madenine

Enterprise Sales, Support, Training, Certifications, Deals for packing services/software/etc, Incentive programs, Extensive partner network, etc, etc, etc.

There's an entire realm of software development and consulting in and around Microsoft enterprise products - and that has extended to Azure as well.

Personally, I've worked with AWS,GCP and Azure in a professional capacity (data science consulting) - and while Azure has its fair share of warts, the level of integration and "it just works" between Azure and MS Enterprise products is fairly impressive.

For example, I'm big on postgres - but working with SQL Server on Azure is such as breeze. There's nothing I can do with it that I couldn't do on other platforms, but the combination of tooling (SSMS, VSCode) and smooth azure integration (Blob Storage, AzureSQL, Logic Apps, Azure Functions, etc) made it easy to make things that 1. worked and 2. could be mostly supported by a BA instead of a dev.

I delivered food while in college before the rise of delivery apps and it was _awful_. Even with grubhub - each restaurant was responsible for hiring and managing its own delivery staff.

If the restaurant I was delivering for wasn't busy? I made bad less money (the people I worked for were pretty solid; you made tipped hourly + tips or non-tipped hourly during the shift, which ever was greater). If it was too busy I would be run ragged and my tips would go down as it took longer to complete orders.

To make more and more consistent money, some friends and I would 'game the system' to a degree and each be working 1-2 restaurants in the same business district at the same time. We would trade orders back and forth based on where we were going to make trips more efficient and split the payout pretty much evenly (if someone had a hard delivery or something, they might be given a bit extra).

If the restaurants knew we were doing this they would probably have fired us.

My shifts were fixed; ie I had the sunday evening and tuesday evening shifts every week. I was at the whim of overall demand; slow nights means less money. Most importantly; I could be fired.

The best office I ever worked in was an open office... with an open remote work policy. On any given day, only 15-30% of the office would be in; meaning while the office was "open", it was also usually empty and quiet.

People came in when they had in-person meetings, and teams generally fell into a loose schedule to come in on the same days 1-2 times per week.

I thought it provided a good balance; the company need to have a huge space to give people private offices etc, and it was quiet even if someone nearby was on a call.

Glad to hear you're working through it.

At Metis, state and federal regulation (we're accredited) is a big burden - but something I as an instructor feel is in the best interest of my students.

Cool, but I would rather use a better supported static site generator with an ipynb plugin (ie, https://github.com/danielfrg/pelican-ipynb) than a tool just for notebooks.

Especially because this looks like an nbconvert workflow under the hood.

There’s a misconception out there about the data science skills gap - the truth is there is a huge demand for highly skilled data scientists, a big demand for data and ml literate developers, and a moderate demand for entry level data scientists.

These resources from google and courses like Fast AI are great for getting devs up to speed so they can meaningfully contribute to data science projects - filling that big demand for data + ml literate devs, especially internally. They’re not designed to get people jobs (disclosure, getting people jobs in data science is what we do at thisismetis.com)

If you want to go deeper? The open source data science masters is a good set of resources[0]. The first few sections of Goodfellow’s deep learning book are a great crash course in ML math/stats theory[1]. Introduction to Statistical Learning is a staple in most people’s library[2]. There’s a glut of intro level data science content out there on the internet, but intermediate to advanced stuff usually means putting in serious effort or breaking out your checkbook and going back to school (whether traditional or otherwise).

[0]http://datasciencemasters.org/ [1]https://www.deeplearningbook.org/ [2]http://faculty.marshall.usc.edu/gareth-james/ISL/

Cool stuff, but did anyone else feel like they were reading an advertorial for the author? Managed to pack most of their CV in there.

I don’t think firing two employees for “poor performance” after they voiced concerns internally as an ‘appropriate’ reaction.

I don’t see responding to a letter from ~100 of your content producers with a legalese blog post purposely hidden from search engines as ‘appropriate’

People do shitty things; that doesn’t have to reflect on the organization that employs them unless the org decides to do shitty things as well.

They're not even the largest manufacturer of car batteries.

