In this chapter, you learned about ML architecture and components for a ML workflow. You learned about how data and ML go hand in hand. It is essential to get high-quality data with feature engineering to build the right ML model.
You learned about ML model validation by recognizing model overfit versus underfit situations. You also learned about various supervised and unsupervised ML algorithms. As the cloud is becoming a go-to platform for ML model training and deployment, you learned about ML platforms in popular public cloud providers.
Further, you learned about the ML workflow, including data preprocessing, modeling, evaluation, and prediction. Also, you learned about building ML architecture with a detailed reference architecture built in AWS cloud platforms. MLOps is essential for putting ML models in production. You learned about MLOps principles and best practices. Further, you got an overview of deep learning, which helps solve complex problems by mimicking...