AI Integration Specialist

Alec Attie

(0)

Starting at

$

80

/hr

About this service

Summary

Alec integrated AI capabilities into applications using PyTorch, Python, OpenAI APIs, and Langchain.

Process

I start by planning the project and gathering requirements. This begins with a kick-off meeting with stakeholders to understand their needs and expectations. Together, we define the project’s objectives, clearly outlining the problem to be solved and the desired outcomes. I document the functional and non-functional requirements, user stories, and acceptance criteria. Then, I develop a detailed project roadmap with milestones, timelines, and resource allocation.
Next, I focus on data collection and preparation. I identify and gather relevant data from various sources, such as databases, APIs, or web scraping. After collecting the data, I clean it by removing duplicates, handling missing values, and correcting inconsistencies. I then transform the data, normalizing and standardizing it to make it suitable for model training. To better understand the data, I perform exploratory data analysis (EDA), examining distributions, relationships, and potential issues.
With the data prepared, I move on to model development. I select appropriate algorithms and model architectures based on the problem type, whether it’s classification, regression, or clustering. I use the training data to develop models, experimenting with different features and hyperparameters. To optimize model performance, I engage in hyperparameter tuning, using techniques like grid search or random search. Throughout this process, I evaluate the model using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC.
After developing the model, I ensure it performs well on unseen data and meets project requirements. I use validation datasets to test the model and prevent overfitting, often performing k-fold cross-validation to evaluate stability and robustness. I generate and analyze performance metrics, including confusion matrices and ROC curves. Based on validation results and stakeholder feedback, I iteratively refine the model.
Once the model is validated, I prepare for deployment. I develop scripts to serve the model via APIs or web services, using tools like Flask or FastAPI. I containerize the application with Docker to ensure consistent deployment. I set up the necessary cloud infrastructure, whether on AWS, Azure, or GCP, to host the model. Additionally, I implement continuous integration and deployment (CI/CD) pipelines to automate testing and ensure smooth transitions from development to production.
Finally, I focus on monitoring and maintaining the deployed model. I set up systems to track its performance and detect any issues, ensuring it continues to operate effectively. I also plan for scheduled retraining and validation to keep the model up-to-date with new data. Throughout this entire process, I maintain regular communication with stakeholders, providing updates and seeking their input to ensure the project aligns with their vision and expectations.

What's included

  • Project Plan and Timeline

    Detailed project roadmap with milestones and deadlines.

  • Requirements Documentation

    Functional and non-functional requirements. User stories and acceptance criteria.

  • Data Collection and Preparation

    Data acquisition plan. Raw data sets. Data cleaning and preprocessing scripts. Exploratory data analysis (EDA) report.

  • Model Development

    Jupyter notebooks or scripts for model training. Description of model architecture and algorithms used. Hyperparameter tuning details. Model training logs and metrics.

  • Model Evaluation

    Evaluation metrics and performance reports (e.g., accuracy, precision, recall).

  • Model Documentation

    Detailed description of the final model. Assumptions and limitations. Guidelines for model retraining and updates.

  • Integration

    API endpoints for model inference. Integration with existing systems and applications. Load testing and performance optimization reports.

  • Documentation

    Code documentation (comments, readme files). API documentation (Swagger, Postman collections). User manuals and guides for using the model and API.

  • Final Delivery and Handover:

    Complete source code repository. Access credentials for all services and environments. Final project report and summary. Post-deployment support plan.

Skills and tools

AI Developer
Django
OpenAI
PyTorch

Industries

Software

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