Learn about different cloud environments and tools for building scalable data and model pipelines.

Become a Data Scientist

In this module, we’ll cover different cloud environments and tools for building scalable data and model pipelines. We’ll learn the different data sets and types of models that will be used heavily in everyday production. Throughout the module, we’ll have plenty of exercises and challenges to get us comfortable working with the diverse toolset. Lastly, we’ll explore streaming model workflows that are crucial for building real-time data pipelines.
By the end of this module, we'll have a better understanding of how to build scalable machine learning pipelines in a cloud environment.

Data Science in Production: Building Scalable Model Pipelines

Multiple Python libraries have the ______________ datasets for testing out models.

Multiple Python libraries have the ______________ datasets for testing out models.

```python 3 
import pandas as pd
gamesDF = pd.read_csv("https://github.com/bgweber/Twitch/raw/master/Recommendations/games-expand.csv")
gamesDF.head()
```

How many records will this code display?

<pre><code class="language-python">import pandas as pd
gamesDF = pd.read_csv(&quot;https://github.com/bgweber/Twitch/raw/master/Recommendations/games-expand.csv&quot;)
gamesDF.head()
</code></pre>
How many records will this code display?

You need to set up new Google Cloud credentials every time you query BigQuery from a Python script.

You need to set up new Google Cloud credentials every time you query BigQuery from a Python script.

Pull Kaggle datasets directly into Pandas dataframes.

Use the Kaggle library to programmatically download CSV files and then push it to Pandas dataframes.


Load the data present in Kaggle library directly to Pandas dataframes.

How do we fetch data from Kaggle datasets into Panda dataframes?

How do we fetch data from Kaggle datasets into Panda dataframes?

Introduction to Building Scalable Model Pipelines

Models as Web Endpoints

Models as Serverless Functions

Containers for Reproducible Models

Workflow Tools for Model Pipelines

PySpark for Batch Pipelines

Cloud Dataflow for Batch Modeling

Streaming Model Workflows

Conclusion

Quiz - Importing Datasets