During 2018, we witnessed a dramatic rise in the platforms, tools, and applications based on Machine learning and artificial intelligence. These technologies not only impacted software and the Internet industry but also other verticals such as healthcare, legal, manufacturing, automobile and agriculture.
The level of technological advancement and adoption of Artificial Intelligence in different business industries is incredible. Every business is striving to integrate machine learning and continuous intelligence to enhance production and boost efficiency. There is more to business in 2019 than the conventional marketing techniques; apart from e-commerce where most transactions and purchases are made online, AI is rising in all nature of businesses.
Top companies like Google, Microsoft, and Amazon are pushing for the implementation of different aspects of AI in business. In this blog we examine the AI trends fueling organizations in 2019.
Movement towards Automated Machine Learning
Data analysis is a huge task for scientists working in different niches. It takes time and brains to rack market elements and advising companies on the best techniques and decision that will distinguish it from other firms in the market. The business industry is all about competition; if you are not ready to learn new tactics every day, then the business might not be for you. Data scientists have to identify possible challenges in the niche and find possible solutions that not only improve a business’ revenue but also improves its market position for several months. Reputation is crucial in business; some customers are only drawn by the popularity of a company name.
In 2019, the shift from machine learning to the automation of analysis and decision-making is simplifying the process. Google, for instance, has recently launched AutoML that enhances speed and accuracy in cloud computing.
Machine Learning in Cloud Computing
Initially, cloud computing was more towards data storage but now it is more than storing data. Cloud computing hosts the commands and machine learning tools. It provides a cloud environment for analyzing and training models to works as per the required design and structures of a machine. It has provided machine learning as a service where data scientists can use analytical method directly on the cloud to get faster results.
Deep Learning Leading the Way
Deep learning is a subset of machine learning. There is more to it than machine learning and integration of aspects of technology with different business settings using neural networks to perform machine learning tasks. Deep learning has a lot of applications in computer vision, natural language processing, and speech recognition.
Today, companies rely heavily upon human intelligence to interpret, anticipate, and intuit information in ways that machines cannot. That’s about to change. In the future, the intelligence generated by data intelligence generated from company assets—infrastructure, IT systems, and inventory, for example—may surpass human insights as organizations’ most mission-critical business intelligence. Sensors embedded in vast IoT networks, computer vision, and machine learning will feed data into analytics systems in real time. AI tools, acting autonomously on the resulting insights, can reconfigure dynamic pricing on store shelves, recalculate warehouse staffing projections, calibrate manufacturing machines, and optimize supply chains.
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