Mastering Machine Learning Engineering for Production-Ready AI Systems
You’re not just building models anymore. You’re expected to deliver AI systems that survive real-world conditions, scale across infrastructure, and generate measurable business value. The pressure is real. Deadlines loom. Stakeholders demand results. And right now, you might feel like you’re translating academic prototypes into production systems without a reliable blueprint. What if you could go from concept to a fully operational, board-ready AI deployment in under 30 days? Not just a demo, but a robust, monitored, governed system trusted by engineering and executives alike. That transition-from promising prototype to production-grade asset-is exactly what this program is engineered to enable. In Mastering Machine Learning Engineering for Production-Ready AI Systems, you gain a battle-tested, industry-aligned framework used by teams at leading tech firms to ship models that last. No theory without application. No fluff. Just the exact sequence of decisions, tools, and architecture patterns that lead to successful AI deployment and long-term maintenance. Take Sarah Chen, Senior Data Scientist at a global logistics firm. After completing this course, she led the deployment of a routing optimisation model that cut dispatch delays by 27%. Her leadership was recognised with a promotion within two quarters. She didn’t learn new algorithms-she mastered the engineering layer that made her work impossible to ignore. You already know machine learning. What’s missing is the production discipline. This course fills that gap with precision, equipping you with the systems thinking, deployment automation, and operational rigor needed to stand out in a crowded field. Here’s how this course is structured to help you get there.Course Format & Delivery Details This is a self-paced, fully on-demand program with immediate online access upon registration. There are no fixed start dates, no scheduled sessions, and no time commitments-learn at your own pace, anytime, from anywhere in the world. What’s Included
- Lifetime access to all course materials, with all future updates delivered at no additional cost
- 24/7 global access compatible with desktop, tablet, and mobile devices
- A comprehensive, structured learning journey designed for maximum retention and real-world application
- Step-by-step guidance with decision frameworks, architecture templates, and deployment checklists
- Hands-on implementation exercises using industry-standard tools and environments
- Access to an exclusive set of professional-grade resources: configuration blueprints, monitoring dashboards, CI/CD pipelines, and security compliance templates
- Direct instructor support via curated Q&A pathways for targeted clarification and troubleshooting
- A Certificate of Completion issued by The Art of Service, a globally recognised credential trusted by IT professionals and engineering leaders in over 125 countries
Most learners complete the core curriculum within 40 hours and begin applying transformational changes to their workflows and projects within the first two weeks. The course is designed for immediate ROI-every module connects to a real business or technical challenge you can solve before finishing. Zero Risk. Full Confidence.
We understand the hesitation. Many programs promise career transformation but deliver generic content that doesn’t translate to your actual environment. That’s why this course operates under a simple guarantee: Satisfied or fully refunded. If you complete the first three modules and don’t find immediate, actionable value, simply request a refund-no questions asked. You’ll receive a confirmation email immediately after enrollment. Access credentials and entry instructions will be delivered separately once your learner profile is provisioned and the full suite of materials is prepared for you. This program works even if: - You’ve never deployed a model beyond Jupyter Notebook
- Your current stack lacks MLOps tooling
- You work in a regulated industry with strict compliance requirements
- You’re not a software engineer-but need to collaborate like one
- You’ve been told your models “aren’t ready for production”
Pricing is straightforward with no hidden fees or recurring charges. One single investment grants you full, lifetime access, including every future update to reflect evolving tools, best practices, and certification standards. We accept Visa, Mastercard, and PayPal-securely processed with bank-grade encryption. From engineers at FAANG companies to AI leads in healthcare and finance, professionals rely on The Art of Service for technically rigorous, career-accelerating training. This course continues that tradition-delivering not just knowledge, but documented proof of mastery through a globally respected certification.
