Service Level Agreements (SLAs) are commitments given to customers in relation to the product or service being provided. If breached, not only are organizations expected to compensate through penalties and credit fees, but they can also face a significant dip in brand reputation and loss of customer trust. This is why preventing SLA breaches is a top priority for any customer-facing organization. To stay on top of breaches, agents traditionally check the ticket status of each incident manually.

We’re seeing an increase in demand for hybrid AI deployments. This trend can be attributed to a number of factors. First of all, many enterprises look to hybrid solutions to address data locality, in accordance with a rise in regulation and data privacy considerations. Secondly, there is a growing number of smart edge devices powering innovative new services across industries.

No, not really. You probably won’t be rich unless you work really hard… As nice as it would be, you can’t really predict a stock price based on ML solely, but now I have your attention! Continuing from my previous blog post about how awesome and easy it is to develop web-based applications backed by Cloudera Operational Database (COD), I started a small project to integrate COD with another CDP cloud experience, Cloudera Machine Learning (CML).

Continuous evaluation—the process of ensuring a production machine learning model is still performing well on new data—is an essential part in any ML workflow. Performing continuous evaluation can help you catch model drift, a phenomenon that occurs when the data used to train your model no longer reflects the current environment.

Data science has become an important capability for enterprises looking to solve complex, real-world problems, and generate operational models that deliver business value across all domains. More and more businesses are investing in ML capabilities, putting together data science teams to develop innovative, predictive models that provide the enterprise with a competitive edge — be it providing better customer service or optimizing logistics and maintenance of systems or machinery.

Artificial intelligence and machine learning are relentlessly revolutionizing marketplaces and ushering in radical, disruptive changes that threaten incumbent companies with obsolescence. To maintain a competitive edge and gain entry into new business segments, many companies are racing to build and deploy AI applications.

Read Part 1 here > Data analytics is a complex process that demands time and effort from data scientists. From cleaning and prepping data to performing data analysis, data scientists go through an extensive procedure to uncover hidden patterns, identify trends, and find correlations in data to make informed business decisions. The task of integrating, cleaning, and organizing data assets often take up the bulk of the data scientist’s time.