OpenBlender, the pioneering ML Enrichment platform that generates and blends thousands of variables from publicly-available data to greatly improve model performance, today announced it has been recognized by Gartner as one of the 2021 Cool Vendors in Data for Artificial Intelligence and Machine Learning. As ML techniques become standard practice, companies are benefiting from prioritizing the integration of useful external data over perpetual model optimization. In the report, Gartner predicts that "By 2025, 70% of organizations will be compelled to shift their focus from big to small and wide data, providing more context for analytics and making AI less data hungry." OpenBlender's innovative data blending solutions are significantly improving machine learning results in a wide variety of use cases with contextual ML-ready variables from news reports, social media interactions, web search trends, reviews, COVID statistics, economic and financial figures, geolocations, mobility, weather, etc. "Our selection as a Gartner Cool Vendor confirms our efforts to empower companies to realize the full potential of their machine learning and boost business outcomes," said Federico Riveroll, Chief Data Scientist at OpenBlender. "Our technology makes it very simple for data science teams to transparently enrich their models with performance-fueling features in only a few lines of code."

In this tutorial we'll make a Machine Learning Pipeline which inputs Business News and generates predictions for Apple Stock Price re-training through time. We'll also measure how profitable it is in real life. It has the daily high, low, open and close prices and the percentual change during that day. Note: To get a token you need have to create an account on openblender.io Now let's look at the data: Let's plot the price and Change: Now, what we want is to detect if the price is going to increase or decrease on the next day so we can buy or short.