Congratulations!
1. Congratulations!
Congratulations on completing the course!2. Chapter 1
Over these four chapters, we've covered the complete process of setting up forecasting automation. In Chapter 1, we introduced Nixtla, our core forecasting framework.3. Chapter 2
In Chapter 2, we dove into the experimentation process, model tracking, and evaluation with MLflow.4. Chapter 3
In Chapter 3, we reviewed how to design data and ML pipelines with Airflow.5. Chapter 4
And in Chapter 4, we learned about the foundations of model deployment and monitoring.6. Next steps
We've covered a lot of ground - some applied, some theoretical. Now it's your turn to connect all the dots and apply these learnings to real-life problems. You can start by identifying opportunities for applying forecast automation with your own data. Alternatively, look for open-source data to build automation. The EIA API we used throughout this course is a great resource, with over one million time series from the energy field, including daily and hourly live data. Although we demonstrated the process using Python and open-source tools like Nixtla, Airflow, and MLflow, the concepts are language- and framework-agnostic. You can apply them in other languages like R and with different forecasting and automation frameworks.7. Thank you!
I hope you enjoyed the course, and happy pipeline development and deployment!Create Your Free Account
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