms to discover vaccine models identifying various combinations of protein structures and their effects on different patients.  

They are utilizing ML capabilities to save considerable time, money, and human resources to identify and differentiate areas concerning corona patient numbers. From task management, health record management to curing several diseases, ML has shown a positive scope in the medical sector.   

**E-Commerce Industry:**

Today, online shopping trends are increasing and even replacing traditional shopping methods. With the rise of the e-commerce industry, business leaders are integrating AI/ML capabilities into their shopping applications and giving their customers virtual personalized experiences by enabling intelligent try on features (i.e., Lenskart). Machine learning is not just growing e-commerce industries’ sales but also helping them to update their features according to the latest trends.    

Machine Learning is also being used in other industries, such as banking and finance, cybersecurity, media & entertainment, production, automotive, etc.   

**Implementing Machine Learning Strategies:**

Every organization and enterprise should make effective strategies before starting machine learning development in order to extract the true capabilities of this technology. Else, it will only continue to automate basic human routine tasks rather than being productive.    

To make an effective machine learning strategy, every business should contain a team of qualitative analysts and translators. So, they can have the benefit of both qualitative and quantitative processes and improve their productive tasks with modernization. 

**Make a Machine Learning Investment Today:**

Even if industries are familiar with machine learning technology, it will take some time and effort to move from traditional systems to modern ones. It is time for companies to adopt ML and data analytics approaches for their businesses and develop complete transparency across working environments with the help of modern technology.  

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