Extensive and Detailed Course Curriculum
Module 1: Foundations of Production Machine Learning - Defining production-ready AI: beyond accuracy to reliability, scalability, and governance
- Key differences between research prototypes and production systems
- The organisational impact of AI deployment failures
- Understanding the full ML lifecycle from ideation to retirement
- Common anti-patterns in model deployment and how to avoid them
- Regulatory and ethical implications of AI in high-stakes environments
- Role of ML Engineers vs Data Scientists vs Data Engineers in production workflows
- Establishing ownership and accountability across model development and operations
- Measuring success: defining KPIs for model performance, system health, and business impact
- Creating alignment between technical teams and business stakeholders
Module 2: Architecting Scalable ML Systems - Designing for failure: fault tolerance in ML pipelines
- Choosing between batch, streaming, and real-time inference architectures
- Service-oriented design for ML components
- Event-driven ML systems using message queues and pub-sub patterns
- Stateless vs stateful model serving and when to use each
- Latency, throughput, and scalability trade-offs in model deployment
- Microservices patterns for model isolation and independent scaling
- Data contracts and API versioning for ML services
- Multi-tenancy considerations in shared model platforms
- Hybrid and multi-cloud deployment strategies for redundancy and compliance
Module 3: Model Development and Training Infrastructure - Reproducible training environments using containerisation
- Managing large-scale training on distributed clusters
- Efficient data loading and preprocessing for training pipelines
- Distributed training frameworks: Horovod, TensorFlow MultiWorkerMirroredStrategy
- Hyperparameter tuning at scale with automated search strategies
- Checkpointing, early stopping, and model saving best practices
- Versioning datasets, code, and model configurations together
- Automated training workflow orchestration with workflow engines
- Cost optimisation for training infrastructure: spot instances, auto-scaling
- Monitoring training job health and resource utilisation
Module 4: Model Packaging and Versioning - Model serialization formats: Pickle, ONNX, PMML, TensorFlow SavedModel
- Interoperability considerations across frameworks and languages
- Containerising models for deployment with Docker
- Building lightweight inference images using multi-stage builds
- Version control for trained models using dedicated model registries
- Metadata tagging for models: lineage, authors, datasets, accuracy metrics
- Immutable model storage and retrieval systems
- Provenance tracking: linking models to training scripts and data versions
- Model card creation and compliance documentation
- Automating model packaging in CI/CD pipelines
Module 5: Model Deployment Patterns - Canary deployments for low-risk model rollout
- Blue-green deployments for zero-downtime updates
- Shadowing: running new models in parallel without affecting users
- A/B testing framework for model performance comparison
- Multi-armed bandit strategies for adaptive model selection
- Rollback mechanisms and automated failover triggers
- Edge deployment for low-latency, offline-capable models
- Federated learning deployment patterns
- Serverless inference with AWS Lambda, Google Cloud Functions
- GPU vs CPU inference: performance and cost trade-offs
Module 6: Model Serving Platforms - Introduction to TensorFlow Serving and TorchServe
- Using KServe for Kubernetes-native model serving
- Building custom model servers with FastAPI and Flask
- Request batching and dynamic batching for throughput optimisation
- Model warm-up and pre-loading strategies
- Multi-model serving: managing thousands of models efficiently
- Caching predictions for deterministic models
- Serving ensemble models and stacked architectures
- Supporting multiple frameworks in a single serving environment
- Security hardening for model serving endpoints
Module 7: Continuous Integration and Continuous Deployment (CI/CD) - Designing CI/CD pipelines for machine learning systems
- Automated testing for data quality, model performance, and code correctness
- Unit and integration testing for ML components
- Creating deployment gates based on model accuracy and drift thresholds
- Automated rollback triggers in CI/CD workflows
- Infrastructure as Code (IaC) for reproducible environments
- Terraform and Pulumi for cloud resource provisioning
- GitOps workflows for declarative deployment management
- Secrets management and secure pipeline execution
- Auditing and logging all changes in the deployment pipeline
Module 8: Data and Feature Engineering for Production - Feature stores: design, implementation, and integration
- Bulk and online feature serving patterns
- Feature versioning and consistency across training and serving
- Real-time feature computation with stream processing
- Data validation pipelines using Great Expectations and Soda Core
- Automated schema checking and data type enforcement
- Monitoring data freshness and completeness
- Handling missing data in production inference
- Feature lineage and impact analysis
- Building reusable, composable feature transformations
Module 9: Monitoring and Observability - Monitoring model performance: accuracy, precision, recall over time
- Tracking prediction latency, request rates, and error rates
- Concept drift detection using statistical tests and alerts
- Data drift detection with population stability index and KL divergence
- Feature drift and outlier detection in input data
- Monitoring for silent model failure
- Logging prediction requests and responses for audit trails
