Introduction to Data, Analytics, and Machine Learning
Data, analytics and machine learning are the foundation for AI (artificial intelligence). The challenge with data is the variety across locations (cloud, on-prem, private cloud), types (structured, unstructured), and platforms (operational database, data warehouse, hadoop, fast data platforms, etc). Once we deal with our data management, we are able to move on to analytics, which lets us extract insight from our data. Predictive analytics leads us to machine learning. Once we develop enough machine learning models, we are beginning to reach AI.
IBM's solution to managing big data is the Hybrid Data Management Platform. Check it out: https://ibm.co/2JNMyKp
Follow Caleb on Twitter: http://bit.ly/2LUrOTe
Follow Db2 on Twitter: http://bit.ly/2AfIHq4
Follow IBM Analytics: http://bit.ly/2K0jek7
Data, analytics and machine learning are the foundation for AI (artificial intelligence). The challenge with data is the variety across locations (cloud, on-prem, private cloud), types (structured, unstructured), and platforms (operational database, data warehouse, hadoop, fast data platforms, etc). Once we deal with our data management, we are able to move on to analytics, which lets us extract insight from our data. Predictive analytics leads us to machine learning. Once we develop enough machine learning models, we are beginning to reach AI.
IBM’s solution to managing big data is the Hybrid Data Management Platform. Check it out: https://ibm.co/2JNMyKp
Follow Caleb on Twitter: http://bit.ly/2LUrOTe
Follow Db2 on Twitter: http://bit.ly/2AfIHq4
Follow IBM Analytics: http://bit.ly/2K0jek7
