Leveraging Google Pre-trained ML APIs with Databricks
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Chapter 1: Introduction to Unstructured Data Pipelines
The integration of unstructured data into data pipelines presents unique challenges that differ significantly from traditional data engineering tasks. Here, artificial intelligence can play a crucial role. Let’s examine an example that demonstrates the synergy between Databricks and Google Cloud.
When dealing with structured data, constructing data pipelines is typically a straightforward endeavor. This process relies on pre-structured data, allowing for minor cleansing and transformation steps. In contrast, unstructured data requires a more robust approach to data enrichment, which is a vital stage in data pipelines. The objective here is to augment the inherent value of raw data to benefit business stakeholders.
A particularly beneficial aspect of enriched data is the ability to assign meaning to unstructured formats like free-form text, documents, images, and videos. This process involves employing machine learning classification models to create metadata that can be stored alongside the unstructured data. However, this task is often fraught with challenges.
Organizations frequently encounter difficulties stemming from the contrasting platforms that manage structured versus unstructured data. They also struggle with the development or procurement of high-quality models, a task known for being time-consuming. The operational and maintenance demands of these models further complicate matters, especially when seeking to minimize costs. In this context, leveraging Google's pre-trained ML APIs within Databricks can significantly alleviate the workload for Data Engineers.
Chapter 2: Setting Up Google’s Natural Language API
In this scenario, Databricks operates on Google Cloud Storage, though it is also compatible with AWS or Azure. After ingesting data into the "Bronze layer," the next step involves Data Enrichment. To utilize Google's Natural Language APIs, the following steps are necessary:
- Activate the Natural Language API in your Google Cloud project.
- Generate an API key for authentication.
- Install the client library for your chosen programming language.
- Write code to interact with the Natural Language API, passing text or content for analysis and receiving a response.
The Google APIs facilitate a range of analytic capabilities, including:
- Sentiment Analysis: Evaluating digital text to ascertain whether the emotional tone is positive, negative, or neutral.
- Entity Analysis: Scrutinizing text for recognized entities (proper nouns such as notable individuals, landmarks, etc.).
- Syntax and Data Quality Analysis.
For instance, if you're conducting sentiment analysis, the API can return valuable insights.
This information can subsequently enhance your data processing, simplifying later analysis by Data Analysts and Business Users. Attempting to execute such machine learning tasks independently using alternative cloud services or integrating Python scripts can be considerably more complex and challenging to maintain. While there may be scenarios requiring different analytical approaches not supported by these APIs, it could be worthwhile to experiment with them, perhaps starting with a small proof of concept.
Sources and Further Readings
[1] Alteryx, What is Data Enrichment? (2024)
[2] Google, Enriching Databricks Pipelines with Google Cloud's pre-trained ML APIs (2024)
Chapter 3: Practical Applications and Video Resources
To gain further insights into leveraging pre-trained ML APIs, check out the following videos:
The first video provides an overview of how to work with Google's pre-trained machine learning APIs on the Google Cloud Platform, showcasing practical applications and examples.
The second video illustrates the process of deploying and serving models from Azure Databricks onto Azure Machine Learning, offering valuable guidance for implementation.