Systems | Development | Analytics | API | Testing

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).

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.

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.

With the speed of change in artificial intelligence (AI) and big data, podcasts are an excellent way to stay up-to-date on recent developments, new innovations, and gain exposure to experts’ personal opinions, regardless if they can be proven scientifically. Great examples of the thought-provoking topics that are perfect for a podcast’s longer-form, conversational format include the road to AGI, AI ethics and safety, and the technology’s overall impact on society.

Simplifying feature engineering for building real-time ML pipelines might just be the next holy grail of data science. It’s incredibly difficult and highly complex, but it’s also desperately needed for multiple use cases across dozens of industries. Currently, feature engineering is siloed between data scientists, who search for and create the features, and data engineers, who rewrite the code for a production environment.