Machine learning tools and practices continue to develop at a dizzying pace. The industry has moved on from running Python tools on the data scientist’s local laptop to hosted services from all the major cloud vendors that inexpensively build accurate enough models with many popular algorithms, both traditional (such random forest, support vector machine and
Read MoreThe biggest problem in machine learning model accuracy is that all models degrade. They perform well initially but as the real world changes the models no longer capture the underlying structure. Both concept drift (the meaning and universe of values of the target dependent value being predicted) and data drift (the universe of values for
Read MoreIn my last post I discussed why we at Auger believe that AI will eat software. Enterprises will move beyond just solving their biggest problems with painstakingly built predictive models that teams of data scientists spend months on. Instead every enterprise application can build predictive models wherever they have access to data. Such predictive models can replace hand-coded rule of
Read MoreI just returned from a few weeks in Europe speaking at various AI and ML conferences. Notwithstanding our many posts on the topic, I actually don’t like to give talks about why Auger.AI is the best AutoML product. Instead what I have been talking about is where I see all of this innovation in AutoML heading. “First generation” Automated Machine Learning was focused on the
Read MoreWe are very excited to announce the launch of Auger.AI Cloud! Auger.AI Cloud is a fully hosted service eliminating the additional costs of paying for your own Amazon Web Services account. Only pay for what you use: choose the level of worker hours, storage, and numbers of users that you need to run your prediction
Read MoreShortly after Microsoft released the AutoML product a few months ago, we published a comparison of Auger.AI versus Azure’s accuracy on Microsoft’s own chosen datasets. Specifically, in their paper behind their AutoML approach, Microsoft chose 89 OpenML datasets. As Azure did, we then compared that accuracy against H20 and TPOT as well. The results showed Auger with 4.5%
Read MoreIn our last post, we pointed out that Auger outperforms the new Microsoft AutoML and other AutoML tools such as H2O and TPOT. Specifically, we took the OpenML datasets that Microsoft used to compare their AutoML with other tools, time-limited to one hour, and compared Auger’s predictive models (algorithms plus hyperparameters). Auger provided an average
Read MoreIf you’re a regular reader of this blog you probably know that Microsoft recently released their own AutoML SDK. We at Auger.AI were excited to see this development. It helps to validate and build awareness of this exciting new product category: Automated Machine Learning. And we love to see other products to compare our results to. Microsoft in their
Read MoreSomething I rarely tell people is that I am a dropout from UC Davis’ Ph.D. viticulture program. One of the first models we built in Wine Production (which encompassed both growing and later production of wine) was based on mostly exogenous variables not under grower control. Things like temperature and humidity at various points in
Read MoreSince we launched Auger a few months ago, we found that some of the most significant benefits were to novice machine learning users. These users typically don’t have great instincts on what machine learning algorithms to use for their problem. They usually have applied some statistical method to perform basic prediction, such as linear regression
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