Workshop

Prototyping a high confidence AI/ML model to a target business problem statement is the first step to operationalize ML. Further, making the ML model available for business to aid effective decision making is the end goal. This session will explore the landscape and look at different approaches to deploy and scale ML models to production. Additionally, the session will introduce few approaches for ML lifecycle management. The session will include hands-on experience to attendees on model serving using a pretrained model with an overview of the training pipeline. This working session will look at:

Hands-on approach to pipelines and their orchestration using TFX/Airflow.
API/SDK approach to model deployment as a web service with Flask
Pre-requisite:

Laptop with minimum of 8 GB ram with Windows/Linux/MacOS
Anaconda (individual edition) installed
Good internet connection to download coding stubs and pretrained model
Docker desktop installed
Basic familiarity with google collab with a google account

Text data inherently have lot of noise like the punctuations, stop words etc. which needs to be removed before building a machine learning model. To build an ML model out of text the text data needs to be vectorized. Text vectorization can be done using frequency-based techniques or using some of the neural net based pre-trained models. In this session you will learn about:
1. Steps to clean the text data
2. Obtain inferences through text data through word cloud and bar graphs
3. Vectorize text
4. Build ML models for text classification
5. Apply Neural net pre-trained models for text classification