adamwi

Nordax Bank | Date warehouse architect | REMOTE (EU) or ONSITE Stockholm Sweden | Full Time | https://www.nordaxgroup.com/

Nordax is a niche bank that operates in Europe with the main focus on the Nordics together with our subsidiary Bank Norwegian. Together with Bank Norwegian we have roughly two million private customers in seven countries, our product offering include deposits, mortgage loans, credit cards and private loans. We are growing quickly by challenging incumbent banks through an efficient digital customer interface (no branch offices) and by innovating with new financial products (e.g., filling underserved niches with new types of mortgage loan products).

We have one central data warehouse team that serve the organization with data. The team is currently expanding with more team members and rebuilding the data warehouse to meet the needs of the growing organization, so it is a good time to join in a senior technical role.

Nordax operates out of Stockholm with one office for all our employees, our subsidiary Bank Norwegian is based out of one office in Oslo. We offer a hybrid work model with part time in the office or fully remote for the right candidate.

More information and application form: https://career.nordax.com/jobs/2072715-dw-architect-to-norda...

Seems like Avanza competitor Nordnet is fairly transparent about loaning out your stocks. On by default for some account types (when the stocks are not in your name, e.g. KF account type). They will give you a cut of the gains. You can chose to opt out.

https://www.nordnet.se/se/marknad/aktier/aktielaneprogrammet

Sry for the confusion my bad, too late to edit unfortunately. As pointed out this is the link to the OP post that was missing initially as well.

Nice write up, really enjoyed it! Technical question, which approach did you use to create the formulas/equations?

Great work!

An alternative I have been using recently for some projects is Molnify [1], it has more focus on exposing calculation logic in the app created and less control over background images and layout. Takes either a Excel file or a google sheet doc as input.

[1] http://www.molnify.com/

Really brings you back to the good old days =) Even got the help topics exactly as in the original

Alternative for automated security checks, https://detectify.com/.

We run a collaboration service [1] on AWS, nothing fancy in terms of infrastructure needs (DB, some EC2 instances and S3). We spend around 500 USD per month, but our largest customers run run our service on prem on their own servers.

[1] www.qlutter.io

Love how they use the terminology "neural lace" from the Culture novels by Iain Banks. But I guess it goes in line with naming the Spacex drone ships after space ships in the same series =)

https://detectify.com/ Automatically scans for vulnerabilities (including owasp top 10 mentioned in the previous comments). Not a knowledge resources as you were looking for but solve the same problem with less work for you =)

17 000 000+ edits in total, https://sv.wikipedia.org/wiki/Anv%C3%A4ndare:Lsjbot.

Important topic but not much details or examples in the linked material. Any tips on other sources of content on the topic of building mindshare? Working in the Saas B2B space but any examples are interesting.

Always fun with people that run with exotic ideas =)

I might be missing some aspects but would it not be much easier to locate the datacenter on land but close to the ocean (or other large body of water), with similar principles as used for cooling nuclear reactors? Then you would get the benefits but would not have the same strict requirements on reliability of the components.

If you locate it on land you would also have the benefit of using the excess heat for house heating (if located in cold climate and the temperature difference to outside is big enough). This is done in a number of cases in Sweden with excess heat from large industrial plants [1].

[1] Report in Swedish; http://www.svenskfjarrvarme.se/Global/Rapporter%20och%20doku...

Interesting, first of all fully agree with you regarding the business side. Always need to have the customer, their problems, and the revenue model in mind. E.g. for the health care example I think there is a big upside from the insurer side to able to proactively identify illnesses and treat them early (typically cheaper than emergency care, but not always), not to talk about minimizing the human suffering.

But back to the actual question, rules of thumb to estimate feasibility of machine learning application without having access to a actual data set for the specific problem. Make sense to break it down in different problem domains as you mention, NLP, words, image classification, etc.

The 10,000 examples is for bag of words is something I will keep in mind going forward, thanks! When it comes to image classification I guess a fairly good benchmarks can be achieved by looking at available image datasets and public models built on top them (e.g. ImageNet and later versions) and then extrapolate on the precision and number of images needed to achieve it (assuming similar image datasets).

Anyone aware of other relevant rules of thumb for other problem domains?

Very interesting read that nuance the challenges with Facebook's approach to news, at least for a naive European like me.

I agree with the overall article, but it mention that no self organising communities succeed in policing itself, I guess Wikipedia is good counter-point.

What resources would you recommend to get started for someone new to Docker? Given that it changes quickly it would be great to have recommendations from someone working with it.

Video that gives a better feeling of the generated model https://vimeo.com/189332811