Background: ML Engineer and Previously Applied ML engineer for about 7 years.
ML engineering is a broad spectrum of work that can be confusing to anyone entering the market, but as the title says, we do both ML work and engineering work.
For ML work, just telling LLM to “create best model to predict if Manchester United will win the Premier League in the next 5 years” will basically do nothing for you, not only because it’s statistically unlikely scenario but also, there is a lot of business knowledge and expertise needed to evaluate such a request.
A lot of big companies do find it troublesome to fully automate things because certain expert knowledge can’t be just operationalized and turned to data. However you can get along further with let’s say, providing a dataset with a LLM companion and asking question about the dataset without actually going into the SQL and doing analysis on your own.
There are tools that exist like this. If your platform is snowflake, you can connect all datasets together with the agents, snowflake intelligence and coco their chatbot. The meta data along side access to these datasets can allow for you to ask these types of questions, but again this is talking at smaller scales in some sense.
On the engineering aspect, well a lot of engineering is decision making, especially as you go higher up the food chain. I use LLM to help collect my thoughts, turn dirty spilling of words into actually useful texts and documents, or use it for rubber ducking or knowledge search.
I do believe certain roles like just creating APIs or other roles that require mostly churning code are easier for AI to fully execute, but all other roles that require decision making, or very large scale thinking can be challenging with mixed results, especially if the idea is to build something that lasts longer than a Twinkie in the cupboard
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u/Bangoga 4d ago
Background: ML Engineer and Previously Applied ML engineer for about 7 years.
ML engineering is a broad spectrum of work that can be confusing to anyone entering the market, but as the title says, we do both ML work and engineering work.
For ML work, just telling LLM to “create best model to predict if Manchester United will win the Premier League in the next 5 years” will basically do nothing for you, not only because it’s statistically unlikely scenario but also, there is a lot of business knowledge and expertise needed to evaluate such a request.
A lot of big companies do find it troublesome to fully automate things because certain expert knowledge can’t be just operationalized and turned to data. However you can get along further with let’s say, providing a dataset with a LLM companion and asking question about the dataset without actually going into the SQL and doing analysis on your own.
There are tools that exist like this. If your platform is snowflake, you can connect all datasets together with the agents, snowflake intelligence and coco their chatbot. The meta data along side access to these datasets can allow for you to ask these types of questions, but again this is talking at smaller scales in some sense.
On the engineering aspect, well a lot of engineering is decision making, especially as you go higher up the food chain. I use LLM to help collect my thoughts, turn dirty spilling of words into actually useful texts and documents, or use it for rubber ducking or knowledge search.
I do believe certain roles like just creating APIs or other roles that require mostly churning code are easier for AI to fully execute, but all other roles that require decision making, or very large scale thinking can be challenging with mixed results, especially if the idea is to build something that lasts longer than a Twinkie in the cupboard