You’ve reached the end of the last course of the Machine Learning on Google Cloud course series.

Congratulations.

We hope you’ve enjoyed your journey through machine learning, and have practiced using the skills and services you’ve learned about here.

Let’s have a quick recap of the series.

We designed this content for dedicated or aspiring machine learning, data scientists engineers, and analysts who are interested in machine learning in the Cloud using,

Vertex AI Platform,

BigQuery ML,

TensorFlow,

and Keras.

There were five courses in this series.

In how Google does machine learning, you learned about different best practices for implementing machine learning on Google Cloud, particularly, with Vertex AI.

We also discussed what exactly machine learning is, what kinds of problems it can solve, and how to recognize bias in machine learning workflow.

Launching into machine learning discussed, the importance of data quality and performing exploratory data analysis.

You learned how to use Vertex AI AutoML, the benefits of BigQuery ML and how to optimize and assess the quality of ML models.

In TensorFlow on Google Cloud, you learned how to use TensorFlow and Keras, to design and build ML models to solve** machine learning problems**.

By the next course, feature engineering, you learned about Vertex AI feature store and how to improve the accuracy and performance of your ML models.

We reviewed how to distinguish good features from bad features, and you practiced how to perform feature engineering in both BigQuery ML and Keras.

The final course, which you just wrapped up, is machine learning in the enterprise.

This is our newest course.

Here, we reviewed a real-world practical approach to the ML workflow.

You learned more about custom training and even practiced it yourself.

So if you haven’t done so, we highly recommend and encourage you to complete all the labs in each course, they give you real hands-on practice with the different concepts and services we’ve discussed.

Make sure you check out all of the resources we’ve provided, we want to support you as you begin or continue on your machine learning journey.

Thanks for joining us.