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| Section | Objectives |
|---|---|
| Topic 1: Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
| Topic 2: Collaborating within and across teams to manage data and models | - Data management and governance - Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers |
| Topic 3: Automating and orchestrating ML pipelines | - Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines - CI/CD for ML systems |
| Topic 4: Serving and scaling models | - Model optimization (Quantization, Distillation) - Batch prediction - Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving |
| Topic 5: Architecting low-code ML solutions | - AutoML capabilities and implementation - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - Implementing BigQuery ML for basic models |
| Topic 6: Monitoring ML solutions | - Performance monitoring and drift detection - Model retraining strategies - Logging and alerting (Cloud Monitoring) |
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NEW QUESTION # 302
You are the Director of Data Science at a large company, and your Data Science team has recently begun using the Kubeflow Pipelines SDK to orchestrate their training pipelines. Your team is struggling to integrate their custom Python code into the Kubeflow Pipelines SDK. How should you instruct them to proceed in order to quickly integrate their code with the Kubeflow Pipelines SDK?
Answer: B
Explanation:
The easiest way to integrate custom Python code into the Kubeflow Pipelines SDK is to use the func_to_container_op function, which converts a Python function into a pipeline component. This function automatically builds a Docker image that executes the Python function, and returns a factory function that can be used to create kfp.dsl.ContainerOp instances for the pipeline. This option has the following benefits:
* It allows the data science team to reuse their existing Python code without rewriting it or packaging it into containers manually.
* It simplifies the component specification and implementation, as the function signature defines the component interface and the function body defines the component logic.
* It supports various types of inputs and outputs, such as primitive types, files, directories, and dictionaries.
The other options are less optimal for the following reasons:
* Option B: Using the predefined components available in the Kubeflow Pipelines SDK to access Dataproc, and run the custom code there, introduces additional complexity and cost. This option requires creating and managing Dataproc clusters, which are ephemeral and scalable clusters of Compute Engine instances that run Apache Spark and Apache Hadoop. Moreover, this option requires writing the custom code in PySpark or Hadoop MapReduce, which may not be compatible with the existing Python code.
* Option C: Packaging the custom Python code into Docker containers, and using the load_component_from_file function to import the containers into the pipeline, introduces additional steps and overhead. This option requires creating and maintaining Dockerfiles, building and pushing Docker images, and writing component specifications in YAML files. Moreover, this option requires managing the dependencies and versions of the Python code and the Docker images.
* Option D: Deploying the custom Python code to Cloud Functions, and using Kubeflow Pipelines to trigger the Cloud Function, introduces additional latency and limitations. This option requires creating and deploying Cloud Functions, which are serverless functions that execute in response to events.
Moreover, this option requires invoking the Cloud Functions from the Kubeflow Pipelines using HTTP requests, which can incur network overhead and latency. Additionally, this option is subject to the quotas and limits of Cloud Functions, such as the maximum execution time and memory usage.
References:
* Building Python function-based components | Kubeflow
* Building Python Function-based Components | Kubeflow
NEW QUESTION # 303
You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?
Answer: A
Explanation:
Google has bought this software and support for this tool is not good. SQL can work in Cloud fusion pipelines too but I would prefer to use a single tool like Bigquery to both transform and store data.
NEW QUESTION # 304
You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they're interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:
1. Check for availability of the movie tickets at the selected cinema.
2. Assign the ticket price and accept payment.
3. Reserve the tickets at the selected cinema.
4. Send successful purchases to your database.
Each step in this process has low latency requirements (less than 50 milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process.
You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do?
Answer: B
Explanation:
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.
References:
* [Exporting models for prediction | BigQuery ML]
* [tfx_bsl.public.beam.run_inference | TensorFlow Extended]
* [Vertex AI documentation]
* [TensorFlow Lite documentation]
NEW QUESTION # 305
You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest most efficient approach. What should you do?
Answer: A
NEW QUESTION # 306
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
Answer: A
NEW QUESTION # 307
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