Let's explore the complexity and vulnerability of IT infrastructure and how to build a modern IT infrastructure monitoring solution, using a combination of time series databases with machine learning. Check out ZDNet's series of articles detailing the outages. The outage caused disruptions to the likes of YouTube, Snapchat, and Gmail, among others. We have quickly embraced the cloud as more resilient than on-premise infrastructure, so this news is sobering. It also shows the vulnerability of the IT infrastructures, both cloud-based and on-premise, that power much of our software-dependent world -- a world that now includes entertainment and personal, as well as professional connections.
In today's business environment, technology has become the foundation on which innovation is built. As a result, companies are looking into digital transformation as a key strategic priority. It seems as if every chief information officer (CIO), regardless of company size, geography or industry is going through some kind of digital transformation journey. The reason for this is that digital transformation has the power to change organizational processes and capabilities and ultimately transform how a business delivers value to customers. In this article, we'll explore how real-time data is essential for powering digital transformation as providing faster response and continuous availability to critical data becomes a key competitive advantage for businesses to keep up in today's dynamic and always-on world.
As data becomes the only real competitive advantage feeding increased operational efficiencies, better customer intimacy and constantly improving customer experience, it is imperative that enterprises shift their disaster recovery efforts from just focusing on availability and reliability of services to ensure that their data assets are recoverable and re-integratable into various data powered scenarios backing their business. Modern enterprises require data in many shapes and forms across the board for powering planning, ideating, experimenting and designing/developing new products and services. These business-critical scenarios are often dependent on data that has been transformed, processed and made suitable to meet the requirements. As the "distance" between raw data and transformed data that drives products and services increases due to increasingly complex techniques of transformation, disaster recovery needs to include the not just the time to bring up the copy of lost data online but the time it takes to retransform the data. AI techniques such as Machine Learning, NLP, Anomaly Detection etc. produce "models" that can be leveraged to drive predictions, classifications and categorization.
'The network is the computer' was the mantra of the early days of connected systems, but it took the Internet to fully realize the concept. In today's era of smart sensors, cheap storage and sophisticated algorithms, an apt aphorism might be'the data is the business' in that business decisions, new services and product strategies are fueled by the analysis of massive amounts of mundane data. The ability to collect, store and analyze such routine data as transaction records, system logs, sensor readings and location information with increasing granularity has the potential to turn what was formerly lost or ignored information into valuable business assets. The organizations that are most adept at spinning the digital straw into gold find themselves at a significant competitive advantage. Aside from the advances in core infrastructure, perhaps nothing has been as responsible for the rise of data-inspired business decisions as the Hadoop ecosystem of open source distributed data storage and processing software.
Continual learning to build and automate ML pipelines from research to production, automatically retraining models in production with incoming data and advanced monitoring capabilities to ensure that models are accurate, healthy and performing well. Machine learning management that standardizes the full ML process in a collaborative environment, which supports management of models, experiments, data and research for "100% reproducible data science". An open platform that works with any framework or programming language. The platform's advanced connectivity to any compute resources (cloud/on premis) lets companies utilize on-premise infrastructure, including Kubernetes, Data Lakes, Hadoop, and more – as well as scale to any cloud service. Continual learning to build and automate ML pipelines from research to production, automatically retraining models in production with incoming data and advanced monitoring capabilities to ensure that models are accurate, healthy and performing well.