Yes. Machine Learning and AI tools are the future of data preparation. Tools like MinHash can hugely improve your machine learning results.
I’d like to discuss a really simple and effective technique that I feel is often overlooked by ML engineers. Machine learning can be split into two steps: data preparation and model training (building a model). The quality of the models you build and the predictions you make are heavily dependent on your data preparation step (or lack thereof). Your final model will only be as strong as your data-prep process.
When it comes to data preparation and improving your machine learning results, you have several options to choose from Auto-encoder/Decision trees/Ensembles/Support vector machines…the list goes on. But there is a really simple and effective technique that I’d like to discuss today that I feel is often overlooked by many ML engineers. Machine learning can be split into two steps: data preparation and model training (building a model). The quality of the models you build and the predictions you make are heavily dependent on your data preparation step (or lack thereof). Your final model will only be as strong as your data-prep process.
Data Preparation & Its Future [ML & AI]
Tools:
While it is true that AI and machine learning tools are improving all the time, there is no way they will ever be good enough to completely replace humans. Data preparation always requires a human eye to check it over before it is used, no matter how powerful the algorithm in question happens to be. The future of data preparation will inevitably involve an increase in automation, but this does not mean that your job is going anywhere.
It’s only going to get easier for you to have the tedious parts of this job taken out of your hands, with powerful algorithms doing all of the really difficult stuff. However, this doesn’t mean that you’ll be able to get rid of humans for good.
Systems:
Data preparation is the process of transforming raw data so that it can be used by a machine learning algorithm. The problem is that this process is tedious, which makes it a bad fit for most scientists and researchers. And yet, because the world has more data than ever before, the need for data preparation is more important than ever.
The job of a data scientist:
To this end, there is one avenue that you may not have considered: Machine learning and natural language generation (NLG). Here are some ways in which these technologies can help you get less bogged down in the data preparation process and make your work more efficient.
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Communication:
One of the current challenges when it comes to data preparation is that communicating what you have done can take a long time. You need to write up reports and make sure that everyone involved understands them properly.
The good news is that Machine learning and NLG can do away with some of the manual steps involved in this process by enabling the creation of automated reports. These may be published online or sent directly to your colleagues via email or messaging apps. This will make it easier for you to share information with whoever needs it. And also speed up the process as a whole.
ML ENGINEER’S career predictions for 2022:
It is not a far leap of imagination to say this could be the scenario in 4-5 years. This may sound like a dream come true. But machine learning and artificial intelligence are already transforming the world in different industries. It is these servants who are replacing humans in several routine jobs. And this situation will continue to do so in the future.
As per Gartner’s recent report, in 2020 more than 50% of chief information officers (CIOs) used cognitive technologies. This is to automate their business processes. In businesses, there is a potential of over $300 Billion in opportunities that cognitive IT can unlock through automation. For instance, business process automation which will improve operational efficiencies with intelligent analytics can be a huge area for ML.
Thousands of Jobs for Engineers [Highly paid]
Field Engineer has curated a list of the highest paying engineering jobs for the year 2022. And also we mentioned our predictions for the most in-demand engineering jobs of the future. Did you know, for example, that machine learning engineers are some of the highest-paid engineers in the industry?
At Field Engineer, we’re always looking for ways to help our field engineers learn new skills and find new opportunities. We didn’t stop at the machine learning and AI engineer job opportunities in data preparation. Field Engineer has curated a list of the highest paying engineering jobs currently along with our predictions for the most in-demand jobs of the future. Learn more!
