Skip to main content
Amazon Machine Learning

Amazon Machine Learning

$348.00
Availability:
Downloadable Resources, Instant Access
Your guarantee:
30-day money-back guarantee — no questions asked
Toolkit Included:
Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
How you learn:
Self-paced • Lifetime updates
Who trusts this:
Trusted by professionals in 160+ countries
When you get access:
Course access is prepared after purchase and delivered via email
Adding to cart… The item has been added

What does the Amazon Machine Learning course cover?

Amazon Machine Learning is covered here in 10 modules: Strategic Alignment of Machine Learning with Enterprise Objectives, Data Strategy and Infrastructure Readiness on AWS, Feature Engineering and Management at Scale and 7 more. The outline lists 80 specific topics, opening with evaluate business KPIs to determine alignment between ML initiatives and corporate strategy, including revenue growth, cost optimization, and customer retention targets.

How do you approach Amazon Machine Learning step by step?

The work is sequenced in 10 stages. It starts with Strategic Alignment of Machine Learning with Enterprise Objectives, moves through Data Strategy and Infrastructure Readiness on AWS and Feature Engineering and Management at Scale, and ends at Financial and Operational Accountability of ML Initiatives. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Amazon Machine Learning course?

Module 1 is Strategic Alignment of Machine Learning with Enterprise Objectives. It works through evaluate business KPIs to determine alignment between ML initiatives and corporate strategy, including revenue growth, cost optimization, and customer retention targets., assess opportunity cost of ML investments versus alternative technology or process improvements across departments., define success criteria for ML projects using measurable operational outcomes, not just model.

How is the Amazon Machine Learning course delivered?

The Amazon Machine Learning course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Amazon Machine Learning course cost?

The Amazon Machine Learning course is $348 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: Machine Learning Toolkit, Azure Machine Learning, Machine Learning As Service in Machine Learning, Automated Machine Learning Toolkit.

More answers: what you get with every course, refund policy, all help answers.

This curriculum reflects the scope typically addressed across a full consulting engagement or multi-phase internal transformation initiative.

Module 1: Strategic Alignment of Machine Learning with Enterprise Objectives

  • Evaluate business KPIs to determine alignment between ML initiatives and corporate strategy, including revenue growth, cost optimization, and customer retention targets.
  • Assess opportunity cost of ML investments versus alternative technology or process improvements across departments.
  • Define success criteria for ML projects using measurable operational outcomes, not just model accuracy.
  • Map stakeholder incentives and constraints to anticipate resistance or support during ML adoption.
  • Conduct feasibility triage to prioritize use cases based on data availability, technical complexity, and business impact.
  • Establish governance thresholds for model deployment, including minimum performance benchmarks and risk tolerance levels.
  • Navigate trade-offs between speed-to-insight and long-term model maintainability in project scoping.
  • Integrate ML roadmaps with existing IT and data architecture planning cycles to avoid siloed development.

Module 2: Data Strategy and Infrastructure Readiness on AWS

  • Design data ingestion pipelines using AWS services (Kinesis, Glue, S3) that balance latency, cost, and fault tolerance.
  • Implement data versioning and lineage tracking to support reproducibility and audit requirements.
  • Assess data quality gaps and their impact on model reliability, including missingness, bias, and schema drift.
  • Structure data lakes to enable cross-functional access while enforcing role-based security and compliance policies.
  • Optimize storage tiers (S3 Standard, IA, Glacier) based on access frequency and regulatory retention needs.
  • Evaluate trade-offs between batch and real-time processing for feature engineering workloads.
  • Integrate metadata management tools (AWS Glue Data Catalog) to enable discoverability and reuse across teams.
  • Plan for data governance requirements under GDPR, CCPA, and industry-specific regulations in pipeline design.

Module 3: Feature Engineering and Management at Scale

  • Design reusable feature stores using Amazon SageMaker Feature Store to reduce redundancy and ensure consistency.
  • Implement feature validation rules to detect outliers, distribution shifts, and data leakage in production.
  • Balance feature complexity against model interpretability and training cost in high-dimensional spaces.
  • Establish refresh schedules for features based on staleness sensitivity and upstream data update frequency.
  • Apply dimensionality reduction techniques only when justified by compute constraints or model performance degradation.
  • Document feature logic and business meaning to support auditability and cross-team collaboration.
  • Monitor feature drift and correlation decay over time to trigger retraining or redesign.
  • Enforce access controls on sensitive features derived from PII or proprietary business data.

