Google Cloud Certified Data Engineer: 9. Deploying Machine Learning Pipelines - Important Things To Know

1. Know the stages of ML pipelines. Data ingestion, data preparation, data segregation, model training, model evaluation, model deployment, and model monitoring are the stages of ML pipelines. Although the stages are listed in a linear manner, ML pipelines are more cyclic than linear, especially relating to training and evaluation.
2. Understand batch and streaming ingestion. Batch data ingestion should use a dedicated process for ingesting each distinct data source. Batch ingestion often occurs on a relatively fixed schedule, much like many data warehouse ETL processes. It is important to be able to track which batch data comes from, so include a batch identifier with each record that is ingested. Cloud Pub/Sub is designed for scalable messaging, including ingesting streaming data. Cloud Pub/Sub is a good option for ingesting streaming data that will be stored in a database, such as Bigtable or Cloud Firebase, or immediately consumed by machine learning processes running in Cloud Dataflow, Cloud Dataproc, Kubernetes Engine, or Compute Engine. When using BigQuery, you have the option of using streaming inserts.
3. Know the three kinds of data preparation. The three kinds of data preparation are data exploration, data transformation, and feature engineering. Data exploration is the first step in working with a new data source or a data source that has had significant changes.
The goal of this stage is to understand the distribution of data and the overall quality of data. Data transformation is the process of mapping data from its raw form into data structures and formats that allow for machine learning. Transformations can include replacing missing values with a default value, changing the format of numeric values, and deduplicating records. Feature engineering is the process of adding or modifying the representation of features to make implicit patterns more explicit. For example, if a ratio of two numeric features is important to classifying an instance, then calculating that ratio and including it as a feature may improve the model quality. Feature engineering includes the understanding of key attributes (features) that are meaningful for machine learning objectives at hand. This includes dimensional reduction.
4. Know that data segregation is the process splitting a dataset into three segments: training, validation, and test data. Training data is used to build machine learning models.
5. Validation data is used during hyperparameter tuning. Test data is used to evaluate model performance. The main criteria for deciding how to split data are to ensure that the test and validation datasets are large enough to produce statistically meaningful results, that test and validation datasets are representative of the data as a whole, and that the training dataset is large enough for the model to learn to make accurate predictions with reasonable precision and recall.
6. Understand the process of training a model. Know that feature selection is the process of evaluating how a particular attribute or feature contributes to the predictiveness of a model. The goal is to have features of a dataset that allow a model to learn to make accurate predictions. Know that underfitting creates a model that is not able to predict values of training data correctly or new data that was not used during training.
7 . Understand underfitting, overfitting, and regularization. The problem of underfitting may be corrected by increasing the amount of training data, using a different machine learning algorithm, or modifying hyperparameters. Understand that overfitting occurs when a model fits the training data too well. One way to compensate for the impact of noise in the data and reduce the risk of overfitting is by introducing a penalty for data points, which makes the model more complicated. This process is called regularization. Two kinds of regularization are L1 regularization, which is also known as Lasso Regularization, for Least Absolute Shrinkage and Selection Operator, and L2 or Ridge Regression.
8. Know ways to evaluate a model. Methods for evaluation a model include individual evaluation metrics, such as accuracy, precision, recall, and the F measure; k-fold cross-validation; confusion matrices; and bias and variance. K-fold cross-validation is a technique for evaluating model performance by splitting a data set into k segments, where k is an integer.
Confusion matrices are used with classification models to show the relative performance of a model. In the case of a binary classifier, a confusion matrix would be 2×2, with one column and one row for each value.
9. Understand bias and variance. Bias is the difference between the average prediction of a model and the correct prediction of a model. Models with high bias tend to have oversimplified models; this is underfitting the model. Variance is the variability in model predictions. Models with high variance tend to overfit training data so that the model works well when making predictions on the training data but does not generalize to data that the model has not seen before.
10. Know options for deploying machine learning workloads on GCP. These options include Cloud AutoML, BigQuery ML, Kubeflow, and Spark MLib. Cloud AutoML is a machine learning service designed for developers who want to incorporate machine learning in their applications without having to learn many of the details of ML. BigQuery ML enables users of the analytical database to build machine learning models using SQL and data in BigQuery datasets. Kubeflow is an open source project for developing, orchestrating, and deploying scalable and portable machine learning workloads. Kubeflow is designed for the Kubernetes platform. Cloud Dataproc is a managed Spark and Hadoop service. Included with Spark is a machine learning library called MLib, and it is a good option for machine learning workloads if you are already using Spark or need one of the more specialized algorithms included in Spark MLib.