Harnessing the Power of NLP to Cleanse the Social Media Landscape
Install Required Google Cloud CLI before run : Click
- Initealize first in CLI, use this command:
gcloud initgive GCP credentials here [it can also be checkedin while completing GCP CLI Setup] - Provide Project Name, Select Compute Resion
- Create a Bucket with unique name (to save data)
- Upload data in the Bucket (simple drag and drop csv file from local to new bucket)
NOTE:
- There are plethora of tutorials on how to setup GCP CLI
- Don'r forget to add artifacts in git ignore
Gathering Ingredients:
- We have various suppliers (data sources) like text files, databases, or even zipped bags (zip files) full of data.
- We first unpack any zipped bags in the "unzip_and_clean" step to make sure everything is accessible.
- Then, we set up our kitchen workstations (new directories) to keep things organized.
Preparing the Feast:
- This is where the real cooking starts! We clean and chop the ingredients (data preparation).
- This might involve removing unwanted parts, cutting them into smaller pieces (e.g., words from sentences), or even bringing in special seasoning (pretrained model weights, if needed).
- We follow a specific recipe (data ingestion) that involves tasks like transforming the data into formats our model understands and maybe even cooking with pre-trained flavors (model loading).
Serving Delicious Outcome:
- Finally, the finished dish (processed data)! This could be predictions from our model, like predicting sentiment, or neatly prepared data files ready for further analysis.
- We serve this tasty output to hungry customers (models or analysts) who can use it to make informed decisions or create even more insights.
Remember:
- The order of some steps might be flexible depending on our recipe (implementation).
- Some details might be hidden like specific cleaning techniques or seasoning ingredients, but the overall flow remains the same.
Remaining Pipelines to Add: Model Evaluation and Model Push Back to GCP