Common Challenges That You Will Face in Your Big Data Training
Big data is becoming a powerful tool in many data science and management industries where automation, AI ML and Cloud computing are considered as pillars of the industrial revolution. In fact, without the support of developments in these capabilities, it is hard to imagine the future of technology world beyond the traditional tools of automation, robotics and few documentation processes. Big data’s popularity and fan following are at an all time high, no doubt. But, that’s not the only reason why you should be joining Big Data training course today. It’s the only science today that can be, in general terms, applied to all the fields relevant to sustainability of the life, such as healthcare, medical, retail, manufacturing and hundreds of others.
So, let’s start focusing on the challenges first-time users of Big data training course have to face in their journey with this novel platform. I have highlighted some here.
Too much data to work with
It’s called Big Data for some reason, folks! You have to encounter with tons of data before even getting familiar with what is big data training and handling the advanced fields in this subject. 6 out of 8 big data trainees abandon their career due to their inability to handle Big Data management and lack of focus while handing data. Anyone can overcome these fears of big data management by paying extra attention to data in the initial phase of training, especially when you are working with AI ML lifecycle. Machine learning techniques have solved complex challenges in Big Data by enabling developers and analysts with reliable platform to handle huge data sets.
Top Big Data blog: The Future of Big Data in India
Choosing the wrong data set or wrong ML model
Your data set might capture various problems due to issues pertaining to classification or intervals that might impact the way your machine learning models works in future. For example, if you have a poorly built classification system where the model has more elements of only one particular class and lacks examples from other classes, then your eventual ML model would also end up becoming highly skewed and obsolete.
You should rely on a Big data training programs that equips you with identification and mitigation of issues related to commonly referred problems called “class imbalance.”
Testing versus training
Most big data training programs lack sufficient resources to build out test data. If you are into data ops for Big data companies, always approach a problem using test data, rather than doing the same using trained data sets.
In more ways than one, test data for DataOps projects leak into trained data sets, thus causing complications referred to as poor validation. This is where most developers and engineers come across the challenges of working with machine learning models. The machine learning models can solve many problems, but this — where simple pattern matching and computing of algorithms require human intelligence, rather than deep learning of a machine.
