Case Study: Automating Employment Agreement Analysis

Scenario:
 Technology Solutions is developing an Employment Agreement Management System. As an AI/ML Developer intern, you are required to create a machine learning model that can analyze employment agreements to extract key information and identify any potential discrepancies or missing elements.

Tasks:

Data Collection and Preprocessing:

Collect a dataset of employment agreements, ensuring diversity in terms of content and structure.
Preprocess the text data, including steps like tokenization, stop-word removal, and lemmatization.
Feature Engineering:
Identify and extract relevant features from the employment agreements. Features could include clauses related to salary, job role, termination conditions, etc.
Use techniques such as TF-IDF, word embeddings (Word2Vec, GloVe), or other relevant methods to represent the text data.

Model Development:

Develop a machine learning model to classify and extract key information from the employment agreements. You may use algorithms like Named Entity Recognition (NER), text classification, or sequence-to-sequence models.
Train and validate the model using appropriate metrics to evaluate its performance (e.g., precision, recall, F1-score).

Cloud Services Integration:

Deploy the trained model on a cloud platform such as AWS, Google Cloud, or Azure. Use services like AWS SageMaker, Google AI Platform, or Azure Machine Learning.
Implement an API endpoint that allows the Employment Agreement Management System to interact with the model for real-time analysis.

Evaluation and Improvement:

Evaluate the model’s performance on a test dataset and identify areas for improvement.
Fine-tune the model and retrain it to enhance its accuracy and reliability.

Documentation:

Document the entire process, including data collection, preprocessing steps, feature engineering, model development, and cloud deployment.
Provide a user guide on how to use the API for analyzing employment agreements.

Submission Requirements:

A detailed report (6-8 pages) documenting the data collection, preprocessing, feature engineering, model development, and cloud deployment process.
Source code for the data preprocessing, feature extraction, and model training.
Instructions for deploying the model on a cloud platform and using the API endpoint.
A brief presentation (slides) summarizing the key steps, findings, and results of the project.

Evaluation Criteria:

Technical Knowledge:

Proficiency in data preprocessing and feature engineering for text data.
Knowledge of machine learning algorithms and their application to text analysis.

Practical Application:

Ability to develop and train an effective machine learning model.
Competence in deploying models on cloud platforms and creating APIs.

Problem-Solving Skills:

Effectiveness in identifying and addressing challenges during the project.
Strategies for improving model performance based on evaluation metrics.

Communication:

Clarity and coherence in the documentation and presentation.
Ability to explain complex technical concepts in a user-friendly manner.

By completing this case study, you will demonstrate your AI/ML development skills and practical understanding of applying these techniques to a real-world problem, which are essential for an AI/ML Developer role . We look forward to seeing your innovative solutions and thorough understanding of AI/ML practices.