- Distributing tracing across microservices with Jaeger and OpenTelemetry
- Alerting strategies: threshold-based, anomaly detection, and machine learning
- Creating custom dashboards in Grafana and Prometheus
Module 10: Model Governance and Compliance - Establishing model risk management frameworks
- Model inventory and registry management
- Documentation requirements for regulatory compliance
- Model validation and audit processes
- Explainability reporting for regulatory submissions
- Data privacy considerations under GDPR, HIPAA, and CCPA
- Model fairness and bias audits across demographic groups
- Access control and role-based permissions for model access
- Rights to explanation and model contestability
- Creating and maintaining model risk logs
Module 11: Security and Ethical Considerations - Securing ML APIs with authentication, rate limiting, and encryption
- Protecting against model inversion and membership inference attacks
- Defending models from adversarial inputs and evasion attacks
- Model watermarking for IP protection
- Detecting and preventing data poisoning in training pipelines
- Securing pipeline dependencies and open-source packages
- Role of differential privacy in training and inference
- Ethical guidelines for AI deployment in sensitive domains
- Setting up model use policy enforcement
- Creating an AI ethics review board within organisations
Module 12: Testing in Production: Safe Experimentation - Designing safe canary launches with automatic rollback
- Shadow mode: validating new models using real traffic
- Chaos engineering for ML systems: simulating failures
- Canary analysis using statistical significance testing
- Automated goldenset evaluations in production
- Latency and load testing under peak traffic conditions
- Monitoring business KPIs during experimental launches
- Shadow databases for safe integration testing
- Safe rollback procedures and state recovery
- Post-mortem analysis of failed deployments
Module 13: High Availability and Disaster Recovery - Designing for 99.99% uptime in ML systems
- Automatic failover across availability zones and regions
- Backup and restore strategies for model artifacts and metadata
- Disaster recovery planning for ML platforms
- Load balancing across multiple model instances
- Rate limiting and circuit breakers for API protection
- Graceful degradation modes during partial failures
- Capacity planning for unexpected traffic spikes
- Monitoring health of dependent services and databases
- Automated recovery scripts and health checks
Module 14: Cost Management and Optimisation - Tracking compute costs per model and endpoint
- Right-sizing inference instances based on load patterns
- Auto-scaling strategies: horizontal and vertical
- Spot instances and preemptible VMs for cost-efficient training
- Model pruning and quantisation for efficient inference
- Batching strategies to reduce per-prediction cost
- Monitoring idle models and decommissioning unused endpoints
- Cost allocation tags and chargeback models
- Cloud billing alerts and budget thresholds
- Choosing between managed services and self-hosted solutions
Module 15: Real-World Implementation Projects - End-to-end project 1: Deploying a fraud detection model in finance
- Building a CI/CD pipeline for credit risk models
- Implementing drift detection and alerting system
- Creating a feature store for customer behaviour data
- Deploying a recommendation engine with A/B testing
- Setting up observability dashboards for API performance
- Implementing model governance in a healthcare use case
- Building a GDPR-compliant model deletion workflow
- Designing a disaster recovery plan for a mission-critical AI system
- Optimising a computer vision model for edge deployment
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts and decision frameworks
- Submitting a real-world implementation case study
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn, resumes, and professional profiles
- Leveraging certification in salary negotiations and promotions
- Joining a private network of certified ML Engineers
- Accessing advanced reading and supplemental resources
- Staying current with future updates and industry shifts
- Building a portfolio of production-grade implementation projects
Module 1: Foundations of Production Machine Learning - Defining production-ready AI: beyond accuracy to reliability, scalability, and governance
- Key differences between research prototypes and production systems
- The organisational impact of AI deployment failures
- Understanding the full ML lifecycle from ideation to retirement
- Common anti-patterns in model deployment and how to avoid them
- Regulatory and ethical implications of AI in high-stakes environments
- Role of ML Engineers vs Data Scientists vs Data Engineers in production workflows
- Establishing ownership and accountability across model development and operations
- Measuring success: defining KPIs for model performance, system health, and business impact
- Creating alignment between technical teams and business stakeholders
Module 2: Architecting Scalable ML Systems - Designing for failure: fault tolerance in ML pipelines
- Choosing between batch, streaming, and real-time inference architectures
- Service-oriented design for ML components
- Event-driven ML systems using message queues and pub-sub patterns
- Stateless vs stateful model serving and when to use each
- Latency, throughput, and scalability trade-offs in model deployment
- Microservices patterns for model isolation and independent scaling
- Data contracts and API versioning for ML services
- Multi-tenancy considerations in shared model platforms
- Hybrid and multi-cloud deployment strategies for redundancy and compliance
Module 3: Model Development and Training Infrastructure - Reproducible training environments using containerisation
