Everyone knows that their apps aren’t running as well as they could be. They know that – with the right tweaks to container and VM resource settings – they could be costing less, and performing better. There are various reasons why attempts at performance tuning often fall short. Optimization is difficult to fold into a release cycle; in the roadmap, it tends to get crowded out by new features; engineers don’t find it very exciting.

We’ve already made the case for how important analytics is in the field of human resources, but let’s not stop there. Organizations can take it one step further with AI-powered insights. Not only will artificial intelligence help streamline operations and improve efficiency, but it also plays an integral role in optimizing the human aspect of employee management: quality conversations and interaction, mentoring, and promoting motivation in the workplace.

With today’s level of automation in software development and delivery, along with continuous improvement to CI/CD pipelines, keeping up with production needs becomes harder and harder. So, how can you improve release management and deploy new features and services at breakneck speeds without hindering service reliability?

CIO magazine are a serious publication. They’ve been around since 1987, and they’re run by the Boston-based media heavyweights International Data Group. So it made our week to hear the news that CIO had named us as one of 2019’s top 25 artificial intelligence companies.

Companies of various sizes have embraced the concept of the lean startup. Organizations are continually looking for ways to save money and stretch limited budgets to the max. Thanks to the growth in diverse applications of artificial intelligence, technology is helping companies achieve this goal.

This is the story of how Vectorspace datasets came about using the Elastic Stack, and Canvas in Kibana, in order to visualize information and unlock the power and value of data. In 2002 at Lawrence Berkeley National Laboratory, Vectorspace created feature vectors based on Natural Language Understanding (NLU), also known today as word embeddings.

You have just started work and your calendar looks packed for the rest of the day. There are several tickets in the queue and you start resolving them one-by-one. A frustrated customer reports an issue. You help the customer out and update the status of the ticket to “resolved”. Job well done.

AI and its enabled tools continue to enthrall business with its promise of efficiency and innovation. But, one of the things AI is also clearly enabling is the bias. We’ve all read the news and heard the scaremongering stories around potential flaws and biases in Artificial Intelligence systems. I believe for this technology to reach its full potential, addressing bias will need to be a top priority.

As machine learning continues to address common use cases it is crucial to consider what it takes to operationalize your data into a practical, maintainable solution. This is particularly important in order to predict customer behavior more accurately, make more relevant product recommendations, personalize a treatment, or improve the accuracy of research.