Summary: Too many solutions. We are at an inflection point where too many vendors are offering too many solutions for moving our AI/ML models to production. The very real risk is duplication of effort, fragmentation of our data science resources, and incurring unintended new technical debt as we bind ourselves to platforms that have hidden assumptions or limitations in how that approach problems.
Added by William Vorhies on November 25, 2019 at 9:44am — No Comments
Summary: AI/ML itself is the next big thing for many fields if you’re on the outside looking in. But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility.
What is RPA?
Robotic Process Automation (RPA) is the utilization of programming with machine learning and AI capacities to deal with high-volume, repeatable tasks, and transactions. RPA is an innovation planned for computerizing business forms. Robotic Process Automation conveys direct productivity and improves accuracy transforming an organization’s workflow. Empowering RPA allows flexibility within the enterprise. Programming robots are easy to…Continue
Added by Vinod Saratchandran on September 10, 2019 at 3:00am — No Comments
Summary: Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place. Looking back at the data and the dominance of RPA as the most widely reported instance makes us think that the number is probably significantly lower.
Summary: Digital Decisioning Platforms is a new segment identified by Forrester that marries Business Process Automation, Business Rules Management, and Advanced Analytics. For platform developers it’s a new way to slice the market. For users it eases integration of predictive models into the production environment.
Added by William Vorhies on October 30, 2018 at 8:30am — No Comments
Summary: Advanced analytics and AI are the fourth great lever available to create organic improvement in corporations. We’ll describe why this one is different from the first three and why the CEO needs the direct help of data scientists to make this happen.
If you’re a CEO or any other flavor of top executive leading a…Continue
All businesses are at the mercy of data quality challenges. From the moment you capture your first lead, you’ll be fighting a battle against data decay. The bigger the database gets, the more problems the business can encounter, and it isn’t easy to single out a…
The analytical scene has recently been dominated by the prediction that we would soon experience an important shortage of analytical talent. As a response, academic programs and massive open online courses (MOOCs) have sprung up like mushrooms after the rain, all with the purpose of developing skills for the analyst or its more modern counterpart, the data scientist. However, in the …Continue
Added by Geert Verstraeten on August 27, 2015 at 11:30pm — No Comments
For statistical process control, a number of single charts that jointly monitor both process mean and variability recently have been developed. For quality control-related hypothesis testing, however, there has been little analogous development of joint mean-variance tests: only one two-sample statistic that is not computationally intensive has been designed specifically for the one-sided test of Ho: Mean2<=Mean1 and StDev2<=StDev1 vs. Ha: Mean2>Mean1 OR StDev2>StDev1 (see…Continue
Added by J.D. Opdyke on March 9, 2012 at 5:42am — No Comments