AI/Machine Learning & Data Engineering in AWS, GCP and AzureMatthew Robb
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 work
Matthew's other services
Starting at$25 /hr
Tags
AWS
Azure
Google Cloud Platform
Data Engineer
ML Engineer
Software Engineer
Service provided by
Matthew Robb Edinburgh, UK
AI/Machine Learning & Data Engineering in AWS, GCP and AzureMatthew Robb
Starting at$25 /hr
Tags
AWS
Azure
Google Cloud Platform
Data Engineer
ML Engineer
Software Engineer
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 work
Matthew's other services
$25 /hr