1- Developing AI Models:
* Model Creation: Designing and building machine learning models to solve specific problems.
* Model Training: Training models using large datasets to ensure accuracy and efficiency.
* Model Deployment: Transforming models into APIs for integration with other systems.
2- Data Management:
* Data Collection: Gathering and preprocessing data from various sources.
* Data Cleaning: Ensuring data quality by removing inconsistencies and errors.
* Data Storage: Managing databases and data warehouses to store large volumes of data.
3- Statistical Analysis:
* Data Analysis: Using statistical methods to analyze data and extract meaningful insights.
* Performance Metrics: Evaluating model performance using metrics like accuracy, precision, recall, and F1 score.
* Optimization: Fine-tuning models to improve performance and efficiency.
4- Collaboration:
* Cross-functional Teams: Working with data scientists, software engineers, and product managers.
* Stakeholder Communication: Explaining complex AI concepts to non-technical stakeholders.
* Project Management: Leading AI projects from conception to deployment.