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 The end-to-end architecture of the predictive model for estimating delay times for multiple transportation routes should be configured using Kubeflow Pipelines. Kubeflow Pipelines is a platform for building and deploying scalable, portable, and reusable machine learning pipelines on Kubernetes. Kubeflow Pipelines allows you to orchestrate your multi-step workflow from data preparation, model training, model evaluation, model deployment, and model serving. Kubeflow Pipelines also provides a user interface for managing and tracking your pipeline runs, experiments, and artifacts1

Using Kubeflow Pipelines has several advantages for this use case:

Full automation: You can define your pipeline as a Python script that specifies the steps and dependencies of your workflow, and use the Kubeflow Pipelines SDK to compile and upload your pipeline to the Kubeflow Pipelines service. You can also use the Kubeflow Pipelines UI to create, run, and monitor your pipeline2

Scalability: You can leverage the power of Kubernetes to scale your pipeline components horizontally and vertically, and use distributed training frameworks such as TensorFlow or PyTorch to train your model on multiple nodes or GPUs3

Portability: You can package your pipeline components as Docker containers that can run on any Kubernetes cluster, and use the Kubeflow Pipelines SDK to export and import your pipeline packages across different environments4

Reusability: You can reuse your pipeline components across different pipelines, and share your components with other users through the Kubeflow Pipelines Component Store. You can also use pre-built components from the Kubeflow Pipelines library or other sources5

Schedulability: You can use the Kubeflow Pipelines UI or the Kubeflow Pipelines SDK to schedule recurring pipeline runs based on cron expressions or intervals. For example, you can schedule your pipeline to run every month to retrain your model on the latest data.

The other options are not as suitable for this use case. Using a model trained and deployed on BigQuery ML is not recommended, as BigQuery ML is mainly designed for simple and quick machine learning tasks on large-scale data, and does not support complex models or custom code. Writing a Cloud Functions script that launches a training and deploying job on AI Platform is not ideal, as Cloud Functions has limitations on the memory, CPU, and execution time, and does not provide a user interface for managing and tracking your pipeline. Using Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model is not optimal, as Dataflow is mainly designed for data processing and streaming analytics, and does not support model serving or monitoring.

References: 1: Kubeflow Pipelines overview 2: Build a pipeline 3: Scale your machine learning training and prediction workloads 4: Export and import pipelines 5: Build components and pipelines : [Schedule recurring pipeline runs] : [BigQuery ML overview] : [Cloud Functions documentation] : [Dataflow documentation]

 A data pipeline is a set of steps or processes that move data from one or more sources to one or more destinations, usually for the purpose of analysis, transformation, or storage. A data pipeline can be designed using various components, such as data sources, data processing tools, data storage systems, and data analytics tools1

To design a data pipeline for analyzing customer sentiments in each call, one should consider the following requirements and constraints:

The call center receives over one million calls daily, and data is stored in Cloud Storage. This implies that the data is large, unstructured, and distributed, and requires a scalable and efficient data processing tool that can handle various types of data formats, such as audio, text, or image.

The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. This implies that the data is sensitive and subject to data privacy and compliance regulations, and requires a secure and reliable data storage system that can enforce data encryption, access control, and regional policies.

The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. This implies that the data analytics tool is external and independent of the data pipeline, and requires a standard and compatible data interface that can support SQL queries and operations.

One of the best options for selecting components for data processing and for analytics is to use Dataflow for data processing and BigQuery for analytics. Dataflow is a fully managed service for executing Apache Beam pipelines for data processing, such as batch or stream processing, extract-transform-load (ETL), or data integration. BigQuery is a serverless, scalable, and cost-effective data warehouse that allows you to run fast and complex queries on large-scale data23

Using Dataflow and BigQuery has several advantages for this use case:

Dataflow can process large and unstructured data from Cloud Storage in a parallel and distributed manner, and apply various transformations, such as converting audio to text, extracting sentiment scores, or anonymizing PII. Dataflow can also handle both batch and stream processing, which can enable real-time or near-real-time analysis of the call data.

BigQuery can store and analyze the processed data from Dataflow in a secure and reliable way, and enforce data encryption, access control, and regional policies. BigQuery can also support SQL ANSI-2011 compliant interface, which can enable the data science team to use their third-party tool for visualization and access. BigQuery can also integrate with various Google Cloud services and tools, such as AI Platform, Data Studio, or Looker.

Dataflow and BigQuery can work seamlessly together, as they are both part of the Google Cloud ecosystem, and support various data formats, such as CSV, JSON, Avro, or Parquet. Dataflow and BigQuery can also leverage the benefits of Google Cloud infrastructure, such as scalability, performance, and cost-effectiveness.

The other options are not as suitable or feasible. Using Pub/Sub for data processing and Datastore for analytics is not ideal, as Pub/Sub is mainly designed for event-driven and asynchronous messaging, not data processing, and Datastore is mainly designed for low-latency and high-throughput key-value operations, not analytics. Using Cloud Function for data processing and Cloud SQL for analytics is not optimal, as Cloud Function has limitations on the memory, CPU, and execution time, and does not support complex data processing, and Cloud SQL is a relational database service that may not scale well for large-scale data. Using Cloud Composer for data processing and Cloud SQL for analytics is not relevant, as Cloud Composer is mainly designed for orchestrating complex workflows across multiple systems, not data processing, and Cloud SQL is a relational database service that may not scale well for large-scale data.

References: 1: Data pipeline 2: Dataflow overview 3: BigQuery overview : [Dataflow documentation] : [BigQuery documentation]

AI Platform supports hyperparameter tuning for PyTorch models using custom containers. This allows you to use any Python dependencies and libraries that are not included in the pre-built AI Platform Training runtime versions. You can also use a pre-trained model such as ResNet as a base for your custom model. To run a hyperparameter tuning job on AI Platform using custom containers, you need to do the following steps:

Create a Dockerfile that defines the container image for your training application. The Dockerfile should install PyTorch and any other dependencies, copy your training code and configuration files, and set the entrypoint for the container.

Build the container image and push it to Container Registry or another accessible registry.

Create a YAML file that defines the configuration for your hyperparameter tuning job. The YAML file should specify the container image URI, the training input and output paths, the hyperparameters to tune, the metric to optimize, and the tuning algorithm and budget.

Submit the hyperparameter tuning job to AI Platform using the gcloud command-line tool or the AI Platform Training API.

References:

Hyperparameter tuning overview

Using custom containers

PyTorch on AI Platform Training

 The best option for building a recommendation system without any user event data is to use simple heuristics based on content metadata. This is a type of content-based filtering, which recommends items that are similar to the ones that the user has interacted with or selected, based on their attributes. For example, if a user selects a comedy movie from the US released in 2020, the system can recommend other comedy movies from the US released in 2020 or nearby years. This approach does not require any machine learning, but it can leverage the existing metadata of the videos to provide relevant recommendations. It also allows the system to start collecting user event data, such as views, likes, ratings, etc., which can be used to train a more sophisticated machine learning model in the future, such as a collaborative filtering model or a hybrid model that combines content and collaborative information. References:

Recommendation Systems

Content-Based Filtering

Collaborative Filtering

Hybrid Recommender Systems: A Systematic Literature Review