- Managing large-scale training on distributed clusters
- Efficient data loading and preprocessing for training pipelines
- Distributed training frameworks: Horovod, TensorFlow MultiWorkerMirroredStrategy
- Hyperparameter tuning at scale with automated search strategies
- Checkpointing, early stopping, and model saving best practices
- Versioning datasets, code, and model configurations together
- Automated training workflow orchestration with workflow engines
- Cost optimisation for training infrastructure: spot instances, auto-scaling
- Monitoring training job health and resource utilisation
Module 4: Model Packaging and Versioning - Model serialization formats: Pickle, ONNX, PMML, TensorFlow SavedModel
- Interoperability considerations across frameworks and languages
- Containerising models for deployment with Docker
- Building lightweight inference images using multi-stage builds
- Version control for trained models using dedicated model registries
- Metadata tagging for models: lineage, authors, datasets, accuracy metrics
- Immutable model storage and retrieval systems
- Provenance tracking: linking models to training scripts and data versions
- Model card creation and compliance documentation
- Automating model packaging in CI/CD pipelines
Module 5: Model Deployment Patterns - Canary deployments for low-risk model rollout
- Blue-green deployments for zero-downtime updates
- Shadowing: running new models in parallel without affecting users
- A/B testing framework for model performance comparison
- Multi-armed bandit strategies for adaptive model selection
- Rollback mechanisms and automated failover triggers
- Edge deployment for low-latency, offline-capable models
- Federated learning deployment patterns
- Serverless inference with AWS Lambda, Google Cloud Functions
- GPU vs CPU inference: performance and cost trade-offs
Module 6: Model Serving Platforms - Introduction to TensorFlow Serving and TorchServe
- Using KServe for Kubernetes-native model serving
- Building custom model servers with FastAPI and Flask
- Request batching and dynamic batching for throughput optimisation
- Model warm-up and pre-loading strategies
- Multi-model serving: managing thousands of models efficiently
- Caching predictions for deterministic models
- Serving ensemble models and stacked architectures
- Supporting multiple frameworks in a single serving environment
- Security hardening for model serving endpoints
Module 7: Continuous Integration and Continuous Deployment (CI/CD) - Designing CI/CD pipelines for machine learning systems
- Automated testing for data quality, model performance, and code correctness
- Unit and integration testing for ML components
- Creating deployment gates based on model accuracy and drift thresholds
- Automated rollback triggers in CI/CD workflows
- Infrastructure as Code (IaC) for reproducible environments
- Terraform and Pulumi for cloud resource provisioning
- GitOps workflows for declarative deployment management
- Secrets management and secure pipeline execution
- Auditing and logging all changes in the deployment pipeline
Module 8: Data and Feature Engineering for Production - Feature stores: design, implementation, and integration
- Bulk and online feature serving patterns
- Feature versioning and consistency across training and serving
- Real-time feature computation with stream processing
- Data validation pipelines using Great Expectations and Soda Core
- Automated schema checking and data type enforcement
- Monitoring data freshness and completeness
- Handling missing data in production inference
- Feature lineage and impact analysis
- Building reusable, composable feature transformations
Module 9: Monitoring and Observability - Monitoring model performance: accuracy, precision, recall over time
- Tracking prediction latency, request rates, and error rates
- Concept drift detection using statistical tests and alerts
- Data drift detection with population stability index and KL divergence
- Feature drift and outlier detection in input data
- Monitoring for silent model failure
- Logging prediction requests and responses for audit trails
- Distributing tracing across microservices with Jaeger and OpenTelemetry
- Alerting strategies: threshold-based, anomaly detection, and machine learning
- Creating custom dashboards in Grafana and Prometheus
Module 10: Model Governance and Compliance - Establishing model risk management frameworks
- Model inventory and registry management
- Documentation requirements for regulatory compliance
- Model validation and audit processes
- Explainability reporting for regulatory submissions
- Data privacy considerations under GDPR, HIPAA, and CCPA
- Model fairness and bias audits across demographic groups
- Access control and role-based permissions for model access
- Rights to explanation and model contestability
- Creating and maintaining model risk logs
Module 11: Security and Ethical Considerations - Securing ML APIs with authentication, rate limiting, and encryption
- Protecting against model inversion and membership inference attacks
- Defending models from adversarial inputs and evasion attacks
- Model watermarking for IP protection
- Detecting and preventing data poisoning in training pipelines
- Securing pipeline dependencies and open-source packages
- Role of differential privacy in training and inference
- Ethical guidelines for AI deployment in sensitive domains
- Setting up model use policy enforcement
- Creating an AI ethics review board within organisations
Module 12: Testing in Production: Safe Experimentation - Designing safe canary launches with automatic rollback
- Shadow mode: validating new models using real traffic
- Chaos engineering for ML systems: simulating failures
