The enthusiasm for Machine Learning (ML) can be appreciated by essentially understanding that there is a development in volumes and varieties of raw data, the various procedures, and hence, there is a need to find affordable data storage. The need of the hour is to implement a method by which organizations can quickly and automatically analyze bigger, increasingly complex information. Not just this, by actualizing and coordinating ML in an association, it gets simpler to improve the procedure. How? Since ML helps deliver faster, and more accurate results.
There are some of the Challenges of Machine Learning which could be adopted while training machine learning models:
1. Inaccessible Data and Sensitive Data Security
At the point when an organization needs to implement Machine Learning in their database, they require the presence of raw data, which is difficult to gather. Data of 100 or 200 items is insufficient to implement Machine Learning correctly. However, gathering data isn’t the main concern. When an organization 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 effectively and productively. 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 classified data can be made open to trusted team members.
2. Inflexible Business Models
Machine learning requires a business to be agile in their policies. Implementing Machine Learning strongly expects one to change their framework, their mentality, and also requires proper and relevant abilities. 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. If one of the Machine Learning procedures doesn’t work, it enables the company to learn what is required and consequently guides them in building a new and strong Machine Learning design.
3. Infrastructure Requirements for Testing and Experimentation
One of the challenges of Machine Learning is it needed other technology in place, such as big data, business intelligence (BI), cloud computing (at least private cloud) infrastructure etc . Emerging and new technology always bring in technology breakthrough but at the same time require company re-design for the existing technology infrastructure that are whether remain value added where new technology is able to allow the company leverage them to gain competitive advantage to provide faster, better response in term of business intelligence (BI) as input for the various operation decision making and execution.
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