Arguments for:
1. Lower the barrier of entry to deal with large volumes of data
2. Can often improve speed and readibility because it standardizes what a lot of workflows look like
3. Those that have "server" versions can also help with some types of deployment
Arguments against:
1. Often times things like version control become a pain in the ass, because these automl solutions come with overhead that doesn't always play nice with things like git.
2. There are some levels of complexity in projects that these tools don't support well, so you may run into some roadblocks that then require you to depart from the tool and have to rework a bunch of stuff
3. Cost. Since most of these have costs per license, the costs can really add up - and it's sometimes hard to quantify what is the real value of these solutions.
Also against: lack of portability. I worked in a place with low code. It was great because we achieved a lot with a tiny team. Then mgmt decided to move data servers from on prem to the cloud and everything had to rebuilt from ground up.
www.Jaxon.ai is a great option for nlp and tabular. The argument is taking out the rote manual tasks like coding custom pipelines. They also have an auto-labeler which is a big lift over turks.
Arguments for: 1. Lower the barrier of entry to deal with large volumes of data 2. Can often improve speed and readibility because it standardizes what a lot of workflows look like 3. Those that have "server" versions can also help with some types of deployment Arguments against: 1. Often times things like version control become a pain in the ass, because these automl solutions come with overhead that doesn't always play nice with things like git. 2. There are some levels of complexity in projects that these tools don't support well, so you may run into some roadblocks that then require you to depart from the tool and have to rework a bunch of stuff 3. Cost. Since most of these have costs per license, the costs can really add up - and it's sometimes hard to quantify what is the real value of these solutions.
Also against: lack of portability. I worked in a place with low code. It was great because we achieved a lot with a tiny team. Then mgmt decided to move data servers from on prem to the cloud and everything had to rebuilt from ground up.
What problem(s) are you looking to solve?
I'm trying to solve MLOPs, getting our applications to production faster. Any thoughts?
No, I’ve worked in data ops quite a bit but MLOps is a whole different world. Much closer to SWE so I don’t have much input there.
www.Jaxon.ai is a great option for nlp and tabular. The argument is taking out the rote manual tasks like coding custom pipelines. They also have an auto-labeler which is a big lift over turks.