As an Enterprise, Are You Ready For Machine Learning?

Are You Ready For Machine Learning? Key points to consider for companies adopting machine learning now.

We are all in the middle of a technological revolution and artificial intelligence AI and machine learning ML are at the forefront.

ML is being adopted rapidly by different types of business across the globe.

So if you are in the nascent stage of adapting AI and ML techniques, “Are You Ready For Machine Learning?”

Well, you should look out for the following factors before you start.

Volume of Data

The heart of every algorithm lies in data- training data and testing data for ML.

What may sound like an obvious question is a rather significant one, do you have a sufficient volume of data to build and implement algorithms?

Machine learning with a limited data set isn’t really among the best choice. So before you think of moving over to machine learning models, run a quick test and understand if you have the ‘ideal’ volume of data.

“Given that artificial intelligence and machine learning are among the hottest topics these days, it should come as no surprise that a significant percentage of marketing outreach involves these technologies.” Oksana Sokolovsky.

Is Your Data Clean?

When your data comes from a myriad of sources, you need to ensure that it is clean and standardized in the form of a proper dataset.

For all we know, machine learning algorithms are all about GIGO- Garbage In, Garbage Out.

Do You Have Enough Money?

You have tons of data so you will need data warehouses. The average cost of storing data is $0.028 per gigabyte. So this means a lot of investment is involved as well.

You do need to ask yourself- What is the volume of data I have? What amount of data do I analyze? How often do I do it? Who will do it?

Often you will have data scientist do the job for insights. So this makes it cardinal for you to analyze the total cost involved.

Do You Have Enough?

Locate Your Data

When you have multiple storehouses for your data, you need to figure out where and in which form is your data residing?

Even for a centrally localized organization, data may be stored in multiple databases. Data handling is an essential part and if you have managed your data with the utmost care and accuracy, you can re-think on adapting machine learning as of now.

So, are you ready for Machine Learning?

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