Big data a name largely associated with Machine Learning. For a bot to learn and develop it needs data containing patterns, expressions and program details that are fed in from of data. Big Data will help companies make informed decisions based on analytics and solve problems while benefiting them in the run to achieve their goals.
Machine Learning is always not about only advantages- it does have its own disadvantages as well. We will discuss the advantages and disadvantages as we move along this piece. Starting with the advantages;
- Easily understands trends and patterns
Humans cannot easily analyze big amounts of data in form of patterns and trends but machine learning can do that from analysis perspective. Examples are big companies trying to understand browsing behavior and purchase histories of their customers and cater to them the right product.
Human intervention is not needed when applying Machine Learning. Only data is what it needs to perform the tasks and since they get the option to learn from the data, machines try to improve predictions and develop algorithms on their own.
- Continuous Improvement
As ML has the ability to learn by themselves, they improve continuously to improve accuracy and efficiency.
- Capacity to handle multi-dimensional and multi-variant Data
Machine Learning algorithms are good at handling data that are multi-dimensional and multi-variety, and they can do this in dynamic or uncertain environments.
- Wider implementation
Anyone can use ML to get information- you may be a healthcare provider or e-teller. It targets the right customers by holding the capability to deliver a personal experience to customers.
Disadvantages of Machine Learning;
- Data requirements
ML requires a huge amount of data to train itself, which should be of good quality, unbiased and inclusive. Sometimes they will have to wait for new data to be generated result in waste of resources due to the delay.
- Resource and Time
Time has to be given to the bot to learn from data that is being fed in so that it increases its accuracy and efficiency. ML also needs massive resources to implement resulting in an increase in resources.
Another factor to keep an eye on is the ability of algorithms to deliver desired results. Algorithms that are developed should be perfectly tested before they are deployed. You must also carefully choose the algorithms for your purpose.
- High error-susceptibility
As machine learning is autonomous it is highly susceptible to errors. If the data sets fed are too small to not be inclusive it will end up with biased predictions coming from a biased training set. The error, in this case, will be irrelevant ads being displayed to customers. In machine learning such errors can go a long way being noticed. Also if they are recognized it will take quite some time get to the source of the issue and even more time to correct it.