Machine learning — the practice of writing algorithms that improve automatically through experience — has become a buzzword nowadays that connotes to something otherworldly and on the bleeding edge of technology. I’m here to tell you while that may be true, getting started with machine learning doesn’t have to be hard!
Five worthy reads is a regular column on five noteworthy items we have discovered while researching trending and timeless topics. This week, we highlight the urgent need for ethical AI due to increased adoption of AI enabled technologies by businesses during the COVID-19 pandemic.
Once you’ve reached the point where you want to deploy your machine learning models to production, you will eventually need to monitor operations and performance. You might also want to receive alerts in case of any unexpected behavior or inconsistencies with your model or your data quality. This is where you most likely start learning about various aspects of Machine Learning Operations (MLOps).
As there are more advances in data use, businesses must ensure they’re delivering value by utilizing all data sources present in their environments. Irrespective of the source of data, be it operational or transactional systems, smart devices, social media, video, or text, it’s what the business does with the data available that determines its value strategy.
When was the last time you had the chance to listen to some of the most beautiful concerts that nature can play for you? From simple chirps and tweets to complex bird songs composed into a sophisticated soundscape, you may wish you could decrypt and understand their daily conversation. “Hey, good morning, how are you today?”, you might hear in the early hours, sometimes so loudly that you are awakened from the chirping.
In May we released the Splunk Machine Learning Toolkit (MLTK) version 5.2. We’ve loved telling you about some of the great new features, including the most recent blog on DensityFunction. However, we know that before you can start experimenting with model-building algorithms such as DensityFunction, your data needs to be prepared for machine learning. Machine learning operates best when you provide clean data as the foundation for building your models.
The 7.6 release of the Elastic Stack delivered the last piece required for an end-to-end machine learning pipeline. Previously, machine learning focused on unsupervised techniques with anomaly detection. However, several features have been released over the 7.x releases. In 7.2 Elasticsearch released transforms for turning raw indices into a feature index. Then 7.3, 7.4, and 7.5 released outlier detection, regression, and classification, respectively.
Use of AI can be critical when developing systems to support social good, with some inspiring examples using Splunk in healthcare and higher education organisations. According to our State of Dark Data report, however, only 15% of organisations admit they are utilising AI solutions today due to lack of skills. So how can we help organisations unlock the potential of AI?
