ML/AL Service

Mohit kumar

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About this service

Summary

Problem Formulation: Identifying the problem that the client wants to solve and formulating it as an AI/ML problem.

Data Collection: Gathering relevant data from different sources and in different formats, such as databases, logs, images, videos, text, and audio.

Data Preparation and Feature Engineering: Cleaning, pre-processing, and transforming the data to make it suitable for training an AI/ML model. This involves techniques such as data normalization, feature selection, feature extraction, and dimensionality reduction.

Model Selection and Architecture Design: Choosing the appropriate AI/ML model architecture and configuration based on the problem statement and the data.

Model Training: Training the AI/ML model on the prepared data and evaluating its performance using appropriate metrics.

Model Deployment: Deploying the trained AI/ML model in a production environment to make predictions or take actions.

What's included

deliverables for AL/ML
Data Preparation and Feature Engineering: This includes the cleaned, pre-processed and transformed data that will be used to train the AI/ML model. Model Training Results: This includes the trained models and their associated metrics that were generated during the training process. Deployment Strategy: This involves a detailed plan for deploying the AI/ML model in a production environment, including infrastructure requirements, deployment architecture, and software stack. Model Performance Report: A report that outlines the model's performance metrics such as accuracy, precision, recall, F1-score, and other metrics specific to the client's use case. Source Code: The source code for the AI/ML model, as well as any associated scripts, tools, and libraries. API Documentation: A comprehensive documentation outlining how to interact with the deployed AI/ML model's API.

Example projects