>It would be accurate to say that Tesla is the largest manufacturer of car batteries

Pretty sure that title goes to Johnson Controls, who produces about 1/3rd of all car batteries globally.

Just like if you were going to learn Python for Deep Learning, you're going to need 3 things:

1. Understanding of Deep Learning

2. General familiarity with the language

3. Understanding of packages/tools that will help you accomplish your goals. If this were python we'd be talking Numpy/TensorFlow/PyTorch/Keras/etc.

We're talking C++, I'm guessing you'll want to work with less abstraction than TF and the like, so BLAS, LAPACK, and maybe the whole CUDA family (CUDA, cuBLAS, cuDNN, etc).

Otherwise, TensorFlow, Caffe, CNTK, and other popular deep learning frameworks are available for C++, even though they might be better known and more heavily used in other languages.

I travel a lot for work with my X1 Carbon (7th gen i7), and have had no issues.

I also have a t460s (current version is the t480s) which is also pretty decent and went through a ton of travel with no issues.

Outside the bay area, 95-120 is about the going rate for someone with those skills. Add a little for the bigger cities, and maybe 10k for every 3-5 years of experience.

The market for serious data scientists is growing, but is not as dire as these articles make it seem.

The private sector has wrapped 'senior data analyst','BI developer','data base administrator', and 'data engineer' into 'data scientist', along with the ML/AI/stats roles you would expect.

So sure, when you put all the roles that touch data into one basket, and we're in a world that demands increasing familiarity with data systems and data analysis techniques... a gap is going to appear. Color me shocked

Typical data scientist role I see posted is looking for: - SQL - AWS/Azure - Tableau/Power BI/Other BI tools - Python/R - Increasingly Hadoop/Spark

With Java/Scala/C#/C++ listed as 'nice to haves' along with 'machine learning' and 'big data'. These are not research roles, and while they often want a masters degree or more they really don't need them.

If you fit that bill you can probably get a role paying $95-120k just about anywhere in the US. You'll wrangle data, make pipelines, build dashboards and occasionally deploy a model or two when the planets align.

If you want to/are capable of doing more ground breaking/cutting edge work, these roles will crush your soul in 8-12 months.

We do this with old kindle fires. Works OK

Its not the bottom we should be worried about, its the middle.

AI is going to outright replace a whole bunch of what are currently decently paying, stable, office jobs: accountants, clerks, schedulers.... and AI-augmentation is going to allow 1 person to do the same work that currently requires 2+ in so many fields.

Law and Medicine, two fields that have historically been good pathways to the middle class, are likely going to see AI hit them first in the middle, with the need for non-senior/non-specialized doctors/lawyers dramatically reduced as AI-assisted tools become standard.

As the middle gets cut out, more people are pushed towards lower-skill jobs that require physical interaction, likely pushing down wages in a buyer's market and decreasing the incentives for robotic automation.

That's going to be a massive blow to the US and global economy, all without having to build a single robot.

>I wonder if the rapid growth of ML recently has diluted the reviewer pool dramatically?

There was a blow up a couple months ago around this issue. Someone posted to r/machinelearning saying they were starting grad school in the fall, and had been accepted as a reviewer for NIPS2018.

Twitter convo about it [0] generated some press, with the pull quote of "It's 'peer review', not 'person who did 5 TensorFlow tutorials review'".

[0]https://twitter.com/yoavgo/status/996021724188725248

ctrl-f "softmax"; 0 results

The paper referenced in the article uses KNN on metadata features to re-identify users from their tweets. They found that account creation time (ACT) alone could get them 97% accuracy.

All they did was exchange username for ACT as a unique identifier; and then found that its only ~97% unique.

41% isn't 'rare' [0]. Aside from protecting your stuff in the event of disaster, the real value of rental insurance (must like car insurance) is liability protection in the event that something bad happens to someone else. Policy Genius has a good overview of what renters insurance does and doesn't normally cover [1].

Renters insurance is cheap, especially compared to the value of your stuff, the cost of living in a hotel for an extended period of time, or paying someone's medical expenses after an accident.

