Professional-Machine-Learning-Engineer Leak Questions

The simplest way to deploy a logistic regression model with BigQuery ML to production while adding minimal latency is to export the model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline. This option has the following advantages:

It allows the model prediction to be performed in real time, as part of the Dataflow streaming pipeline that processes the ticket purchase requests. This ensures that the promo code offer is based on the most recent data and customer behavior, and that the offer is delivered to the customer without delay.

It leverages the compatibility and performance of TensorFlow and Dataflow, which are both part of the Google Cloud ecosystem. TensorFlow is a popular and powerful framework for building and deploying machine learning models, and Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. By using the tfx_bsl.public.beam.RunInference step, you can easily integrate your TensorFlow model with your Dataflow pipeline, and take advantage of the parallelism and scalability of Dataflow.

It simplifies the model deployment and management, as the model is packaged with the Dataflow pipeline and does not require a separate service or endpoint. The model can be updated by redeploying the Dataflow pipeline with a new model version.

The other options are less optimal for the following reasons:

Option A: Running batch inference with BigQuery ML every five minutes on each new set of tickets issued introduces additional latency and complexity. This option requires running a separate BigQuery job every five minutes, which can incur network overhead and latency. Moreover, this option requires storing and retrieving the intermediate results of the batch inference, which can consume storage space and increase the data transfer time.

Option C: Exporting the model in TensorFlow format, deploying it on Vertex AI, and querying the prediction endpoint from the streaming pipeline introduces additional latency and cost. This option requires creating and managing a Vertex AI endpoint, which is a managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. However, querying the Vertex AI endpoint from the streaming pipeline requires making an HTTP request, which can incur network overhead and latency. Moreover, this option requires paying for the Vertex AI endpoint usage, which can increase the cost of the model deployment.

Option D: Converting the model with TensorFlow Lite (TFLite), and adding it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub introduces additional challenges and risks. This option requires converting the model to a TFLite format, which is a lightweight and optimized format for running TensorFlow models on mobile and embedded devices. However, converting the model to TFLite may not preserve the accuracy or functionality of the original model, as some operations or features may not be supported by TFLite. Moreover, this option requires updating the mobile app with the TFLite model, which can be tedious and time-consuming, and may depend on the user’s willingness to update the app. Additionally, this option may expose the model to potential security or privacy issues, as the model is running on the user’s device and may be accessed or modified by malicious actors.

Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models 1 .  You can import models from various sources, such as BigQuery ML, AutoML, or custom models, and assign them to different versions and aliases 1 .  You can also deploy models to endpoints, which are resources that provide a service URL for online prediction 2 .

By importing the new model to the same Vertex AI Model Registry as a different version of the existing model, you can keep track of the model versions and compare their performance metrics 1 .  You can also use aliases to label the model versions according to their readiness for production, such as  default  or  staging 1 .

By deploying the new model to the same Vertex AI endpoint as the existing model, you can use traffic splitting to gradually shift the production traffic from the old model to the new model 2 .  Traffic splitting is a feature that allows you to specify the percentage of prediction requests that each deployed model in an endpoint should handle 2 .  This way, you can minimize the impact to the existing and future model users, and monitor the performance of the new model over time 2 .

The other options are not suitable for your scenario, because they either require creating a separate endpoint or a Cloud Run service, which would increase the complexity and maintenance of your deployment, or they do not allow you to use traffic splitting, which would create a sudden change in your prediction results.  References :

Introduction to Vertex AI Model Registry | Google Cloud

Deploy a model to an endpoint | Vertex AI | Google Cloud

Categorical cross-entropy is a loss function that is suitable for multi-class classification problems, where the target variable has more than two possible values. Categorical cross-entropy measures the difference between the true probability distribution of the target classes and the predicted probability distribution of the model. It is defined as:

L = - sum(y_i * log(p_i))

where y_i is the true probability of class i, and p_i is the predicted probability of class i. Categorical cross-entropy penalizes the model for making incorrect predictions, and encourages the model to assign high probabilities to the correct classes and low probabilities to the incorrect classes.

For the use case of building a model that predicts whether images contain a driver’s license, passport, or credit card, categorical cross-entropy is the appropriate loss function to use. This is because the problem is a multi-class classification problem, where the target variable has three possible values: [‘drivers_license’, ‘passport’, ‘credit_card’]. The label map is a list that maps the class names to the class indices, such that ‘drivers_license’ corresponds to index 0, ‘passport’ corresponds to index 1, and ‘credit_card’ corresponds to index 2. The model should output a probability distribution over the three classes for each image, and the categorical cross-entropy loss function should compare the output with the true labels. Therefore, categorical cross-entropy is the best loss function for this use case.