This blog post is a first of a series on how to leverage PyTorch’s ecosystem tools to easily jumpstart your ML / DL project. The first part of this blog describes common problems appearing when developing ML / DL solutions, and the second describes a simple image classification example demonstrating how to use Allegro Trains and PyTorch to address those problems.

There are lots of applications that users use these days. Some are accessed via the internet while some of them are installed on the user’s computer. The former being categorized as the web-based applications while the latter falling into the desktop applications category. There are a lot of differences in the operating mechanisms of both and some groups even consider either one of them as being better than the other.

In 2009 Node.js opened up a door for front-end developers to dip their toes into the world of servers without having to leave the comfort of their language. It’s almost effortless to get started with Node. You can basically copy-paste an entire HTTP server into existence and then install an ODM and you’ve got your CRUD app ready to roll! However, if we’ve learned anything from the amazing Spider-Man, it’s that with great power, comes great responsibility.

There are two ways you can view the current mappings on your Logit ELK Stacks. One way is to use dev tools in Kibana. You can access Kibana from any of your dashboards by choosing from your dashboard Stack settings > Access Kibana. You can also search for a specific mapping of an Index name. For example if we wanted to see the mappings for the a Filebeat index name we can run the following to return only the desired mappings.

Survival analysis is one of the most developed fields of statistical modeling, with many real-world applications. In the realm of mobile apps and games, retention is one of the initial focuses of the publisher once the app or game has been launched. And it remains a significant focus throughout most of the lifecycle of any endeavor.

This blog was written in partnership with Navdeep Alam, Senior Director, Global Data Warehouse, IQVIA Healthcare is unique. It isn’t defined like other businesses by how much revenue can be generated, but more in terms of achieving positive health outcomes, better value, and saving lives through the rapid development of new treatments and therapies.

Companies need special scheduling tools to organize and plan events, tasks, and various facilities. The business executives and managers or various projects need to be well-versed in their upcoming course of action. They need to plan their week accordingly, to decide when to attend meetings, events, and when to attend to clients. By planning their whole day, they will have time to make payments and perform their respective roles and responsibilities without any hassle.

At Sauce Labs, we define Continuous Testing as a “best practice approach to software quality that involves testing earlier and more often throughout the development lifecycle.” Teams that adopt continuous testing realize a number of benefits, including fewer bugs found in production, faster release cycles, and overall improved customer experience. Another rarely discussed benefit is that as the number of quality checkpoints increases, so does the volume of test data and quality signals.

I ordered a ride share recently from a beach; the app struggled to find a car, so I had to make several requests. After the fourth or fifth attempt, my bank alerted me to possible fraudulent activity on my credit card via SMS. Each time I ordered a ride, the service put a pending charge on my card. After I texted back that it was just me, the bank reactivated my account. Though the process was annoying, I felt reassured my bank could detect possible fraudulence that quickly.

Aside from ensuring each service is working properly, one of the most challenging parts of managing a cloud-based infrastructure is cost monitoring. There are countless services to keep track of—including storage, databases, and computation—each with their own complex pricing structure. Monitoring cloud costs is quite different from other organizational costs in that it can be difficult to detect anomalies in real-time and accurately forecast monthly costs.