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What are the main things a modern machine learning engineer does?
This seems like an easy question with a simple answer:
Build machine learning models and analyze data.
In reality, this answer is often not true.
Efficient use ofdatais essential in a successful modern business.
However, transforming data into tangible business outcomes requires it to undergo a journey.
It must be acquired, securely shared and analyzed in its own development lifecycle.
Solutions consultant at Ataccama.
These are the data users who use it to build models and contribute to other profit-generating activities.
If the input data is flawed, the output models and analyses will be too.
This massive, often invisible cost slows projects and reduces their outcomes.
The problem worsens when data clean up tasks are performed in repetitive silos.
The impact of data quality on machine learning
Clean data is particularly important for machine learning projects.
Data is an ever-changing landscape, after all.
This means previous assumptions of the data may no longer hold true.
Natural language processing enables non-technical users to query data and extract insights contextually.
The expected growth rate of AI between 2023 and 2030 is 37 percent.
Data quality is the backbone of AI.
It enhances the performance of algorithms and enables them to produce dependable forecasts, recommendations and classifications.
For the 33 percent of companies reporting failed AI projects, the reason is due to poor data quality.
In fact, organizations that pursue data quality are able to drive higher AI effectiveness all around.
But data quality isnt just a box it’s possible for you to tick off.
Data should be constantly tested, wherever it lives, against an ever-expanding pool of known problems.
All stakeholders should contribute and all data must have clear, well-defined data owners.
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