"The future is much like the present, only longer," said baseball pitcher Dan Quisenberry. Here are some predictions for the future of marketing in the year 2019 and beyond from some thought leaders I polled, plus a few of my own prognostications. Here are predictions from digital growth marketing expert and speaker Lisa Apolinski, owner of 3DogWrite, who I met this year when she asked me to help edit her upcoming manuscript. Let's face it, you don't need psychic powers to know that marketing will become more digital. That means more live streaming, more personalized video messaging, and more curated content with enhanced relevance to micro niches.
The last decade has seen a growing interest in solver portfolios, automated solver configuration, and runtime prediction methods. At their core, these methods rely on a deterministic, consistent behaviour from the underlying algorithms and solvers. However, modern state-of-the-art solvers have elementsof stochasticity built in such as randomised variable and value selection, tie-breaking, and randomised restarting. Such features can elicit dramatic variations in the overall performance between repeated runs of the solver,often by several orders of magnitude. Despite the success of the aforementioned fields, such performance variations in the underlying solvers have largely been ignored. Supported by a large-scale empirical study employing many years of industrial SAT Competition instances including repeated runs, we present statistical and empirical evidence that such a performance variation phenomenon necessitates a change in the evaluation of portfolio, runtime prediction, and automated configuration methods. In addition, we demonstrate that this phenomenon can have a significant impact on empirical solver competitions. Specifically, we show that the top three solvers from the 2014 SAT Competition could have been ranked in any permutation. These findings demonstrate the need for more statistically well-founded regimes in empirical evaluations.
In today's edition of "Coffeehouse Connect" we take a look at a major predictive event in the United States that occurs each year on February 2. It occurs in the chilly time of the year aroundCandlemas, a midpoint between the winter and spring. On this day we await the prediction of Punxsutawney Phil – a 20 pound prognosticator from Pennsylvania. Prediction is a mark of the human condition. Why are there so many cases of people commonly mistaking correlation with causation? There is a burning desire for us humans to know the future.
Google's Cloud Machine Learning Engine enables let's you create a prediction service for your TensorFlow model without any ops work. Get more time to work with your data, by going from a trained model to a deployed, auto-scaling prediction service in a matter minutes. So, we've gathered our data, and finally finished training up a suitable model and validating that it performs well. We are now finally ready to move to the final phase: serving your predictions. When taking on the challenge of serving predictions, we would ideally want to deploy a model that is purpose-built for serving.