This article was co-written with my colleague and fellow YEC member, Nirman Dave, CEO at Obviously AI. Back in March of this year, MIT Sloan Management Review made a sobering discovery: The majority of data science projects in businesses are deemed failures. A staggering proportion of companies are failing to obtain meaningful ROI from their data science projects. A failure rate of 85% was reported by a Gartner Inc. analyst back in 2017, 87% was reported by VentureBeat in 2019 and 85.4% was reported by Forbes in 2020. Despite the breakthroughs in data science and machine learning (ML), despite the development of several data management softwares and despite hundreds of articles and videos online, why is it that production-ready ML models are just not hitting the mark?

Physicians spend nearly two full business days per week on prior authorization requests as part of an antiquated, manual process. Prior authorization is the "single highest cost for the healthcare industry" in the U.S., totaling some $767 million a year, according to the CAQH index. Physicians spend nearly two full business days per week on prior authorization requests, and payers devote thousands of manhours reviewing and approving them in an antiquated, manual process involving phone calls and faxes. The arduous task often delays necessary treatment and sometimes results in treatment abandonment -- patients just get tired of waiting, so they give up -- both of which hurt patient outcomes and ultimately raise costs in the long run. Prior authorization has been identified as one of the biggest opportunities for applying artificial intelligence (AI) to help lower the administrative burden and cost.

Ryan Owen holds an MBA from the University of South Carolina, and has rich experience in financial services, having worked with Liberty Mutual, Sun Life, and other financial firms. Ryan writes and edits AI industry trends and use-cases for Emerj's editorial and client content. American Express began as a freight forwarding company in the mid-19th century. Expanding over time to include financial products and travel services, American Express today reports some 114 million cards in force and $1.2 trillion in billed business worldwide. American Express trades on the NYSE with a market cap that exceeds $136 billion, as of November 2021.

Building a classification algorithm is always a fun project to do when you are getting into Data Science and Machine Learning. Along with Regression, Classification problems are the most common ones that businesses jump right into when they start experimenting with predictive modelling. But, evaluating a classification algorithm may get confusing, really fast. As soon as you develop a logistic regression or a classification decision tree and output your first ever probability spitted from a model, you immediately think: how should I use this outcome? First and foremost, when it comes to evaluating your classification algorithms there is a big choice that you must do: do you want to use a metric that is already tied to a threshold of your "probability" outcome?

If you are looking for the best way to classify any URL, domain, or email of a website, you should try this helpful API. An email classification system is a monitoring system that monitors the real-time flow of email into an organization from within an email inbox. The system classifies each email depending on the parameters provided by the individual or enterprise by automatically evaluating the content of the email body and any attachments. The system classifies emails into the preferred folder by extracting the email sender and content, as well as learning user behavior. An industry-specific email classification system can be a significant complement to existing compliance standards for the majority of businesses. In this respect, more and more companies are investing in this type of system and there is an API that allows classifying any data with a simple email, URL, or domain.

Intel revealed that it will hold an initial public offering (IPO) for Mobileye in the US sometime around the middle of next year. The automated vehicle technology company originally became a publicly traded entity in 2014 before being acquired by Intel in 2017 for $15.3 billion. Intel, which currently owns 100% of Mobileye, will remain the majority shareholder in the company following its IPO. It plans to continue co-developing solutions for the automotive industry alongside it in the role of a strategic partner. The new entity will include all of Mobileye's assets, portions of the recently acquired Moovit, and "Intel teams working on lidar and radar development."

Extract, transform, and load (ETL) is the process by which data is acquired from various sources. The data is collected in a standard location, cleaned, and processed. Ultimately, the data is loaded into a datastore from which it can be queried. Legacy ETL processes import data, clean it in place, and then store it in a relational data engine. "SQL Server Integration Services is a platform for building enterprise-level data integration and data transformations solutions. Use Integration Services to solve complex business problems by copying or downloading files, loading data warehouses, cleansing and mining data, and managing SQL Server objects and data."

Organizations are under growing pressure to transform the volumes of data captured by their systems into valuable insights that drive impact across all levels and lines of business. Investing in AI/ML is no longer optional but critical for organizations to remain competitive. Yet, this growing investment also brings challenges. AI remains complex and out of reach for many. Outcomes that drive real business change can be elusive.

In probability, Bayes is a type of conditional probability. It predicts the event based on an event that has already happened. You can use Naive Bayes as a supervised machine learning method for predicting the event based on the evidence present in your dataset. In this tutorial, you will learn how to classify the email as spam or not using the Naive Bayes Classifier. Before doing coding demonstration, Let's know about the Naive Bayes in a brief.

Roche (SIX: RO, ROG; OTCQX: RHHBY) today announced the research use only (RUO) launch of three new automated digital pathology algorithms, uPath Ki-67 (30-9), uPath ER (SP1) and uPath PR (1E2) image analysis for breast cancer, which are important biomarkers for breast cancer patients. Breast cancer is the second most common cancer in the world with an estimated 2.3 million new cases in 2020¹ and is the most common cancer in women globally¹,². These new algorithms complete the Roche digital pathology breast panel of image analysis algorithms. This includes a whole slide analysis workflow with automated pre-computing of the slide image prior to pathologist assessment, and a clear visual overlay highlighting tumour cells with and without nuclear staining. Intended for use with Roche's high medical value assays and slides stained on a BenchMark ULTRA instrument using ultraView DAB detection kit, the uPath Ki-67 (30-9) image analysis, uPath ER (SP1) image analysis and uPath PR (1E2) image analysis algorithms are ready-to-use and integrated within Roche's uPath enterprise software and NAVIFY Digital Pathology, the cloud version of uPath.