- Canary analysis using statistical significance testing
- Automated goldenset evaluations in production
- Latency and load testing under peak traffic conditions
- Monitoring business KPIs during experimental launches
- Shadow databases for safe integration testing
- Safe rollback procedures and state recovery
- Post-mortem analysis of failed deployments
Module 13: High Availability and Disaster Recovery - Designing for 99.99% uptime in ML systems
- Automatic failover across availability zones and regions
- Backup and restore strategies for model artifacts and metadata
- Disaster recovery planning for ML platforms
- Load balancing across multiple model instances
- Rate limiting and circuit breakers for API protection
- Graceful degradation modes during partial failures
- Capacity planning for unexpected traffic spikes
- Monitoring health of dependent services and databases
- Automated recovery scripts and health checks
Module 14: Cost Management and Optimisation - Tracking compute costs per model and endpoint
- Right-sizing inference instances based on load patterns
- Auto-scaling strategies: horizontal and vertical
- Spot instances and preemptible VMs for cost-efficient training
- Model pruning and quantisation for efficient inference
- Batching strategies to reduce per-prediction cost
- Monitoring idle models and decommissioning unused endpoints
- Cost allocation tags and chargeback models
- Cloud billing alerts and budget thresholds
- Choosing between managed services and self-hosted solutions
Module 15: Real-World Implementation Projects - End-to-end project 1: Deploying a fraud detection model in finance
- Building a CI/CD pipeline for credit risk models
- Implementing drift detection and alerting system
- Creating a feature store for customer behaviour data
- Deploying a recommendation engine with A/B testing
- Setting up observability dashboards for API performance
- Implementing model governance in a healthcare use case
- Building a GDPR-compliant model deletion workflow
- Designing a disaster recovery plan for a mission-critical AI system
- Optimising a computer vision model for edge deployment
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts and decision frameworks
- Submitting a real-world implementation case study
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn, resumes, and professional profiles
- Leveraging certification in salary negotiations and promotions
- Joining a private network of certified ML Engineers
- Accessing advanced reading and supplemental resources
- Staying current with future updates and industry shifts
- Building a portfolio of production-grade implementation projects
- Designing for failure: fault tolerance in ML pipelines
- Choosing between batch, streaming, and real-time inference architectures
- Service-oriented design for ML components
- Event-driven ML systems using message queues and pub-sub patterns
- Stateless vs stateful model serving and when to use each
- Latency, throughput, and scalability trade-offs in model deployment
- Microservices patterns for model isolation and independent scaling
- Data contracts and API versioning for ML services
- Multi-tenancy considerations in shared model platforms
- Hybrid and multi-cloud deployment strategies for redundancy and compliance
Module 3: Model Development and Training Infrastructure - Reproducible training environments using containerisation
- Managing large-scale training on distributed clusters
- Efficient data loading and preprocessing for training pipelines
- Distributed training frameworks: Horovod, TensorFlow MultiWorkerMirroredStrategy
- Hyperparameter tuning at scale with automated search strategies
- Checkpointing, early stopping, and model saving best practices
- Versioning datasets, code, and model configurations together
- Automated training workflow orchestration with workflow engines
- Cost optimisation for training infrastructure: spot instances, auto-scaling
- Monitoring training job health and resource utilisation
Module 4: Model Packaging and Versioning - Model serialization formats: Pickle, ONNX, PMML, TensorFlow SavedModel
- Interoperability considerations across frameworks and languages
- Containerising models for deployment with Docker
- Building lightweight inference images using multi-stage builds
- Version control for trained models using dedicated model registries
- Metadata tagging for models: lineage, authors, datasets, accuracy metrics
- Immutable model storage and retrieval systems
- Provenance tracking: linking models to training scripts and data versions
- Model card creation and compliance documentation
- Automating model packaging in CI/CD pipelines
Module 5: Model Deployment Patterns - Canary deployments for low-risk model rollout
- Blue-green deployments for zero-downtime updates
- Shadowing: running new models in parallel without affecting users
- A/B testing framework for model performance comparison
- Multi-armed bandit strategies for adaptive model selection
- Rollback mechanisms and automated failover triggers
- Edge deployment for low-latency, offline-capable models
- Federated learning deployment patterns
- Serverless inference with AWS Lambda, Google Cloud Functions
- GPU vs CPU inference: performance and cost trade-offs
Module 6: Model Serving Platforms - Introduction to TensorFlow Serving and TorchServe
- Using KServe for Kubernetes-native model serving
- Building custom model servers with FastAPI and Flask
- Request batching and dynamic batching for throughput optimisation
- Model warm-up and pre-loading strategies
- Multi-model serving: managing thousands of models efficiently
- Caching predictions for deterministic models
- Serving ensemble models and stacked architectures
- Supporting multiple frameworks in a single serving environment
