| Tool | Description |
|---|---|
| 1. Apache Airflow | Open-source platform to programmatically author, schedule, and monitor workflows. |
| 2. [Kubeflow] | Kubernetes-native platform for developing, orchestrating, deploying, and running scalable and portable ML workloads. |
| 3. MLflow | Open-source platform to manage the end-to-end machine learning lifecycle. |
| Question | Description |
|---|---|
| 1. What is the problem you're trying to solve with ML? | Define the specific problem or use case. |
| 2. What is the input data format and source? | Describe the data sources and their formats. |
| 3. How will you preprocess the data? | Discuss data cleaning, normalization, and feature engineering. |
| 4. What machine learning algorithms will you use? | Specify the models and why you chose them. |
| 5. How will you evaluate model performance? | Define metrics and evaluation strategies. |
| 6. How will you handle model deployment and monitoring? | Discuss the deployment process and monitoring plan. |