AWS AI, Machine Learning & Generative AI

Operationalise My AI & ML

Production-grade AI/ML engineering and MLOps environments on AWS.

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The challenge

Where organisations get stuck

AI prototypes work in development but struggle to become reliable production systems.

Manual deployments
Model drift
Poor monitoring
Governance challenges
Inconsistent environments
Difficult model lifecycle management

Abstract neural network over compute hardware representing AWS AI and machine learning

How Analytics Hive helps

AWS AI/ML Engineering & MLOps

Analytics Hive builds production-grade AI/ML engineering and MLOps environments on AWS.

What the engagement includes

Model pipelines
Model deployment
Model registry
CI/CD
Monitoring
Governance
Infrastructure as Code
Model lifecycle management

AWS services we build on

SageMaker
SageMaker Pipelines
Model Registry
CloudWatch
ECR
ECS/EKS
Lambda

Expected outcome

From AI experimentation to reliable production AI.

What we deliver

Tangible outputs, not slideware

Typical artefacts from a ai & machine learning engagement, tailored to the scope we agree with you.

AI opportunity assessment and prioritised use-case roadmap
AI and data readiness review
Generative AI solution architecture with guardrails
Retrieval-augmented knowledge assistants
Predictive model development and evaluation
MLOps pipelines, monitoring and retraining strategy
Responsible AI governance framework

Our approach

How the work runs

01
Assess
Understand your business objectives, current environment, risks and opportunities.
02
Architect
Design the right AWS target architecture and transformation roadmap.
03
Build
Implement the cloud, data, AI and security capabilities required.
04
Secure
Embed security, governance, resilience and compliance into the environment.

Business outcomes

What the business gains

Technical work only matters when it changes how the organisation performs.

01
Repeatable deployments
02
Monitored model performance
03
Governed AI lifecycle

AWS context

Built on AWS-native capability

AI & Machine Learning work at Analytics Hive is delivered on AWS and aligned to the AWS Well-Architected Framework.

Amazon Bedrock and foundation model selection
Amazon SageMaker AI for training and deployment
Amazon Kendra and Amazon OpenSearch for retrieval
AWS Step Functions and Lambda for AI workflows
Amazon Textract, Comprehend and Transcribe
SageMaker Pipelines, Model Registry and Model Monitor
Amazon S3 data lakes as the AI data foundation

Built around enterprise priorities

Security-first
Protect workloads, identities and data with controls designed in from the start.
Scalable
Architect AWS environments that grow with demand instead of constraining it.
Resilient
Design for availability, continuity and tested recovery, not best-effort uptime.
Cost-conscious
Optimise infrastructure spend without compromising performance or resilience.

Next step