AI/Machine Learning & Data Engineering in AWS, GCP and Azure

Matthew Robb

(0)

Starting at

/hr

About this service

Summary

Machine Learning Services I can provide:

Modelling: Both supervised and unsupervised. Predictive models, forecasting, object detection, anomaly detection, novelty detection.

Model Evaluation: thorough model evaluation through exhaustive hyper-parameter tuning and feature engineering.

Documentation: a step-by-step and comprehensive document detailing the process of feature engineering, experiments, decisioning and findings.

ML Ops Services I can provide:

Cloud and ML system architecture solutions.

Implementing ML system monitoring solutions (data, models and infrastructure)

Implementing CICD pipelines within focus on continuous model deployment and improvement.

Implementing ingestion, feature engineering and inference pipelines.

Testing.

Data Engineering Services I can provide:

Data ingestion and extraction from various sources

Data storage solutions such as databases and data warehouses

Data processing and transformation using tools such as Apache Spark and Apache Flink

Data security and privacy, including encryption and access control

Data visualization and reporting

Performance tuning and optimization of data system

The complexity of this project will vary substantially depending on the client’s needs.

The following stages will be included as part of the service:

Requirements Gathering

Architectural Design Review

Implementation Documentation Review

Handover

What's included

Data Engineering Deliverables
The deliverables may vary depending on the project requirements. However, some common deliverables I can provide are: - Data pipelines for the ingestion, processing and storage of data from various sources. - Data warehousing solutions for large-scale data storage and retrieval. - Data quality reports to ensure the accuracy and completeness of data. - Data security and privacy policies to ensure the protection of sensitive data. - Performance metrics and monitoring systems to monitor the performance of data systems. - Documentation of the data architecture, processes, and systems. - Technical support and maintenance of data systems.
Machine Learning / ML Ops Deliverables
The deliverables for a machine learning project can vary depending on the scope and requirements of the project. However, some common deliverables I can provide are: - A well-defined and trained machine learning model - Performance metrics to evaluate the accuracy and effectiveness of the model - Comprehensive documentation. - Monitoring pipeline capable of detecting drift and model degradation. - Automated training pipeline. - Automated feature engineering and inference pipeline. - Monitoring Dashboard - CICD Pipelines (GitHub / Azure DevOps) - Comprehensive testing (Unit, Integration -and System) - Infrastructure as code (Terraform) - Comprehensive handover documentation and support.

Example projects