Analogies are very helpful for explaining ideas. I was recently talking to somebody from my boxing gym about coding interview prep. I explained the stages of preparation in terms of different boxing stages (shadow boxing, pad work, sparring, fights, etc). This analogy helped my friend understand the process, and even made the prep more fun for him (his words). In this article, I will share my experiences with Brazilian Jiu-Jitsu, A.K.A Murder Yoga.

Have you ever thought your product's progress was headed in one direction, and been shocked to see a different story reflected in big picture KPIs like revenue? It can be confusing when customer feedback or metrics like registration or retention are painting a different picture. No matter how sophisticated your analytics are, if you're asking the wrong questions - or looking at the wrong metrics - you're going to have trouble getting answers that can help you. Join Nima Gardideh, CTO of Pearmill, as he demonstrates how to build a strong data culture within your team, so everyone understands which metrics they should actually focus on - and why. Then, he'll explain how you can use your analytics to regularly review progress and successes. Finally, he'll discuss what you should keep in mind when instrumenting your analytics.

Despite what mainstream media wants to led us to believe, the current state of Artificial Intelligence is not yet capable of replacing humans.

I've been a student of Machine Learning for the past two years, but this past year was when I finally got to apply what I learned and solidify my understanding of it. So I decided to share 7 lessons I learned during my "first" year of Machine Learning and hopefully make this article an annual tradition. Nowadays, it is relatively easy to learn about Machine Learning thanks to the vast selection of learning resources that exist online. Unfortunately, many of them tend to gloss over the data collection and cleaning steps. During my first serious Machine learning project, my team and I run into the BIG question of where do we get our data from?

In the business intelligence (BI) world, more and more companies are talking about machine learning (ML) being leveraged in their software. However, try to talk to them about it and there is silence. Infrastructure vendors, IBM, NVIDIA, Intel, Oracle (remember, that's where Sun went), Qualcomm and more are talking up their chips for ML. Again, try to talk with them about a real business case study, a customer who has implemented a system, and, if you get back anything, you get back anonymous companies described in a paragraph or even just a sentence. On the other hand, the success of Apple Siri and Amazon Echo, the continued growth of Microsoft Cortana Echo, and the entrance of Google Now show that voice recognition is rapidly becoming mainstream in the consumer world.