Even if we have a good project plan and a logical concept, we will spend the majority of our time correcting errors. Furthermore, our application can run without obvious errors with JavaScript, we must use various ways to ensure that everything is operating properly. The majority of JavaScript errors in our web applications will be eliminated if we understand both the syntax and how JavaScript works. Furthermore, numerous web services can help us in catching all of them.
Here at Cloudera, we’re committed to helping make the lives of data practitioners as painless as possible. For data scientists, we continue to provide new Applied Machine Learning Prototypes (AMPs), which are open source and available on GitHub. These pre-built reference examples are complete end-to-end data science projects. In Cloudera Machine Learning (CML), you can deploy them with the single click of a button, bringing data scientists that much closer to providing value.
SAP’s library of pre-defined reports for Finance and Controlling (FICO) is great for addressing some of the core tasks associated with finance and accounting. Those reports align well with accounting standards under GAAP and IFRS. Unfortunately, they rarely do a good job of addressing the kind of reporting needed to make informed managerial decisions.
As a very hands-on VP of Product, I have many, many conversations with enterprise data science teams who are in the process of developing their MLOps practice. Almost every customer I meet is in some stage of developing an ML-based application. Some are just at the beginning of their journey while others are already heavily invested. It’s fascinating to see how data science, a once commonly used buzz word, is becoming a real and practical strategy for almost any company.
GraphQL is a solid alternative to a traditional REST API, and offering one to your users may be easier than you think. Follow along as Kevin Cunningham builds a GraphQL endpoint with Node.js and MongoDB!