"I was a happy data scientist until we decided it was time for deploying our models." It is common among many DS/ML teams that when the time for productionizing the model comes, they are caught off guard due to poor planning. Of course, thinking solely about the end is far from enough, the stages beforehand are equally as important. To reach the end of any endeavor we need to be strategic, the same applies for succeeding with MLOps. One such strategy is to take on a less challenging problem or part of it in the beginning and find the easiest way it can be solved.

We have to figure out the "features" of the secret formula that generated the data we were given, so that we can learn them. In my opinion, this is like 80% of the complexity of solving an ML problem. In this example, we were told the shape of the secret formula (it's a cubic!), so the features we have to learn are the coefficients in the polynomial. For something more complex like the "is this a dog or a blueberry muffin" problem, we'd have to look at pixels and colours and formations and what makes a dog a dog and not a muffin. Once we figure out these features (in our case, those a,b,c,d coefficients), we initialize them to some random values.

How Does Radical Innovation Come About? Last year, in researching my upcoming book, THE NEW SCIENCE OF RADICAL INNOVATION, I interviewed several executives from successful tech companies and discovered that they share a signature pattern in how they manage their organizations. I told one of those I interviewed, in watching the historic Go match between AlphaGo and Lee Sedol, I had been struck by how the artificial intelligence (AI) seemed to use the same principles many of these successful Silicon Valley tech giants were using to induce innovation--self-organization, simple rules, a generalist approach, diversity of input, speed of execution, and profuse experimentation (I gave a TEDx talk on this topic and wrote an article about it). I asked him if his company implemented these principles by design or coincidence. His reply was that it was not by design. However, "we very much preach focusing on data, and we try to make sure that people don't have inherent biases in their decision-making.