- Security hardening for model serving endpoints
Module 7: Continuous Integration and Continuous Deployment (CI/CD) - Designing CI/CD pipelines for machine learning systems
- Automated testing for data quality, model performance, and code correctness
- Unit and integration testing for ML components
- Creating deployment gates based on model accuracy and drift thresholds
- Automated rollback triggers in CI/CD workflows
- Infrastructure as Code (IaC) for reproducible environments
- Terraform and Pulumi for cloud resource provisioning
- GitOps workflows for declarative deployment management
- Secrets management and secure pipeline execution
- Auditing and logging all changes in the deployment pipeline
Module 8: Data and Feature Engineering for Production - Feature stores: design, implementation, and integration
- Bulk and online feature serving patterns
- Feature versioning and consistency across training and serving
- Real-time feature computation with stream processing
- Data validation pipelines using Great Expectations and Soda Core
- Automated schema checking and data type enforcement
- Monitoring data freshness and completeness
- Handling missing data in production inference
- Feature lineage and impact analysis
- Building reusable, composable feature transformations
Module 9: Monitoring and Observability - Monitoring model performance: accuracy, precision, recall over time
- Tracking prediction latency, request rates, and error rates
- Concept drift detection using statistical tests and alerts
- Data drift detection with population stability index and KL divergence
- Feature drift and outlier detection in input data
- Monitoring for silent model failure
- Logging prediction requests and responses for audit trails
- Distributing tracing across microservices with Jaeger and OpenTelemetry
- Alerting strategies: threshold-based, anomaly detection, and machine learning
- Creating custom dashboards in Grafana and Prometheus
Module 10: Model Governance and Compliance - Establishing model risk management frameworks
- Model inventory and registry management
- Documentation requirements for regulatory compliance
- Model validation and audit processes
- Explainability reporting for regulatory submissions
- Data privacy considerations under GDPR, HIPAA, and CCPA
- Model fairness and bias audits across demographic groups
- Access control and role-based permissions for model access
- Rights to explanation and model contestability
- Creating and maintaining model risk logs
Module 11: Security and Ethical Considerations - Securing ML APIs with authentication, rate limiting, and encryption
- Protecting against model inversion and membership inference attacks
- Defending models from adversarial inputs and evasion attacks
- Model watermarking for IP protection
- Detecting and preventing data poisoning in training pipelines
- Securing pipeline dependencies and open-source packages
- Role of differential privacy in training and inference
- Ethical guidelines for AI deployment in sensitive domains
- Setting up model use policy enforcement
- Creating an AI ethics review board within organisations
Module 12: Testing in Production: Safe Experimentation - Designing safe canary launches with automatic rollback
- Shadow mode: validating new models using real traffic
- Chaos engineering for ML systems: simulating failures
- Canary analysis using statistical significance testing
- Automated goldenset evaluations in production
- Latency and load testing under peak traffic conditions
- Monitoring business KPIs during experimental launches
- Shadow databases for safe integration testing
- Safe rollback procedures and state recovery
- Post-mortem analysis of failed deployments
Module 13: High Availability and Disaster Recovery - Designing for 99.99% uptime in ML systems
- Automatic failover across availability zones and regions
- Backup and restore strategies for model artifacts and metadata
- Disaster recovery planning for ML platforms
- Load balancing across multiple model instances
- Rate limiting and circuit breakers for API protection
- Graceful degradation modes during partial failures
- Capacity planning for unexpected traffic spikes
- Monitoring health of dependent services and databases
- Automated recovery scripts and health checks
Module 14: Cost Management and Optimisation - Tracking compute costs per model and endpoint
- Right-sizing inference instances based on load patterns
- Auto-scaling strategies: horizontal and vertical
- Spot instances and preemptible VMs for cost-efficient training
- Model pruning and quantisation for efficient inference
- Batching strategies to reduce per-prediction cost
- Monitoring idle models and decommissioning unused endpoints
- Cost allocation tags and chargeback models
- Cloud billing alerts and budget thresholds
- Choosing between managed services and self-hosted solutions
Module 15: Real-World Implementation Projects - End-to-end project 1: Deploying a fraud detection model in finance
- Building a CI/CD pipeline for credit risk models
- Implementing drift detection and alerting system
- Creating a feature store for customer behaviour data
- Deploying a recommendation engine with A/B testing
- Setting up observability dashboards for API performance
- Implementing model governance in a healthcare use case
- Building a GDPR-compliant model deletion workflow
- Designing a disaster recovery plan for a mission-critical AI system
- Optimising a computer vision model for edge deployment
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts and decision frameworks
- Submitting a real-world implementation case study
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn, resumes, and professional profiles
- Leveraging certification in salary negotiations and promotions
