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Machine learning is the subfield of computer science that “gives computers the ability to learn without being explicitly programmed” (Arthur Samuel, 1959). In simple terms, the machine studies from existing data using algorithms that iteratively learn from the data set, machine learning allows computers to find hidden insights without being explicitly programmed where to look. Machine Learning is important for the future industry because it saves human effort and time.

Importance of Machine Learning

The interest in Machine Learning can be comprehended by simply understanding that there is a growth in volumes and varieties of raw data, the different processes, and hence, there is a need to find an affordable data storage.

The need of the hour is to implement a method by which organizations can quickly and automatically analyze bigger, more complex data. Not only this, by implementing and integrating Machine Learning in an organization, it becomes easier to optimize the process. How? Because Machine Learning helps deliver faster, and more accurate results.

What is simply required is to build a precise and customized model, in which Maruti Techlabs can serve as a fundamental assembling point, where your organization can find the best Machine Learning solutions.

Challenges faced while adopting Machine Learning

1. Inaccessible Data and Sensitive Data Security

Aleksandr Panchenko, the Head of Complex Web QA Department for A1QA stated that when a company wants to implement Machine Learning in their database, they require the presence of raw data, which is hard to gather. Data of 100 or 200 items is insufficient to implement Machine Learning correctly.

However, gathering data is not the only concern. Once a company has the data, security is a very prominent aspect that needs to be taken care of. Differentiating between sensitive and insensitive data is essential to implementing Machine Learning correctly and efficiently.

Companies need to store the sensitive data by encrypting such data and storing it in other servers or a place where the data is fully secured. The less confidential data can be made accessible to trusted team members.

2. Infrastructure Requirements for Testing and Experimentation

According to a study by Machine Learning Mastery, Machine Learning is hard for a business to implement, simply because the large scale companies in India have yet to identify and understand the positives that a simple Machine Learning algorithm can bring.

There is a need for proper infrastructure which can aid the testing of different tools. Frequent tests should also be allowed to develop the best possible and desired outcomes, which in turn, can assist in creating a better, stout, and manageable results.

Companies can give their data to different firms and ask for their appropriate response. Then, they can compare the results with a different perspective and the best one can be adapted accordingly by the company and subsequently, by the board. However, a small section should still be allowed to work on a different mechanism to allow space for innovation and it might help in providing a better result.

Maruti Techlabs can assist you to help build the best possible Machine Learning mechanism; you can find more about us by perusing our website and going through the smorgasbord of services we offer especially in the Data Analytics and Bot development domains.

Stratification method is used to test the Machine Learning Algorithm. In this method, we draw a random sample from the dataset which is a representation of the true population. The common practice is to divide the dataset in a stratified fashion. Stratification simply means that we randomly split the dataset so that each class is correctly represented in the resulting subsets — the training and the test set.

3. Inflexible Business Models

Machine learning requires a business to be agile in their policies. Implementing Machine Learning efficaciously requires one to change their infrastructure, their mindset, and also requires proper and relevant skill-set.

However, implementing Machine Learning doesn’t guarantee success. Experimentations need to be done if one idea is not working. For this, agile and flexible business processes are crucial, companies also need to spend less time, effort, and money on unsuccessful projects. A study conducted by StackExchange supports rapid experimentations.

If one of the Machine Learning strategies don’t work, it enables the company to learn what is required and consequently guides them in building a new and robust Machine Learning design.

4. Affordability of Organisations

According to O’Reilly, it was found that the average base salary of a data scientist in the United States in the year 2014 was $105,000. This figure does not include benefits, bonuses, or any other compensation. Including all the other expenses, the figure can rise up to $144,000.

There is a virtue in knowing these values if you’re looking to implement Machine Learning, because if you’re applying Machine Learning, you will require Data Engineers, a Project Manager with a sound technical background. In essence, a full data science team isn’t something newer companies or start-ups can afford.

In conclusion, employing a Machine Learning method can be extremely tedious, but can also serve as a revenue charger for a company. However, this is only possible by implementing Machine Learning in newer and more innovative ways. Machine Learning is only beneficial if there are different plans, so regardless of one plan not performing up to the desired standards, the other can be put into action. Getting a glimpse into which Machine Learning algorithm would suit an organization is the only issue that one needs to get by. Once you get the best algorithm with which you’re achieving the required outcomes, you shouldn’t stop experimenting and trying to find better and more innovative algorithms.

If you’re ready to learn more about how Machine Learning can be applied to your business we’d love to talk to you. For more information view our Big Data and Analytics services.