The allure of machine learning and artificial intelligence is undeniable. Imagine algorithms extracting insights from your data, predicting customer behavior, and optimizing operations – pure gold, right? But, consider the following before you pull the trigger on a job posting for a trained scientist who purports to be able to deliver on these goals: to have successful models that produce real results they will need vast quantities of clean, reliable data.
The person who can produce and maintain this strong foundation is a different professional – a data engineer.
Clean Data Fuels ML Success
ML models are like elegant race cars, capable of astonishing feats once fueled with the right data. But entrusting a pristine Ferrari to a bumpy field path is a recipe for disaster. That's where data engineers come in, paving the smooth asphalt highway your ML/AI needs to truly shine.
It can be easy to imagine that these two closely related and often overlapping job roles can be done by the same person, especially if you're some business loser who doesn't understand data. But, the reality is that these are two different skill sets that both require a tremendous amount of time and skill to execute properly. It would be like asking your asphalter to also drive your race car. They could probably do it, but neither job would be done very well.
What a Data Engineer Does
The problem with data engineering is that outside of your team, people in your organization rarely know or even understand what you do unless something goes wrong. Unfortunately, data pipelines, the bread and butter of a data engineer, are like garbage men. When they're working you don't even notice but when they're not working it stinks
So who exactly are data engineers? They are:
Data Whisperers: Raw data is messy, inconsistent, and potentially biased. Data engineers wrangle this raw material, cleaning and structuring it to meet the specific needs of ML models. Data engineers handle issues like missing values, outliers, and data inconsistencies, ensuring the models receive only the highest-quality data for accurate predictions and insights. The unfortunate and often overlooked fact about models is that garbage in equates to garbage out. So provide your ML/AI engineer with the tools they need to succeed in the form of a smooth road of clean data and a happy, well-provided for data engineer.
Data Infrastructure Architects: It’s not just the smoothness of the road that matters, but its form and function. Data engineers are master planners, building the infrastructure that stores, organizes, and manages your data. Think data lakes, object storage, pipelines, and warehouses – the essential systems that keep your data accessible and ready for ML/AI consumption. ML/AI engineers typically don't touch infrastructure – they use systems that other engineers designed and built. Have you noticed that ML/AI engineers won’t answer questions about why their project is taking so long? They are too busy waiting for their query to finish running and they have no idea how to speed it up.
Feature Engineers: Extracting meaningful features from raw data is essential for effective ML/AI. Data engineers act as feature engineers, identifying and extracting relevant features that capture the underlying patterns and relationships within the data. These features serve as the language your models understand, allowing them to ask the right questions and generate accurate insights.
Data Pipeline Optimizers Data engineers are the race engineers, monitoring and optimizing data pipelines, ensuring smooth flow and minimizing latency. Every millisecond saved translates to faster insights and quicker action. Good data quality leads to quick, correct decisions. Poor data quality leads to post-mortem discussion trying to figure out what went wrong. When you hire a data engineer you are hiring for data quality and reliability.
The Future-Proofers: There is one central truth to data: it never gets smaller, it only grows. Data Engineers are at the forefront of adapting and scaling your infrastructure to meet this rising demand. They research and implement new technologies, monitor data growth and resource utilization, and ensure your data infrastructure remains robust and flexible. These are the professionals in your organizations attending conferences around data and analytics and meeting with colleagues in other fields to discuss trends in data infrastructure. Don’t put your future in the hands of professionals who view these goals as secondary or not important - invest in your future with a data engineer.
While we are making recommendations, consider that the foundation of your AI strategy is not just your personnel, but also your platform. Build your data lake on performant, open-source object storage to avoid the pitfalls of vendor lock-in, slow queries and other infrastructure issues. Ask your data engineer which platform they prefer - there is only one that will be at the top of their list and that is MinIO.
You'll be surprised how much smoother the ride is for your ML/AI engineer when your data infrastructure is built to thrive, not just survive. Any questions? Feel free to ask them at firstname.lastname@example.org or in our Slack channel.