The telecom industry is in the midst of a massive shift to new service offerings enabled by 5G and edge computing technologies. With this digital transformation, networks and network services are becoming increasingly complex: RAN, Core and Transport are only a few of the network’s many layers and integrated components. Today’s telecom engineers are expected to handle, manage, optimize, monitor and troubleshoot multi-technology and multi-vendor networks.

There is nothing permanent except change. In a world of turbulent, unpredictable change, we humans are always learning to cope with the unexpected. Hopefully, your machine learning business applications do this every moment, by adapting to fresh data. In a previous post, we discussed the impact of COVID-19 on the data science industry.

Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. In our current landscape, businesses that have adopted a successful ML strategy are outperforming their competitors by over 9%. The implications of ML on the future of business are clear. However, only 4% of enterprise executives today report seeing success from their ML investment.

Spreadsheets are everywhere! They are one of the most useful productivity tools available. They make organizing, calculating, and presenting data a breeze. Google Sheets is the spreadsheet application included in Google Workspace, which has over 2 billion users. Machine learning, or ML for short, has also become an essential business tool. Making predictions with data at low cost and high accuracy has transformed industries.

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.