Embarking on the journey towards Machine Learning Enlightenment (MLE) is no small feat. It's a path filled with complex algorithms, vast data sets, and innovative technologies. Let's delve into the intricacies of MLE, unraveling its mysteries and exploring its potential.
Ivan Shishkin, The Field of Wheat
A machine learning engineer left her home to seek the answers that she could not find, even in the newly-optimized Google results
- She walked past farms where cows grazed peacefully underneath enormous data silos, until the rows of crops gave way to a smattering of graceful pines and oaks, and she found herself in a forest clearing, headed into the woods.
- After several hours she stopped and took a drink from her Klean Kanteen as she surveyed the sprawling random forest, the valley spread out below her and the sparkling data lake in the distance. Finally, she found a path that started snaking its way up a mountainside, and started to hike upwards, through the red rocks.
What does this mean for me?
Nothing, keep doing what you’re doing.
- You’ll get to XGBoost eventually, and then, after working with the model for a brief period, you’ll have a whole new set of very boring, non model-specific problems related to
What online and offline metrics should be, where you’ll store them, and how to analyze them
The beautiful part is when you finally connect all these systems and your database is talking to your streaming platform and your model is reading from the database and you have a new model every day
- You can look back and see the forest of what you have built
They sat in silence for a bit. The Staff Engineer sipped his cold brew thoughtfully through a biodegradable straw.
Finally, the machine learning engineer said, in a very small voice, “I still haven’t gotten to machine learning engineering.”
- “Well, hang on. You said you were,”
The Staff Engineer said, “Let me ask you something. Did you enjoy the walk here, even though it was long, hard, and annoying?”
The machine learning engineer looked at the sunset thoughtfully
- “The journey, ultimately, is the destination,” he said, neatly depositing his iced coffee container in the recycling bin.
The ecosystem of the model is always greater than the model itself.
Much of the work we do in machine learning will be glue work and vendor work. We’re building more and more on older systems, abstracting away complexity, and in the process creating newer and newer levels of complexity.
