We integrated Airbyte, Superset (<a href="https://superset.apache.org" rel="nofollow">https://superset.apache.org</a>) , and Streamlit (<a href="https://streamlit.io/" rel="nofollow">https://streamlit.io/</a>) with our ML platform, (<a href="https://iko.ai" rel="nofollow">https://iko.ai</a>).<p>The platform provides real-time collaborative notebooks so people can train, track, package, deploy, and monitor machine learning models on Kubernetes.<p>Depending on the client and their data sources, our people can get data using Airbyte. Then build dashboards using Superset. They also can deploy a Streamlit application right from the notebook without worrying about spinning up a VM on GCP, set the environment, deploy, add authentication, etc. as the platform does that. They can invoke the deployed models from there.<p>Especially with projects that move fast, the cycle needs to follow to show results to our clients and we shave off inefficiencies as we go.