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What are the limitations and advantages of ML?

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Advantages of machine learning as a career option

1. Great opportunities to progress

The growth is enormous and very diverse, slipping its tentacles into a variety of industries – from finance and healthcare to transport and education.

As a machine learning engineer, you will not only witness the technological revolution but be a part of it.

Additionally, machine learning engineering as a career is an extremely lucrative job. The starting salary of a machine learning engineer in India is INR 6,70,000 per year.

2. Work that matters

Machine learning engineering will allow you to work and build real-world products, right from autonomous cars to security drones.

These are not some numbers you crunch into a spreadsheet only to never hear about them again.

Everything you create has a real-world application. Imagine the satisfaction of seeing something you’ve created help someone in their everyday life!

3. Direct link to Data Science

As a machine learning engineer, you will also develop the skills needed to be a data scientist. Becoming competent in both fields will make you a hot commodity for employers.

As a data scientist, you’ll be able to analyze data and extract value from it.

As a machine learning engineer, you’ll be able to make use of that information to train a machine learning model to predict results.

In several organizations, machine learning engineers work with data scientists for better synchronization of work products.

Disadvantages of machine learning as a career option

1. Demanding job

Training models, handling data as well as making and testing prototypes on a daily basis can lead to mental exhaustion.

As a machine learning engineer, data munging (or data wrangling) will also be a painful part of your job. Data munging simply means converting raw, unprocessed data into a more appropriate, usable form. Sometimes you might even have to scrape data from a paginated website and integrate it with your client’s internal data while simultaneously dealing with date-time and data type errors.

Doing this is no walk in the park and it could get frustrating for some.

2. It takes time and resources for machine learning to yield tangible results

Machine learning occurs over time.

So, there will be a period when your interface or algorithm won’t be developed enough for your company’s needs. The precise amount of time required will depend upon the nature of data, data source and how it is to be used.

You’ll simply need to wait as new data is generated — sometimes this can take days, weeks, months or even years!

3. Need to stay updated

As mentioned earlier, machine learning is a rapidly evolving field. Due to this, machine learning engineers are required to spend a considerable amount of time learning about the latest updates in the field.Advantages of machine learning as a career option

1. Great opportunities to progress

The growth is enormous and very diverse, slipping its tentacles into a variety of industries – from finance and healthcare to transport and education.

As a machine learning engineer, you will not only witness the technological revolution but be a part of it.

Additionally, machine learning engineering as a career is an extremely lucrative job. The starting salary of a machine learning engineer in India is INR 6,70,000 per year.

2. Work that matters

Machine learning engineering will allow you to work and build real-world products, right from autonomous cars to security drones.

These are not some numbers you crunch into a spreadsheet only to never hear about them again.

Everything you create has a real-world application. Imagine the satisfaction of seeing something you’ve created help someone in their everyday life!

3. Direct link to Data Science

As a machine learning engineer, you will also develop the skills needed to be a data scientist. Becoming competent in both fields will make you a hot commodity for employers.

As a data scientist, you’ll be able to analyze data and extract value from it.

As a machine learning engineer, you’ll be able to make use of that information to train a machine learning model to predict results.

In several organizations, machine learning engineers work with data scientists for better synchronization of work products.

Disadvantages of machine learning as a career option

1. Demanding job

Training models, handling data as well as making and testing prototypes on a daily basis can lead to mental exhaustion.

As a machine learning engineer, data munging (or data wrangling) will also be a painful part of your job. Data munging simply means converting raw, unprocessed data into a more appropriate, usable form. Sometimes you might even have to scrape data from a paginated website and integrate it with your client’s internal data while simultaneously dealing with date-time and data type errors.

Doing this is no walk in the park and it could get frustrating for some.

2. It takes time and resources for machine learning to yield tangible results

Machine learning occurs over time.

So, there will be a period when your interface or algorithm won’t be developed enough for your company’s needs. The precise amount of time required will depend upon the nature of data, data source and how it is to be used.

You’ll simply need to wait as new data is generated — sometimes this can take days, weeks, months or even years!

3. Need to stay updated

As mentioned earlier, machine learning is a rapidly evolving field. Due to this, machine learning engineers are required to spend a considerable amount of time learning about the latest updates in the field.

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