GOALS

• Develop a personalized recommendation engine that leverages real-time user behavior data to increase click-through rates by 15% and drive sales.
• Build a dynamic pricing model that reacts to competitor pricing changes, demand fluctuations, and inventory levels in real-time to maximize revenue and optimize profit margins.
• Create a system for detecting and preventing fraudulent account activity in real-time to enhance security, reduce financial losses, and improve customer trust.

Frustrations

• Encountering difficulties in accessing and processing real-time data from various sources, leading to delays in model training and deployment.
• Experiencing challenges in ensuring the reliability and scalability of real-time feature pipelines, impacting the performance and stability of ML models.
• Finding it time-consuming to debug and resolve data quality issues in real-time data streams, impacting model accuracy and decision-making.

GOALS

• Build a content recommendation system that personalizes suggestions for users based on their real-time interactions, increasing engagement metrics like average session duration and content shares.
• Develop a sentiment analysis model that analyzes real-time user comments and posts to identify potential brand risks, allowing for proactive reputation management.
• Create a real-time anomaly detection system to identify and flag suspicious user behavior patterns, preventing platform abuse and safeguarding the user experience.

Frustrations

• Dealing with the complexities of handling high-volume, high-velocity real-time data streams from social media platforms, requiring robust and scalable infrastructure.
• Facing challenges in maintaining data privacy and security while working with sensitive user information, adhering to ethical AI principles and regulations.
• Struggling to keep up with the rapidly evolving social media landscape and adapt ML models to new trends and platform changes, ensuring model accuracy and relevance over time.