Summons Faced By Bussiness In Adopting Machine Learning

Every business nowadays is growing rapidly by introducing smart technologies. Machine Learning is to diminish human efforts. It is a sub field of computer science which gives the capability for the computer to learn without being programmed explicitly. Existing algorithms are used to learn from the raw data given. Machine learning saves the time and human effort.

Machine learning allows finding the hidden insights without looking explicitly programmed. With the growth of high volumes and varieties of data nowadays the need for machine learning came into existence. Affordable data storage is to be required. Most complex data is to be analyzed automatically and quickly. Machine learning delivers accurate and faster results. Build a customized and precise model which contains a fundamental assembling point that helps to find the best machine learning solutions.

Machine learning problems are to make up the difficult parts of the software we use. Apple’s Siri is one of it. There are some best examples of machine learning they are spam detection, credit card fraud detection, digit recognition, speech understanding, face detection, product recommendation, medical diagnosis, stock trading, customer segmentation, shape detection.

There are some problems when we are doing machine learning they are classification, regression, clustering, rule extraction. Business Organisations also face many challenges while adopting machine learning.

Hardcore Challenges Faced By Business Organisations:

1.Sensitivity and Inaccessibility of data:

Major challenges faced by organisations is the collection of raw data for machine learning. Less number of items is not full to complete the process of learning. However, another major concern is its security. The data collected might be confidential which is to be encrypted and saved in other servers. Only trusted team members had the accessibility to less confidential data. One has to differentiate between the sensitive and insensitive data to implement machine learning efficiently.

2.Requirement Of Infrastructure For Testing and Experimenting:

Proper infrastructure will aid for testing different tools. For the best possible and desired outcomes, frequent tests should be done. Companies offer their data to many other firms to get an appropriate response, the results are to be compared and the best one is to be chosen by the board.

Stratification is one of the methods used to test the machine learning Algorithm. A random sample from the dataset is used to represent the true population. By dividing the dataset into stratified fashion this method works. Each class is correctly represented into subsets by randomly splitting the dataset.

3.Affordability Of Business Organisations:

If you are keen to implement machine learning you will require a project manager with a technical background, data engineers. Startups or newly formed companies can’t afford data scientist team. Machine learning method is extremely tedious but is also a revenue charger. It will give fruitful results only if you implement new and innovative methods. According to the recent studies the average pay scale of a data scientist is 1,05,000 $.

Machine learning is beneficial not by implementing a single plan, so you can try for a different plan for attaining desired standards. One has to find the best algorithm for achieving desired outcomes. By rapid experimenting with the algorithm, you will find the most innovative algorithms.

4. Inflexibility Of Business Models:

If one desires to implement machine learning efficiently, they require to change their mindset, proper and relevant skill set, infrastructure. Agile in policies is to be required by the business for implementing machine learning efficiently.

A guaranteed success is not assured in machine learning, proper and keen experimentation is to be done if one idea doesn’t work. Companies need to spend less amount, time on unsuccessful projects. Constant experimentations will give birth to a new successful robust machine learning design.