Many start-ups have considered Artificial Intelligence and Machine Learning to revolutionise the way businesses work. Thanks to the buzz around ML and AI, start-ups are figuring out how they can introduce ML into their businesses. Let’s go through the top considerations about how Machine Learning can be implemented in the business scenario.
Analysing user behaviour is one of the most common uses of ML, especially in the retail sector.
Think of a retail experience. Whether your business is a brick-and-mortar store or performing online, it comes in contact with a lot of customers daily. Your business collects such data about customer purchase decisions, preferences and so on. Running such data through a machine learning algorithm can allow your business to predict consumer purchasing habits, market trends, and product popularity enabling it to make informed decisions based on the predicted data.
Almost all start-ups are dawning on automation because the importance of automation in streamlining mundane and repetitive tasks in saving cost and time cannot be neglected in the current scenario where Industry 4.0 is approaching.
Machine Learning can be used to improve manufacturing processes at the industrial manufacturing level. Understanding the current manufacturing processes and acknowledging loopholes and pain points can be helpful at the industrial manufacturing level by evaluating current manufacturing models.
ML is not limited to enhancing the manufacturing process. Combining two powerful tools- ML and AI, can greatly reduce defects in manufacturing while increasing the overall scalability and efficiency of processes and production.
With the increased flow of enabling web-based technologies, businesses have become more reliant on web services, leading to a more connected world. However, the risks associated with it include identity theft, phishing attacks, ransomware, privacy concerns, data breaches and so on.
Different businesses utilise different mechanisms and tools to prevent the aforementioned risks. For example, businesses use firewalls, threat management applications, strict data storage policies, intrusion prevention systems etc., to protect data. Large companies use dedicated security teams to monitor and fix vulnerabilities and threats imposed by web-based technologies,
Machine Learning can help offload monitoring and vulnerability assessment tasks with the use of the automated algorithm. For instance, ML can be used as an automated spam filter to protect employees from getting risky emails in their inboxes. Moreover, as the Machine Learning system is always learning, it gets more accurate with time.
Your business can utilise Machine Learning for performing financial analytics:
Performing these tasks can be done by analysing historical data with which your business can predict future performances. The accuracy of predictions depends on how much data is delivered and the strength and nature of the Machine Learning algorithm used. For example, a small data set with a relatively simple ML algorithm can be enough for performing simpler tasks like predicting business expenses. However, businesses performing more complex tasks like algorithmic trading will need decades of accurate data going through multiple revisions until production-ready ML models have been found. Investors and stockbrokers rely on Machine Learning algorithms for predicting accurate market conditions while entering the market.
Apart from these benefits, ML can be used for improved cognitive services like recognising images, natural language processing etc. With such diverse services, ML can improve the scalability and performance of an organisation.