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"How do you get started in enterprise Java development? What is the best way to gain experience without giving up on the current day job that pays the bills?"

"How do you get started in enterprise Java development? What is the best way to gain experience without giving up on the current day job that pays the bills?" Find a small business that needs a web app and build it for them using your favorite technology stack. That's how I started. Show your interviewers the code - shuts off a lot of stupid gotcha/trivia questions. (due disclosure: Though I worked on enterprise dev for 10 years I haven't done it in the last 8 years or so. I got completely bored and the projects all started to blur into each other and so I shifted to algorithm intensive AI/machine learning sw. But it was kinda fun for a while and I worked with some great people and companies. I recently talked to some of my ex colleagues and nothing much has changed in enterprise dev.)

gcheong 17y ago
How did you make the shift from doing enterprise dev to AI?
- plinkplonk 17y ago
  I got good at mathematics. IMHO, This is the key step in making the shift from enterprise dev to ML. This is also where I've seen many people making the attempt give up. I also worked through some AI/Machine Learning books. I started with Norvig and Russell's AIMA and open sourced resulting Java code (yes it is a beginner's book and not tough at all, but it is very well written and a great starting point). Then I worked through more narrowly focuses books (and there are plenty of nice books see lists below). I got involved in the local ML community (mostly defence labs - Bangalore doesn't have many cutting edge startups/ sw companies yet). The combination of deep math and good programming opens many doors (as Bradford Cross and others point out elsewhere - see Brad's post at http://measuringmeasures.blogspot.com/2010/01/learning-about-statistical-learning.html http://measuringmeasures.blogspot.com/2010/01/learning-about... for a list of resources . I wrote a supplementary blog post on the books I found useful (I have a somewhat different focus from Bradford and so ended up working through a different set of books) at http://pindancing.blogspot.com/2010/01/learning-about-machine-learniing.html http://pindancing.blogspot.com/2010/01/learning-about-machin...). It helped that I didn't know I was not supposed to do such "crazy" things in my old age and (perhaps rather naively in retrospect) didn't doubt for a second I could teach myself anything I wanted to. I have zero academic background comp sci/math/ML etc. My proudest moment was when a senior researcher I respect highly at first refused to believe I had no formal education in ML and, when I maintained my position, said " Well if you don't have a PhD you should think about getting one. I never met anyone who levelled up as fast as you. If you ever need a reccomendation for a PhD I'll be glad to give you one". And this was someone with a few dozen credentialed junior scientists in his team (some of whom were majorly pissed off that someone with no academic credentials was in a position to tell them what they were doing wrong and what to do next but that is another story for another day). Well enough reminiscing for today. Back to work. My current project involves Computer Vision and Robotics. Quite a change from the Leasing System From Hell (Old time ThoughtWorkers will get the joke! ). But I am tackling tough problems, learning new things every day and often "going where no one has gone before" programmatically. Life is good.

gcheong 17y ago

How did you make the shift from doing enterprise dev to AI?

plinkplonk 17y ago
I got good at mathematics. IMHO, This is the key step in making the shift from enterprise dev to ML. This is also where I've seen many people making the attempt give up. I also worked through some AI/Machine Learning books. I started with Norvig and Russell's AIMA and open sourced resulting Java code (yes it is a beginner's book and not tough at all, but it is very well written and a great starting point). Then I worked through more narrowly focuses books (and there are plenty of nice books see lists below). I got involved in the local ML community (mostly defence labs - Bangalore doesn't have many cutting edge startups/ sw companies yet). The combination of deep math and good programming opens many doors (as Bradford Cross and others point out elsewhere - see Brad's post at http://measuringmeasures.blogspot.com/2010/01/learning-about-statistical-learning.html http://measuringmeasures.blogspot.com/2010/01/learning-about... for a list of resources . I wrote a supplementary blog post on the books I found useful (I have a somewhat different focus from Bradford and so ended up working through a different set of books) at http://pindancing.blogspot.com/2010/01/learning-about-machine-learniing.html http://pindancing.blogspot.com/2010/01/learning-about-machin...). It helped that I didn't know I was not supposed to do such "crazy" things in my old age and (perhaps rather naively in retrospect) didn't doubt for a second I could teach myself anything I wanted to. I have zero academic background comp sci/math/ML etc. My proudest moment was when a senior researcher I respect highly at first refused to believe I had no formal education in ML and, when I maintained my position, said " Well if you don't have a PhD you should think about getting one. I never met anyone who levelled up as fast as you. If you ever need a reccomendation for a PhD I'll be glad to give you one". And this was someone with a few dozen credentialed junior scientists in his team (some of whom were majorly pissed off that someone with no academic credentials was in a position to tell them what they were doing wrong and what to do next but that is another story for another day). Well enough reminiscing for today. Back to work. My current project involves Computer Vision and Robotics. Quite a change from the Leasing System From Hell (Old time ThoughtWorkers will get the joke! ). But I am tackling tough problems, learning new things every day and often "going where no one has gone before" programmatically. Life is good.