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# Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud

### You have the data, but what are you doing with it?

:::info
**Objectives**
- Explore big data managed services in the cloud.
- Examine using Dataproc to run Apache Hadoop, Apache Spark, and other big data technologies as a managed service in the cloud.
- Learn about building ETL pipelines as a managed service by using Dataflow.
- Explore BigQuery as a managed data warehouse and analytics engine.
:::

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**Dataproc: Qwik Start - Console**

:::success
**Insight**
1. Create a cluster
2. Submit a job
3. View the job output
4. Update a cluster
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* Which type of Dataproc job is submitted in the lab? Spark
* Dataproc helps users process, transform and understand vast quantities of data. True

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**Dataproc: Qwik Start - Command Line**

:::success
**Insight**
1. Create a cluster
2. Submit a job
3. Update a cluster
:::

* Clusters can be created and scaled quickly with a variety of virtual machine types, disk sizes, and number of nodes. True

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**Dataflow: Qwik Start - Templates**

:::success
**Insight**
1. Create a Cloud BigQuery dataset and table Using Cloud Shell
2. Create a Cloud BigQuery dataset and table using the Cloud Console
3. Run the pipeline
4. Submit a query
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* Google Cloud Dataflow supports batch processing. True
* Which Dataflow Template used in the lab to run the pipeline? Pub/Sub to BigQuery

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**Dataflow: Qwik Start - Python**

:::success
**Insight**
1. Create a Cloud Storage bucket
2. Install pip and the Cloud Dataflow SDK
3. Run an example pipeline remotely
4. Check that your job succeeded
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* Dataflow temp_location must be a valid Cloud Storage URL. True

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**Dataprep: Qwik Start**

:::success
**Insight**
1. Create a Cloud Storage bucket in your project
2. Initialize Cloud Dataprep
3. Create a flow
4. Import datasets
5. Prep the candidate file
6. Wrangle the Contributions file and join it to the Candidates file
7. Summary of data
8. Rename columns
:::

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**Quiz**

1. Which of Google Cloud’s big data managed services is optimized for large-scale batch processing or long-running stream processing of structured and unstructured data? Dataflow
2. Which of these is a managed Spark and Hadoop service that lets you benefit from open source data tools for batch processing, querying, streaming, and machine learning? Dataproc
3. You can use three basic patterns to load data into BigQuery. Which one involves using SQL statements to insert rows into an existing table or to write the results of a query to a table? Generated data
4. Which of these is not a feature of BigQuery? BigQuery runs on your on-premises server.

### Let machines do the work

:::info
**Objectives**
- Define machine learning (ML), understand the terminology used, and identify the value proposition.
- Explore Vertex AI, Google’s unified AI platform.
- Use Google APIs to apply a range of pre-trained ML models.
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**Vertex AI: Qwik Start**

:::success
**Insight**
1. Enable Google Cloud services
2. Create Vertex AI custom service account for Vertex Tensorboard integration
3. Launch Vertex AI Workbench notebook
4. Clone the lab repository
5. Install lab dependencies
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**Cloud Natural Language API: Qwik Start**

:::success
**Insight**
1. Create an API key
2. Make an entity analysis request
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**Google Cloud Speech API: Qwik Start**

:::success
**Insight**
1. Create an API key
2. Create your Speech API request
3. Call the Speech API
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**Video Intelligence: Qwik Start**

:::success
**Insight**
1. Enable the Video Intelligence API
2. Set up authorization
3. Make an annotate video request
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**Quiz**

1. Which of these is a no-code solution that lets you build your own machine learning models on Vertex AI through a point-and-click interface? AutoML
2. What is the name of Google’s unified AI platform that brings all the components of the machine learning ecosystem and workflow together? Vertex AI
3. What Google machine learning API can be used to convert audio to text for data processing? Speech-to-Text API