Efficient management of infrastructure systems is crucial for economic stability, sustainability, and public safety. However, infrastructure management is challenging due to the vast scale of systems, stochastic deterioration of components, partial observability, and resource constraints. While data-driven approaches like reinforcement learning (RL) offer a promising avenue for optimizing management policies, their application to infrastructure has been limited by the lack of suitable simulation environments. We introduce InfraLib, a comprehensive framework for modeling and analyzing infrastructure management problems. InfraLib employs a hierarchical, stochastic approach to realistically model infrastructure systems and their deterioration. It supports practical functionality such as modeling component unavailability, cyclical budgets, and catastrophic failures. To facilitate research, InfraLib provides tools for expert data collection, simulation-driven analysis, and visualization. We demonstrate InfraLib's capabilities through case studies on a real-world road network and a synthetic benchmark with 100,000 components.

AI leader DataRobot announced the availability of DataRobot Notebooks, a fully integrated notebooks solution within the DataRobot AI platform that enables data scientists to collaborate across code-first workflows with one-click access to embedded notebooks. "Customers want a notebook solution that will allow them to focus on their data science work rather than infrastructure management" Notebooks are a crucial tool for data scientists to rapidly experiment and share insights through quick environment creation, interactive computation, and code snippets. As the number of notebook users in a data science organization grows, challenges including managing notebooks at scale and maintaining complex dependencies and libraries become overwhelming and costly for data science teams. "We are entering a phase of AI governance where the collaboration and productivity gains of data science teams become increasingly important," said Mike Leone, Senior Analyst at Enterprise Strategy Group. "With DataRobot Notebooks, the flexibility to develop in preferred environments, including open-source ML tooling or in the DataRobot AI platform, streamlines the code development experience and allows data scientists to better collaborate as a team in a unified environment."

Artificial intelligence for IT operations, mainly acknowledged as AIOps, is the talk of the town these days, but people talk less about the way to implement AIOps. However, to implement AIOps successfully, businesses must know the process and tools needed at each stage. And, yes, AIOps will help businesses optimize their IT operations. Today, IT companies operate in complicated and extensive environments, often while connecting on-premises and private and public clouds legacy setups. IT leaders, managers, and teams are usually under pressure to serve the business with their end-to-end IT operations and services. The enterprise's core focus is to prevent the most significant instances and any downtime.

Did you miss a session at the Data Summit? Artificial intelligence (AI) is being unleashed on business processes, data analytics and a host of other enterprise functions, but its role in data center automation stands to change not just the data center itself but all its infrastructure -- physical and virtual, to the edge and beyond. As with most everything AI touches, the data center will become leaner, less costly to operate and achieve higher performance metrics as the transition unfolds, and much of what is done by human operators will be automated -- just like what was happening in the pre-AI era. Still, experts predict that the shortage of qualified data center operators will continue and may even get worse. Clearly, there are many ways in which AI can be used to automate data center management.

Because technology and IT operations have changed dramatically over the previous decade, conventional information management tools and procedures no longer operate as well as they once did. Instead, they impede development by generating a flood of data from the multiple monitoring instruments currently necessary to monitor our sophisticated IT estates efficiently. Simply put, Automation is where humans and AI (artificial intelligence) work together to improve productivity and efficiency. AI automates discovering and fixing IT issues. If you're already on the platform and ready to dive into AI, keep reading for four useful tips.

The pandemic is not just a mere health crisis. It is a devastating economic disruption and has caused dramatic labor turmoil on a global scale. As far as the supply chains are concerned, we have witnessed their exposure to vulnerability and inadequate contingency plans. Due to the growing saliency of ethical aspect during Covid, companies find it crucial to shift to responsible consumption. Putting corporate social responsibility at the forefront, businesses do not expect a rapid reward.

Change from manual to automatic and simplify your IT tasks to skillfully manage all devices across multiple datacenters from a single location. A multi-mode rule based fault detection system, which quickly isolates & resolves fault with simple-to-advanced built in notification and scripting modules. Efficiently gauge network performance through dynamic thresholds and predictive analysis. Download, baseline and actively track any configuration changes in your network devices with color-code highlights, instant notifications and approval settings. Business-driven real-time reports & live dashboards with multi-tier logical views and role-based access, smartly equipped with easy configurable and auto-scheduling options.

San Jose, Calif., October 1, 2019 – Virtual Instruments, the leader in hybrid infrastructure management for mission-critical workloads, announced today its participation at HitachiNEXT 2019, taking place October 8-10 in Las Vegas, and NetApp INSIGHT 2019, taking place October 28-30 in Las Vegas. By holistically monitoring, analyzing and optimizing the performance, availability, capacity and efficiency of hybrid IT infrastructure within the context of the application, VirtualWisdom enables enterprises to take a modern, AIOps-empowered approach to infrastructure management. The latest version of VirtualWisdom applies real-time, AI-based analytics to help enterprises proactively manage the hybrid infrastructure supporting their mission-critical applications. Meanwhile, WorkloadWisdom analyzes production storage workloads, models workloads, creates what-if testing scenarios, and produces workload performance analytics, ultimately offering users better insight into how workload behavior affects storage system performance. NEXT 2019 is the largest annual event for Hitachi Vantara users, with a focus on sharing the best ideas to store, protect, enrich, activate and monetize data.

An app that helps you visualize, debug, track, and run ML/DL experiments, directly on your workstation or on your own servers, in your LAN or in the cloud. Deepkit will be free for individual users and available in all app stores. You can use the app alone or use the real-time collaborative features within a team using the Deepkit team server. We're are looking for alpha users that want to help us building a better, cheaper and more efficient way of doing ML/DL experiments. If you're interested, please register at the website directly or use this link.

There've been several machine learning and artificial intelligence tools, platforms and software in 2018. These platforms, software and tools have impacted every industry like legal, healthcare, automobile, manufacturing and agriculture. There are big brand names like Google, Amazon, Facebook, Apple and Microsoft that have been investing in such technologies for several years now. They wish to be leaders and trend setters to explore new areas of these technologies. While there are several predictions and expectations from AI and related technologies, I feel there will be 10 predominant trends that will dominate AI.