- Joining a private network of certified ML Engineers
- Accessing advanced reading and supplemental resources
- Staying current with future updates and industry shifts
- Building a portfolio of production-grade implementation projects
- Model serialization formats: Pickle, ONNX, PMML, TensorFlow SavedModel
- Interoperability considerations across frameworks and languages
- Containerising models for deployment with Docker
- Building lightweight inference images using multi-stage builds
- Version control for trained models using dedicated model registries
- Metadata tagging for models: lineage, authors, datasets, accuracy metrics
- Immutable model storage and retrieval systems
- Provenance tracking: linking models to training scripts and data versions
- Model card creation and compliance documentation
- Automating model packaging in CI/CD pipelines
Module 5: Model Deployment Patterns - Canary deployments for low-risk model rollout
- Blue-green deployments for zero-downtime updates
- Shadowing: running new models in parallel without affecting users
- A/B testing framework for model performance comparison
- Multi-armed bandit strategies for adaptive model selection
- Rollback mechanisms and automated failover triggers
- Edge deployment for low-latency, offline-capable models
- Federated learning deployment patterns
- Serverless inference with AWS Lambda, Google Cloud Functions
- GPU vs CPU inference: performance and cost trade-offs
Module 6: Model Serving Platforms - Introduction to TensorFlow Serving and TorchServe
- Using KServe for Kubernetes-native model serving
- Building custom model servers with FastAPI and Flask
- Request batching and dynamic batching for throughput optimisation
- Model warm-up and pre-loading strategies
- Multi-model serving: managing thousands of models efficiently
- Caching predictions for deterministic models
- Serving ensemble models and stacked architectures
- Supporting multiple frameworks in a single serving environment
- Security hardening for model serving endpoints
Module 7: Continuous Integration and Continuous Deployment (CI/CD) - Designing CI/CD pipelines for machine learning systems
- Automated testing for data quality, model performance, and code correctness
- Unit and integration testing for ML components
- Creating deployment gates based on model accuracy and drift thresholds
- Automated rollback triggers in CI/CD workflows
- Infrastructure as Code (IaC) for reproducible environments
- Terraform and Pulumi for cloud resource provisioning
- GitOps workflows for declarative deployment management
- Secrets management and secure pipeline execution
- Auditing and logging all changes in the deployment pipeline
Module 8: Data and Feature Engineering for Production - Feature stores: design, implementation, and integration
- Bulk and online feature serving patterns
- Feature versioning and consistency across training and serving
- Real-time feature computation with stream processing
- Data validation pipelines using Great Expectations and Soda Core
- Automated schema checking and data type enforcement
- Monitoring data freshness and completeness
- Handling missing data in production inference
- Feature lineage and impact analysis
- Building reusable, composable feature transformations
Module 9: Monitoring and Observability - Monitoring model performance: accuracy, precision, recall over time
- Tracking prediction latency, request rates, and error rates
- Concept drift detection using statistical tests and alerts
- Data drift detection with population stability index and KL divergence
- Feature drift and outlier detection in input data
- Monitoring for silent model failure
- Logging prediction requests and responses for audit trails
- Distributing tracing across microservices with Jaeger and OpenTelemetry
- Alerting strategies: threshold-based, anomaly detection, and machine learning
- Creating custom dashboards in Grafana and Prometheus
Module 10: Model Governance and Compliance - Establishing model risk management frameworks
- Model inventory and registry management
- Documentation requirements for regulatory compliance
- Model validation and audit processes
- Explainability reporting for regulatory submissions
- Data privacy considerations under GDPR, HIPAA, and CCPA
- Model fairness and bias audits across demographic groups
- Access control and role-based permissions for model access
- Rights to explanation and model contestability
- Creating and maintaining model risk logs
Module 11: Security and Ethical Considerations - Securing ML APIs with authentication, rate limiting, and encryption
- Protecting against model inversion and membership inference attacks
- Defending models from adversarial inputs and evasion attacks
- Model watermarking for IP protection
- Detecting and preventing data poisoning in training pipelines
- Securing pipeline dependencies and open-source packages
- Role of differential privacy in training and inference
- Ethical guidelines for AI deployment in sensitive domains
- Setting up model use policy enforcement
- Creating an AI ethics review board within organisations
Module 12: Testing in Production: Safe Experimentation - Designing safe canary launches with automatic rollback
- Shadow mode: validating new models using real traffic
- Chaos engineering for ML systems: simulating failures
- Canary analysis using statistical significance testing
- Automated goldenset evaluations in production
- Latency and load testing under peak traffic conditions
- Monitoring business KPIs during experimental launches
- Shadow databases for safe integration testing
- Safe rollback procedures and state recovery
- Post-mortem analysis of failed deployments
Module 13: High Availability and Disaster Recovery - Designing for 99.99% uptime in ML systems
