Kubeflow | MLflow | Metaflow | Flyte | ZenML | Airflow | Argo | Tekton | Prefect | Luigi
“The public does not know what is possible. We do.”
— Akio Morita, co-founder of Sony 
I was given an excellent opportunity while working on a customer project to share some of my findings related to Task and Workflow Orchestration tooling analysis.
After completing a project and moving to a next one you realize that the landscape of available tools has changed, sometimes drastically and the basis for reasoning on what to use in your project with it.
“You know, it’s moments like these when I realize what a superhero I am.”
Tony Stark 
Until recently, developers in the embedded space didn’t have much opportunity to be exposed to Machine Learning due to the enduring perception of strict hardware limitations. It seemed that power-hungry algorithms like deep neural networks required cloud-based high-end processors or beefy graphic cards combined with a lot of prior scientific knowledge. The reality is that embedded devices already have all the necessary power and most of the required tools are free and open source with extensive tutorials and documentation.
Ai Engineer | Cloud Architect | DevOps Expert