•1 min read•from Data Science
Do MLEs actually reduce your workload in your job?
Our take
In the realm of machine learning, the role of Machine Learning Engineers (MLEs) can be pivotal, yet their impact on workload reduction often varies. In larger organizations, the disconnect between MLEs and Data Scientists can complicate model deployment and monitoring, leading to increased debugging responsibilities for data professionals. In contrast, smaller companies can foster more collaborative relationships, allowing for smoother transitions from model training to deployment.
Maybe I’m wrong, but I feel like in the bigger companies I have worked for, the “client - provider” kind of setup for MLEs / MLOps people and Data Scientists is broken.
Not having an MLE in the pod for a new model means that invariably when something is off with the serving, I end up debugging it because they have no context on what’s happening and if it is something that challenges the current stack, the update to account for it will only come months down the road when eventually our roadmaps align. I don’t feel like they take a lot of weight off my shoulders.
The best relationship I ever had with MLEs was in a small company where I basically handed off the trained model to them for deployment and monitoring, and I would advise only on what features were used and where they come from (to prevent a distribution mismatch in their feature serving pipelines online).
Discuss
[link] [comments]
Read on the original site
Open the publisher's page for the full experience
Related Articles
- Is the ds/ml slowly being morphed into an AI engineer? [D]Agents are amazing. Harnesses are cool. But the fundamental role of a data scientist is not to use a generalist model in an existing workflow; it's a completely different field. AI engineering is the body of the vehicle, whereas the actual brain/engine behind it is the data scientist's playground. I feel like I am not alone in this realisation that my role somehow got silently morphed into that of an AI engineer, with the engine's development becoming a complete afterthought. Based on industry requirements and ongoing research, most of the work has quietly shifted from building the engine to refining the body around it. Economically, this makes sense, as working with LLMs or other Deep Learning models is a capital-intensive task that not everyone can afford, but the fact that very little of a role's identity is preserved is concerning. Most of the time, when I speak to data scientists, the core reply I get is that they are fine-tuning models to preserve their "muscles". But fine-tuning is a very small part of a data scientist's role; heck, after a point, it's not even the most important part. Fine-tuning is a tool. Understanding, I believe, should be the fundamental block of the role. Realising that there are things other than "transformers" and finding where they fit into the picture. And don't even get me started on the lack of understanding of how important the data is for their systems. A data scientist's primary role is not the model itself. It's about developing the model, the data quality at hand, the appropriate problem framing, efficiency concerns, architectural literacy, evaluation design, and error analysis. Amid the AI hype, many have overlooked that much of their role is static and not considered important. AI engineering is an amazing field. The folks who love doing amazing things with the models always inspire me. But somehow, the same attention and respect are no longer paid to the foundational, scientific side of data and modeling in the current industry. I realise it's not always black and white, but it's kind of interesting how the grey is slowly becoming darker by the day. Do you feel the same way? Or is it just my own internal crisis bells ringing unnecessarily? For those of you who have recognized this shift, how are you handling your careers? Are you leaning into the engineering/systems side and abandoning traditional model development? Or have you found niche roles/companies that still value the fundamental data scientist role (data quality, architectural literacy, statistical rigor)? I'd love to hear how you are adapting submitted by /u/The-Silvervein [link] [comments]
- What has been people's experience with "full-stack" data roles?I started my career being a jack of all trades - hired as a data analyst but I had to extract, clean, and then analyze data and even sometimes train models for simple predictions and categorization. That actually led me to become a data engineer but I've spent most of my career working closely with data scientists and trying my best to make their jobs easier by taking away all the preprocessing tasks away from them so they can focus on training, inference MLops, etc. While I claim to have helped them, to be honest DE teams often become a bottleneck and an obstacle. Everything from not being able to provide the data needed to train on time, or how we processed the data was wrong and led to bad performance, or they went live with a model blindly because we couldn't get them the observation data on time for them to analyze accuracy. I'm wondering how much of the data engineering tasks can be automated/vibed away by data scientists. My guess is that in larger companies this won't be the case but I think startups and SMBs want to move fast so they'd rather have data scientists own the whole pipeline. What has been other's experience with this and where is it heading? submitted by /u/uncertainschrodinger [link] [comments]
Tagged with
#generative AI for data analysis#Excel alternatives for data analysis#natural language processing for spreadsheets#financial modeling with spreadsheets#rows.com#big data management in spreadsheets#conversational data analysis#real-time data collaboration#intelligent data visualization#data visualization tools#enterprise data management#big data performance#data analysis tools#data cleaning solutions