I’m going to start this post with a big helping of gratitude. I’m so grateful to the 1000’s of readers that helped me get into the field of Data Science and Machine Learning. I was a part-time blogger that turned my passion into a full-time gig in the AI Startup world. This post is my way of paying it forward to you. Read below if you want to learn how to make money blogging.

I was once told that the best time to start a business is in a recession. The reasoning behind it is that the business would get stronger once the recession is over. Essentially you’re starting at the bottom and can only go up from there. From my experience, this is true. I’ve started several businesses over my life and have closed several businesses too. Some were better than others too. I (we) still have our real estate business but it’s morphed more from a real estate investing business to a holding company.

It’s been a long time since I wrote anything related to Real Estate (RE), especially about flipping houses. I heard on Bloomberg yesterday that the real estate flippers are exiting the market as fast as they can because the real estate market is starting to slow down. That’s interesting news because of my personal history with RE. I have been a real estate investor that wanted to be a flipper but got caught in 2007 slowdown.

I watched Mike Gualtieri’s keynote presentation from H2O World San Francisco (2019) and found it to be very insightful from a non-technical MBA type of way. The gist of the presentation is to really look at all the business connections to doing data science. It’s not just about the problem at hand but rather setting yourself up for success, and as he puts it, not getting fired!

This is my third startup. Or maybe my fourth, I’m not sure but it gets hazy after a while. What I do know is that if I ever do another startup, I’ll use open source to make it happen. Let me explain with a bit of back history. My most recent startup was a consulting business that I started in mid 2017. I had just resigned from another startup in the machine learning space and with their software I did Data Science and Engineering consulting for a handful of clients.

While VC’s are a necessary part of growing a startup, they can kill it too. I’ve seen this happen too many times where the VC’s kill the goose that laid the golden egg. They’re so driven by financial goals that they interfere with great startups that just need a bit more time to ‘gel.’ A recent study commissioned by Eric Paley at Founder Collective found that by pressuring companies to scale prematurely, venture capitalists are indirectly responsible for more startup deaths than founder infighting, technical debt and slow customer adoption — combined.

One of the big corporate strategy things I worked on was developing and putting into production a PQL model. It was essentially a propensity to buy model that analyzed usage patterns on the RapidMiner software platform and bucketed new downloaders into those that were likely to buy or not buy. It was incredibly successful and helped the sales team focus on thier leads better. My former colleague Tom shares his thoughts on it since it’s been in production for over a year now.

An important lesson I’ve learned while working at a Startup is to do more of what works and jettison what doesn’t work, quickly. That’s the way to success, the rest is just noise and a waste of time. This lesson can be applied to everything in life. Data is your friend We generate data all the time, whether it’s captured in a database or spreadsheet, just by being alive you throw of data points.

This is just nuts. Millennials just can’t seem to catch a break. Now AI is coming for their jobs. Research released by Gallup on Thursday indicates a collision between technology and “business as usual” is coming soon, and the fallout will be ugly, especially for Millennials. Automation and artificial intelligence (AI) are among the most disruptive forces descending upon the workplace, says the Gallup report, and 37% of Millennials “are at high risk of having their job replaced by automation, compared with 32% of those in the two older generations.

ICYMI the Startup markets are getting hotter in the Data Science space. Every time I turn around, some small company got millions of dollars in startup funding. It used to be a company with an algorithm or data science library but now it’s Data Science platforms. These platforms aresuddenly all the rage and many new entrants are racing to gain market and mind share. The above image from FundersandFounders.com really captures a successful startup from inception to IPO.

I’ve been following Howard and Fred on and off over the years. They’re old Web 2.0 veterans like me and have kept a blog going for over 10 years. It’s always nice to see continuity and recently I found a great interview of Fred at MIT on Howard’s blog. It’s just under an hour and gives advice to students interested in the Venture Capitalist (VC) field. CheerleadersWhat caught my ear (and Howard’s too) was how his wife (Gotham Girl) is his biggest cheerleader.

I recently read a great article on the Freemium model that resonated with me. It kept me thinking for several days afterwards about how some startups with freemium models “made it” and how others failed. The difference between success and failure has become a very fine line indeed. I completely get it, it’s all about how many users your product or service has. The rationale has always been that more users == more revenue and many startups created wonderful free products (i.

First off this is not a political post but I will come out and say it. Make America Great Again is a stupid slogan. I admit there are problems with the middle class but America is and will continue to be great. Why? Because of what I wrote two years ago. It’s as relevant today as it was back then. Startup Land Two years ago I wrote a LinkedIn article on a train ride home titled “I believe in America.

Brian tweeted an interesting link the other day about a new trend that HBR picked up on. It was about newly minted MBA graduates buying existing businesses instead of going into right into the consulting world. At the Harvard Business School, for example, the number of MBAs who decide to look for a business to acquire right after graduation has gone from less than a handful a decade ago to more than a dozen, and in an occasional year, twice that amount.

I’m completely boggled by the vast amounts of money that’s looking to be invested. It’s been finding homes in Startups from AirBnB to [Domo][3] and Uber. The money raised by these Startups is so insane that Fortune dubs them unicorns. The king of the Unicorns is Uber. Uber is ranked #1 on the list with a valuation of $62 Billion dollars. Yes you read that right. 62 freaking billion dollars! How is that even possible for a taxi service?