The latest News and Information on APIs, Mobile, AI, Machine Learning, IoT, Open Source and more!
The open-source community is about to benefit greatly from Netdata’s new Grafana data source plugin, which makes use of a powerful data collection engine. This new plugin maximizes the troubleshooting capabilities of Netdata in Grafana, making them more widely available. Some of the key capabilities provided to you with this plugin include the following.
Learn how to deploy InfluxDB Cloud’s Native Collectors with Kepware and The Things Network. Did you hear about the new feature that just dropped to InfluxDB Cloud? Native Collectors! Starting with MQTT. There will be plenty of content to get you started with Native Collectors. So this blog series covers connecting two popular IoT-based platforms to InfluxDB Cloud using native Collectors. One Enterprise use case and one industrial use case.
In this era, machine learning is important. Machine learning helps in business Management operations and understanding customer behaviors. It also helps in the development of new products. Every leading company is shifting towards machine learning. Companies like Amazon, Facebook, Google, and of course Nastel Technologies, prioritize machine learning as their central part. Let’s see how machine learning works.
Running or operating applications requires several tasks throughout their lifecycle: scaling instances, checking the health, integrating with other applications, running backups, and applying updates – to name a few examples. It’s a time and labour-intensive process. To automate these tasks, developers can implement scripts for repeated execution. This is where the software operator comes in.
When we think of computers, we typically think in terms of exactness. For example, if we ask a computer to do a numeric calculation and it gives us a result, we are 100% sure that the result is correct. And if we write an algorithm and it gives an incorrect result, we know we have coded improperly and it needs to be corrected. This exactness however, is not the case when dealing with Machine Learning. As a matter of fact, it is par for the course, that Machine Learning will be incorrect a percentage of the time.
