It sounds more like the team is rather inexperienced for that kind of work environment. Communication, the overarching priorities and alignment are super important. I work for a German insurance company and we rolled…
That's because there are other legal reasons to keep that kind of data. But that isn't a reason to keep all kinds of data related to a user account. Monetary transactions, claim data for insurances, but not support…
That might be true for startups. But larger business organizations are far better of creating a specific heterogeneous team with data scientist, data engineer and ops in one. At least starting out. That way, there is…
I agree and disagree. We deploy our models via Cloud Foundry which has support for Anaconda. Model building is done in AWS with access to Anaconda. Usually we have an environment.yml for the REST API and one for model…
For instance: a lot of us want one cable to connect display, keyboard, mouse, power to the laptop (aka some device)?
> > (1) all employees are trusted and unmeasured, but you have to tap people on the shoulder every once in a while to confirm that they're on track. Naturally, this is easier if everyone is on-site. > I don't get it -…
But then the question becomes what generation +1 wants? This is probably going to be nearly as big a problem as the engineering one.
The problem with these models is that you have to be careful that your are not modeling something that incorporates a trend. And if you have any kind of seasonality you a dataset with a large enough timeframe. (At least…
Very nice photos! How did you handle food, money etc? But honestly, I would probably be too chicken shit to do a tour like that.
There are also companies like The Flow that offer that data directly from mobile devices via an app.
SAS ist still quite a big thing in the insurance sector, at least in Europe. But the more Data Science focused roles (vs. Pure actuarial roles) are going more and more with R or Python.
It sounds more like the team is rather inexperienced for that kind of work environment. Communication, the overarching priorities and alignment are super important. I work for a German insurance company and we rolled…
That's because there are other legal reasons to keep that kind of data. But that isn't a reason to keep all kinds of data related to a user account. Monetary transactions, claim data for insurances, but not support…
That might be true for startups. But larger business organizations are far better of creating a specific heterogeneous team with data scientist, data engineer and ops in one. At least starting out. That way, there is…
I agree and disagree. We deploy our models via Cloud Foundry which has support for Anaconda. Model building is done in AWS with access to Anaconda. Usually we have an environment.yml for the REST API and one for model…
For instance: a lot of us want one cable to connect display, keyboard, mouse, power to the laptop (aka some device)?
> > (1) all employees are trusted and unmeasured, but you have to tap people on the shoulder every once in a while to confirm that they're on track. Naturally, this is easier if everyone is on-site. > I don't get it -…
But then the question becomes what generation +1 wants? This is probably going to be nearly as big a problem as the engineering one.
The problem with these models is that you have to be careful that your are not modeling something that incorporates a trend. And if you have any kind of seasonality you a dataset with a large enough timeframe. (At least…
Very nice photos! How did you handle food, money etc? But honestly, I would probably be too chicken shit to do a tour like that.
There are also companies like The Flow that offer that data directly from mobile devices via an app.
SAS ist still quite a big thing in the insurance sector, at least in Europe. But the more Data Science focused roles (vs. Pure actuarial roles) are going more and more with R or Python.