Today, where there are almost as many approaches to digital transformation as there are enterprise software vendors, Docker refocuses its strategy on providing the best unified container management platform for DevOps. Docker’s key value proposition is to enable developers to build an application once and then deploy it to any Kubernetes-driven private or public cloud, where DevOps teams and IT operations can manage it throughout its lifecycle and move it to another location at any point in time. However, Docker also aims to absorb traditional enterprise applications, edge and IoT workloads, big data apps, blockchain, and serverless functions, both on Windows and on Linux.
OpenManage Enterprise is a big deal, but underrated: OpenManage Enterprise is the new infrastructure automation platform that could unlock the private and hybrid cloud for Dell. Deploying Dell infrastructure through a simple YAML file is within reach.
Companies who manage to put AI capabilities into the hands of each and every business employee will be vastly successful in the marketplace. Enabling business employees to back up their decisions with data will dramatically increase staff productivity. At the same time, employees will feel empowered by their new ability to make much more informed decisions and automating certain tasks, based on intelligence derived from operations and market data that is now available to them.
Here are the 10 quotes that best sum up #Think2018. Inspired by Think 2018, I came up with my own ideas for AI bots and posted them here.
With development teams working on 3-4 releases in parallel and releasing at least once a quarter, we often see release overhead consuming an average of 20-30% of development and operations resources. Enterprises releasing every month, week, or even day suffer from even larger overhead. Here is what you can do to free up most of these 20-30% for the faster creation of new critical software features: to enhance customer value:
The EMA Top 3 report for Container Management and DevOps in Production and at Scale is a curated collection of a large number of data points that reflect individual priorities, pain points, technology adoption patterns, requirements, opinions, and so on, of 300 enterprises. It then dives deeper into these individual data points to provide readers with enough context to inform their technology selection process to some degree. However, each reader needs to pick out the nuggets that are the most relevant to him or her and then draw her own conclusions. Look at it this way: the EMA Top 3 polls 300 of your peers on 58 container and DevOps related questions and you can learn from the answers without having 300 separate conversations.
Why Machine Learning Diagnoses Cancer but Can’t Run our Hybrid Cloud
Neural Networks Are Key in 2018
Neural networks are back, but please don’t think they are able to emulate the context awareness of the human brain. Don’t get me wrong, I believe that the neural network approach is a viable one, but we do need to be clear about its limitations. All of these limitations are rooted in the limited 'worldview' the neural network is able to take in. This worldview is limited as it relies on a human being with limited time and knowledge creating the neural network topology by providing the following components and enough sample data for the machine to determine how input should be processed.
My constant search for the next ‘cool’ tech toy or app is inspired by my obsession for automation. Trying to teach my 4 and 6 year old kids to think of creative solutions for their repetitive problems (no, there’s not yet a robot that cleans their rooms) surfaced the following 3 top toys and top apps in 2017.