In the traditional model-centric AI lifecycle, the focus is on finding better models to enhance performance, with data largely unchanged. This approach overlooks data quality issues like missing values, incorrect labels, and anomalies. Data-centric AI shifts the emphasis from models to systematic data engineering to build AI systems.
Recently, an excel data with many cells need to query from chatgpt and feed its response into the same table as an results. How can we have it based on current google framework? Please read and following this article to enable it..
Recently, an excel data with many cells need to query from chatgpt and feed its response into the same table as an results. How can we have it based on current google framework? Please read and following this article to enable it..
The basic pipeline of grafana/prometheus/alertmanager/influxDB
The complete block diagram
Related to alert event system, there are two questions, what events will trigger the alert (so called alert event trigger) and when context (so called alert event context) will show to user when alert.
Technology must align with the goals of User needs and Businees Goals or it is just a works make things more complicated.
with this idea, you can work smarter, not harder.
Technology mainly focus on system integration and its related components and output as Biz,Process (flow), Services, Products and etc.
The component can be Algorithm, Application, Software, Hardware, System.
