The Reliability industry needs a managed, non-vendor lock-in answer to spiraling costs, high cardinality and the toil of managing a tsdb.

Artificial intelligence (AI) and machine learning (ML) are two cutting-edge technologies that are revolutionizing the field of website development. AI refers to the ability of computers to perform tasks that typically require human intelligence, such as recognizing speech, understanding natural language, and making decisions based on data. On the other hand, ML is a subset of AI that involves training algorithms to learn from data and make predictions or decisions based on that learning.

The global COVID-19 pandemic ushered in a new era of remote work. The Pew Research Center reports that over 71% of people had transitioned to working from home at the height of the pandemic. As of 2023, that number is still relatively high, with 59% of workers remaining at home though social distancing restrictions have long been relaxed. As remote work becomes increasingly common, many managers are considering transitioning to the new model.

There is rapid adoption of artificial intelligence (AI) and machine learning (ML) in the finance sector. AI in banking is reshaping client experiences, including communication with financial service providers (for example, chat bots). Banks are exploring ways to use AI/ML to handle the high volume of loan applications and to improve their underwriting process.

We know that for many retailers and CPG companies, AI/ML solutions represent a game-changing technology. Yet, this journey seldom comes without a few expectable “growing pains”—from adoption and scale through a fully-fledged data-driven transformation. For multiple internal stakeholders across an organization, the end-to-end process can seem quite daunting—especially without a well-defined plan.

This article was originally published in The New Stack and is reposted here with permission. Selecting the tools that best fit your IoT data and workloads at the outset will make your job easier and faster in the long run. Today, Internet of Things (IoT) data or sensor data is all around us. Industry analysts project the number of connected devices worldwide to be a total of 30.9 billion units by 2025, up from 12.7 billion units in 2021.

Getting more clients or adding a new service may sound like all it takes to expand your business, right? The truth is that expansion requires refining every aspect of your business’s operations and adopting a proactive approach to growth. There are several factors to consider when deciding to scale your business, including the cost of required software, resources needed to train technicians, and the cost of advertising the new service.

As engineers, we tend to pride ourselves on building a production-first mindset and operational excellence. According to a recent survey, 74% of executives believe that AI will deliver more efficient business processes, while 55% think that AI will help develop new business models and create new products and services. However, the reality is that 85% of ML projects fail to deliver, and 53% of machine learning prototypes don't make it to production.