Module 4: Model Development and Algorithm Selection

  • Select algorithms based on data size, label availability, latency requirements, and explainability needs.
  • Compare performance of built-in SageMaker algorithms (XGBoost, Linear Learner) versus custom models for cost and accuracy trade-offs.
  • Implement cross-validation strategies appropriate to temporal, spatial, or hierarchical data structures.
  • Quantify and document bias in training data and its potential amplification in model outputs.
  • Optimize hyperparameters using SageMaker Hyperparameter Tuning with constraints on compute budget and time.
  • Assess model calibration and confidence scoring for high-stakes decision domains.
  • Develop fallback logic for edge cases where model confidence falls below operational thresholds.
  • Balance model complexity against inference cost and scalability in production environments.

Module 5: Model Evaluation Beyond Accuracy

  • Define evaluation metrics aligned with business outcomes (e.g., precision-recall for fraud detection, RMSE for forecasting).
  • Analyze performance disparities across demographic or operational segments to identify fairness issues.
  • Conduct A/B testing of model variants using Amazon CloudWatch and statistical significance thresholds.
  • Measure inference latency and throughput under peak load to validate service level objectives.
  • Estimate economic impact of false positives and false negatives in domain-specific contexts.
  • Validate model robustness to adversarial inputs or data perturbations in high-risk applications.
  • Track model decay over time using holdout datasets and scheduled re-evaluation protocols.
  • Document model limitations and boundary conditions for stakeholder communication and risk management.

Module 6: Secure and Governed Model Deployment

  • Deploy models using SageMaker endpoints with autoscaling, canary, and rollback configurations.
  • Implement VPC isolation, encryption (in transit and at rest), and IAM policies for model artifacts and APIs.
  • Establish CI/CD pipelines for ML using SageMaker Pipelines and AWS CodeBuild with approval gates.
  • Enforce model signing and provenance tracking to prevent unauthorized or unvetted deployments.
  • Integrate model monitoring with existing enterprise incident response and alerting systems.
  • Define ownership and accountability for models in production, including update and retirement protocols.
  • Conduct pre-deployment risk assessments for models influencing legal, financial, or safety-critical decisions.
  • Implement model explainability reports (using SageMaker Clarify) as part of deployment documentation.

Module 7: Monitoring, Observability, and Model Lifecycle Management

  • Configure SageMaker Model Monitor to detect data drift, concept drift, and quality deviations in real time.
  • Set alert thresholds based on statistical significance and business impact, minimizing false alarms.
  • Track model performance decay and correlate with external events or process changes.
  • Establish retraining triggers based on performance degradation, data drift, or scheduled intervals.
  • Manage model versioning and shadow mode testing to validate updates before cutover.
  • Archive deprecated models and associated artifacts in compliance with data retention policies.
  • Monitor inference cost per transaction and optimize instance types or batching strategies.
  • Conduct root cause analysis for model failures using integrated CloudWatch Logs and X-Ray tracing.

Module 8: Scaling ML Operations Across the Enterprise

  • Design centralized ML platforms that balance standardization with team autonomy.
  • Implement shared services for feature stores, model registries, and monitoring to reduce duplication.
  • Define SLAs for model development, deployment, and support across business units.
  • Allocate cloud spending by team or project using AWS Cost Allocation Tags and budgets.
  • Establish cross-functional ML review boards to evaluate high-impact or high-risk models.
  • Develop playbooks for incident response, model rollback, and stakeholder communication.
  • Scale talent strategy by identifying upskilling needs and defining roles (ML engineer, data scientist, MLOps).
  • Measure organizational maturity using ML operational KPIs: deployment frequency, lead time, failure rate, and recovery time.
  • Conduct algorithmic impact assessments for models affecting individuals or regulated processes.
  • Implement bias detection and mitigation workflows using SageMaker Clarify and fairness metrics.
  • Document data provenance and consent status for training datasets involving personal information.
  • Design opt-out mechanisms and human-in-the-loop controls for automated decision systems.
  • Align model behavior with industry regulations (e.g., Fair Lending, HIPAA, MiFID II).
  • Establish audit trails for model decisions in high-compliance environments.
  • Navigate intellectual property rights for models trained on third-party or open-source data.
  • Develop escalation paths for ethical concerns raised by employees or external stakeholders.

Module 10: Financial and Operational Accountability of ML Initiatives

  • Build cost models for ML projects including compute, storage, labor, and opportunity costs.
  • Track ROI of deployed models by linking predictions to downstream business outcomes.
  • Compare total cost of ownership across managed (SageMaker) versus self-hosted solutions.
  • Optimize inference costs using spot instances, model quantization, or early exiting.
  • Report on model utilization rates to identify underused or redundant endpoints.
  • Conduct post-implementation reviews to assess whether models met original business objectives.
  • Forecast capacity needs for ML workloads based on business growth and data volume trends.
  • Integrate ML spend into enterprise financial planning and capital expenditure cycles.
!