Operations | Monitoring | ITSM | DevOps | Cloud

Machine learning has crossed the chasm. In 2020, McKinsey found that out of 2,395 companies surveyed, 50% had an ongoing investment in machine learning. By 2030, machine learning is predicted to deliver around $13 trillion. Before long, a good understanding of machine learning (ML) will be a central requirement in any technical strategy. The question is — what role is artificial intelligence (AI) going to play in engineering?

Your team has worked hard on a software product for months, and it’s finally ready to release to your users! But then the worst-case scenario happens: a wide release soon indicates that the software is plagued with bugs and performance issues, resulting in poor reviews and widespread user dissatisfaction.

In this article, I’ll provide a guide to Git for non-developers. So, if you are reading this as a full experienced Git user, you may disagree with some of my statements. Don’t bother reading on; we recognize you as the expert! If, on the other hand, you’re new to Git, this piece is for you.

Operations staff get a hard time. The lowly systems administrator (sysadmin), database administrator (DBA) and all the other operations engineering team members from cyber penetration specialists to user acceptance testing (UAT) and so on are generally unloved.

Upgrading your Mattermost server involves a bit of research, preparation, and downtime. The pressure to keep your Mattermost instance healthy and reduce downtime for a core system within your organization can be intimidating. Recently, we worked with a handful of customers who were experiencing issues upgrading from Mattermost v5.37 and v5.39 to v6.x. Unfortunately, migration scripts were required to make significant database changes, and there was an issue in product performance.

The explosion of APIs, devices, applications, and data sources has complicated the task of building connectivity across the enterprise. As organizations are connecting to applications outside of their four walls, they risk becoming fragmented. Moreover, existing on-premise systems, such as AS/400 and ERPs, need to be able to communicate both internally and externally.

IBM MQ is a family of message-oriented middleware products that IBM launched in December 1993. It was originally called MQSeries and was renamed WebSphere MQ in 2002 to join the suite of WebSphere products. In April 2014, it was renamed IBM MQ. The products included in the MQ family are IBM MQ, IBM MQ Advanced, IBM MQ Appliance, IBM MQ for z/OS, and IBM MQ on IBM Cloud. MQ stands for MESSAGING AND QUEUEING.

Kafka is an open-source program for storing, reading, and analyzing streaming data. It is open-source, which means it’s free-to-use amongst a big community of users and developers contributing to new features, upgrades, and support on a regular basis. Kafka can run on multiple servers as a distributed system, allowing it to take advantage of each server’s processing power and storage capacity.