Operations | Monitoring | ITSM | DevOps | Cloud

Machine learning (ML) and artificial intelligence (AI) are often used interchangeably in tech discussions, yet they represent distinct concepts with important differences. While AI refers to the broader field of creating machines capable of intelligent behavior that mimics human capabilities, machine learning is a specific subset of AI focused on developing algorithms that allow computers to learn from and make predictions based on data.

The landscape of artificial intelligence is rapidly evolving, demanding robust and scalable infrastructure. To meet these challenges, we’ve developed a comprehensive reference architecture (RA) that leverages the power of open-source tools and cutting-edge hardware.

Kubeflow is a cloud-native, open source machine learning operations (MLOps) platform designed for developing and deploying ML models on Kubernetes. Kubeflow helps data scientists and machine learning engineers run the entire ML lifecycle within one tool. Charmed Kubeflow is Canonical’s official distribution of Kubeflow. The key benefits of Charmed Kubeflow include security maintenance of container images, enterprise support, and further tooling integration with Spark, Feast, MLFlow, and others.

AI/ML development is getting a lot of attention as organizations rush to bring AI services into their business applications. While emerging MLOps practices are designed to make developing AI applications easier, the complexity and fragmentation of available MLOps tools often complicates the work of Data Scientists and ML Engineers, and lessens trust in what’s being delivered.

Losing a customer to a competitor can be costly; customer retention is vital for business success and growth. Businesses must anticipate when and why a customer might leave, so they can implement measures to retain them. One solution might be to build a system that predicts churn. But can it be done? Using machine learning (ML) techniques to analyze customer service interactions can provide valuable insight into customer sentiment.

As AI and ML become more prevalent, administrators can use Site Reliability Engineering (SRE) techniques to manage the ML infrastructure and software.

As the number of organizations moving their ML projects to production is growing, the need to build reliable, scalable architecture has become a more pressing concern. According to BCG (Boston Consulting Group), only 6% of organizations are investing in upskilling their workforce in AI skills. For any organization seeking to reach AI maturity, this skills gap is likely to cause disruption.

Forecasting is all about predicting the future—in data science, it is one of the key skills in dealing with time series data, such as stock price prediction, sales forecasting, logistics planning, etc. In this tutorial, we’ll learn how to forecast the notorious weather pattern of London, UK, using the following free and open source technologies.