[0] https://www.iii.org/fact-statistic/facts-statistics-renters-... [1] https://www.policygenius.com/renters-insurance/learn/what-do...

The advantages of Postgres (and MariaDB) over MySQL are pretty well traveled on HN. OP wants to move from Redshift Postgres & SQL Server to MySQL - which isn't something you see very often.

Disclaimer - taught GA part-time courses (data science)

My heart goes out to those people, and I'm guessing Web Development Immersive students?

I always described GA's programs as a multiplier in that your outcome is going to be weighted by your experience going in. And for the most part I felt GA managed that expectation fairly well for most of its immersive programs - except Web Development.

In WDI I would see students who had no background in CS, design, etc. hoping to go from no experience to a full-stack developer position in 12 weeks. With a broad curriculum that iirc was starting with html/css fundamentals and working through Rails/Node/... maybe Angular? I felt it was giving them a base to start learning on their own, but not enough to immediately start working except for the very-top students (who likely had some sort of helpful background coming in).

I do believe GA is at its core a good organization that wants to see its students succeed (can't say enough good things about the career coaching / placement staff aka "outcomes"). But I can see how with a lot of competition in that space, the sell may have gotten stronger than what the product supports - and that's 100% on GA to address.

Little bit sensationalist.

Microsoft seeing profitability and growth from its cloud business, and investing resources accordingly, doesn't mean its "putting Windows on the back burner".

In its cloud and enterprise offerings MS has been adding support Linux and open source in general. That mentality shift away from "MS products are for Windows OS" is more what's going on than "MS doesn't care about Windows anymore"

The time to take on Adobe was probably 10-15 years ago. At this point to oust them or even try to compete with them you would have have to create a feature for feature better product, and offer it at a lower price point. Your interface would either have to mimic adobe's to ease transition or be a significant improvement that its beneficial to switch.

As far as dialed down "40% functionality" - isn't that what Adobe tried with their "Elements" line dialed back versions of Premier/Photoshop at a lower price point?

I get the feeling it would be hard to roll out a "40%" product without a "100%" as well - or else you risk having the narrative around your product becoming "use this until you start taking yourself seriously - then learn a whole new software suite when you graduate to Adobe"

Idk. Lack of competition isn't great, but back when others were actively trying Photoshop was still king (thinking back to the 4.0/5.0 days)

Give you the benefit of the doubt and assume that last bit was sarcasm?

I find that most of our family 'secret' recipes are the result of trying several recipes for a given dish, picking the best one but incorporating lessons learned from the others. Its not uncommon for a page in our family book to consists of a photocopied page from a published cookbook + notes like "fully mix in the spices 1 at a time in this order:" where the recipe just says "mix in the spices".

Additional notes like "[cousin] likes this with more cinnamon, but [uncle] will always try to add more sugar" are helpful for holidays.

The last paragraph of the piece kinda struck me:

"And perhaps if others saw more realistic portrayals of poverty in the movies, they might be more receptive to programs that would help them instead of demeaning and punishing them."

I would love an example where 'realistic portrayals' of a given subject in movies led to changes in public policy / public perception.

If anything, I would argue that popular films, just like other art forms, are more likely to serve as a barometer of society's perception/reception of a given subject than as something that ignites the change.

Take the war in Vietnam in the 60's and 70's - I'm not sure you could make 'The Deer Hunter' in '68, or 'The Green Berets' in '78.

Or the war on drugs - movies like Trainspotting, Requiem for a Dream, or Traffic certainly showed a realistic side of drugs and addiction... and yet the war on drugs has only intensified since their popular releases.

simple linear classifier... I'm guess logistic regression? And yes, neither would be a black box.

"The fact remains that while Kahana’s system can improve word recall in specific circumstances, he doesn’t know exactly how it’s improving function. That’s the nature of machine learning."

Seems like its also the nature of electro-stimulus to the brain.

Is the real story here in ML/AI, or in advances regarding 'when is it helpful to shock your brain a bit vs when is it not'?

This is a must have, mostly because it serves as the best documentation / user guide for PyMC - a fantastic stats package that can be a little tricky to get started with.

Support great work like this and buy the book!