Tag: data science

Feed: Featured Blog Posts - Data Science Central. Author: ajit jaokar. In the previous post, ten strategies to implement ai on the cloud and edge, I discussed strategies for end to end deployment for machine learning modules. How this relates to Agile? Deployment of AI comes within the scope the normal SDLC (software development lifecycle) So, normal Agile techniques like scrum, sprints, backlog planning, release planning, test driven development and acceptance criteria also apply to deployment of machine learning models in production. However, combining DevOps with development adds an extra level of complexity to machine learning. We need end to ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: William Vorhies. Summary:  Just how much should you trust your AI systems?  Best practice points to constant review, strong governance, and the willingness to override results that seem illogical. Just how much do you trust your AI?  This is not intended to be the skeptical consumer view about bias or black box outcomes.  It’s intended to see if leaders in actual implementation see AI through proverbial rose colored glasses or whether and how much skepticism they think is warranted. As a source, I love these very professionally done surveys of AI ... Read More

Feed: R-bloggers. Author: R on Sastibe's Data Science Blog. Recently, I was tasked with a straightforward question: “In an A/B test setting, how many samples do I have to collect in order to obtain significant results?” As ususal in statistics, the answer is not quite as straightforward as the question, and it depends quite a bit on the framework. In this case, the A/B test was supposed to test whether the effect of a treatment on the success rate p had the assumed size e. The value of the success rate had to be estimated in both test and control ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. It is sometimes said that you don't need to know math to be a data scientist. Sometimes the opposite is said, after all, data science is supposed to be a science! Regardless, below are a few of my articles featuring how data science and math can benefit from each other - not just math to solve data science problems, but also data science to solve math problems. Articles Variance, Attractors and Behavior of Chaotic Statistical Systems New Family of Generalized Gaussian Distributions Gentle Approach to Linear Algebra, with Machine ... Read More

Feed: Microsoft Azure Blog. Author: John 'JG' Chirapurath. Enterprises today are transforming their businesses using Machine Learning (ML) to develop a lasting competitive advantage. From healthcare to transportation, supply chain to risk management, machine learning is becoming pervasive across industries, disrupting markets and reshaping business models. Organizations need the technology and tools required to build and deploy successful Machine Learning models and operate in an agile way. MLOps is the key to making machine learning projects successful at scale. What is MLOps ? It is the practice of collaboration between data science and IT teams designed to accelerate the entire ... Read More

Feed: R-bloggers. Author: Learning Machines. We already covered the so-called Accuracy-Interpretability Trade-Off which states that oftentimes the more accurate the results of an AI are the harder it is to interpret how it arrived at its conclusions (see also: Learning Data Science: Predicting Income Brackets). This is especially true for Neural Networks: while often delivering outstanding results, they are basically black boxes and notoriously hard to interpret (see also: Understanding the Magic of Neural Networks). There is a new hot area of research to make black-box models interpretable, called Explainable Artificial Intelligence (XAI), if you want to gain some intuition ... Read More

Feed: R-bloggers. Author: John. Statisticians often come across outliers when working with datasets and it is important to deal with them because of how significantly they can distort a statistical model. Your dataset may havevalues that are distinguishably different from most other values, these arereferred to as outliers. Usually, an outlier is an anomaly that occurs due tomeasurement errors but in other cases, it can occur because the experimentbeing observed experiences momentary but drastic turbulence. In either case, itis important to deal with outliers because they can adversely impact theaccuracy of your results, especially in regression models. In this tutorial, ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Bill Schmarzo. Imagine this scenario: You enter your dentist’s office as a follow-up visit to the 6-month checkup you had last week. You’re nervous because the checkup revealed bleeding in your gums and cracks in your fillings, all things that require your dentist’s repair on this occasion. So, you’re there nervously fidgeting in the lobby waiting for your name to be called, envisioning all sorts of dentistry implements of torture that will soon be inside your mouth. Finally, when your name is called, you’re shown to the room of horrors and ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Robert R. Tucci. Quantum computers come in 2 varieties, quantum annealers and quantum gate models. So far, DWave is the only company selling quantum annealers. A third type of device is now available from Fujitsu. Fujitsu calls its device a "quantum inspired digital annealer". The Fujitsu device does not have any quantum correlation (quantum entanglement) between separate qubits, so, in that sense, it is not a real qc, and Fujitsu doesn't claim it is, only that it is "quantum inspired". DWave, on the other hand, does claim partial quantum entanglement between ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Roman Chuprina. Machine Learning is a term heard around the world these days. Industries like Retail, Healthcare, and Manufacturing are taking the best out of it. So does the leaders in Banking and Finance. There is enough time and room before the technology will truly explode. However, we can still talk about some real-world use cases and ways your business can benefit. AI and ML in financial services Machine learning in finance is rapidly developing – there are already dozens of options for its use in the financial sector. So why ... Read More