- Automatic failover across availability zones and regions
- Backup and restore strategies for model artifacts and metadata
- Disaster recovery planning for ML platforms
- Load balancing across multiple model instances
- Rate limiting and circuit breakers for API protection
- Graceful degradation modes during partial failures
- Capacity planning for unexpected traffic spikes
- Monitoring health of dependent services and databases
- Automated recovery scripts and health checks
Module 14: Cost Management and Optimisation - Tracking compute costs per model and endpoint
- Right-sizing inference instances based on load patterns
- Auto-scaling strategies: horizontal and vertical
- Spot instances and preemptible VMs for cost-efficient training
- Model pruning and quantisation for efficient inference
- Batching strategies to reduce per-prediction cost
- Monitoring idle models and decommissioning unused endpoints
- Cost allocation tags and chargeback models
- Cloud billing alerts and budget thresholds
- Choosing between managed services and self-hosted solutions
Module 15: Real-World Implementation Projects - End-to-end project 1: Deploying a fraud detection model in finance
- Building a CI/CD pipeline for credit risk models
- Implementing drift detection and alerting system
- Creating a feature store for customer behaviour data
- Deploying a recommendation engine with A/B testing
- Setting up observability dashboards for API performance
- Implementing model governance in a healthcare use case
- Building a GDPR-compliant model deletion workflow
- Designing a disaster recovery plan for a mission-critical AI system
- Optimising a computer vision model for edge deployment
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts and decision frameworks
- Submitting a real-world implementation case study
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn, resumes, and professional profiles
- Leveraging certification in salary negotiations and promotions
- Joining a private network of certified ML Engineers
- Accessing advanced reading and supplemental resources
- Staying current with future updates and industry shifts
- Building a portfolio of production-grade implementation projects
- Introduction to TensorFlow Serving and TorchServe
- Using KServe for Kubernetes-native model serving
- Building custom model servers with FastAPI and Flask
- Request batching and dynamic batching for throughput optimisation
- Model warm-up and pre-loading strategies
- Multi-model serving: managing thousands of models efficiently
- Caching predictions for deterministic models
- Serving ensemble models and stacked architectures
- Supporting multiple frameworks in a single serving environment
- Security hardening for model serving endpoints
Module 7: Continuous Integration and Continuous Deployment (CI/CD) - Designing CI/CD pipelines for machine learning systems
- Automated testing for data quality, model performance, and code correctness
- Unit and integration testing for ML components
- Creating deployment gates based on model accuracy and drift thresholds
- Automated rollback triggers in CI/CD workflows
- Infrastructure as Code (IaC) for reproducible environments
- Terraform and Pulumi for cloud resource provisioning
- GitOps workflows for declarative deployment management
- Secrets management and secure pipeline execution
- Auditing and logging all changes in the deployment pipeline
Module 8: Data and Feature Engineering for Production - Feature stores: design, implementation, and integration
- Bulk and online feature serving patterns
- Feature versioning and consistency across training and serving
- Real-time feature computation with stream processing
- Data validation pipelines using Great Expectations and Soda Core
- Automated schema checking and data type enforcement
- Monitoring data freshness and completeness
- Handling missing data in production inference
- Feature lineage and impact analysis
- Building reusable, composable feature transformations
Module 9: Monitoring and Observability - Monitoring model performance: accuracy, precision, recall over time
- Tracking prediction latency, request rates, and error rates
- Concept drift detection using statistical tests and alerts
- Data drift detection with population stability index and KL divergence
- Feature drift and outlier detection in input data
- Monitoring for silent model failure
- Logging prediction requests and responses for audit trails
- Distributing tracing across microservices with Jaeger and OpenTelemetry
- Alerting strategies: threshold-based, anomaly detection, and machine learning
- Creating custom dashboards in Grafana and Prometheus
Module 10: Model Governance and Compliance - Establishing model risk management frameworks
- Model inventory and registry management
- Documentation requirements for regulatory compliance
- Model validation and audit processes
- Explainability reporting for regulatory submissions
- Data privacy considerations under GDPR, HIPAA, and CCPA
- Model fairness and bias audits across demographic groups
- Access control and role-based permissions for model access
- Rights to explanation and model contestability
- Creating and maintaining model risk logs
Module 11: Security and Ethical Considerations - Securing ML APIs with authentication, rate limiting, and encryption
- Protecting against model inversion and membership inference attacks
- Defending models from adversarial inputs and evasion attacks
- Model watermarking for IP protection
- Detecting and preventing data poisoning in training pipelines
- Securing pipeline dependencies and open-source packages
- Role of differential privacy in training and inference
- Ethical guidelines for AI deployment in sensitive domains
- Setting up model use policy enforcement
- Creating an AI ethics review board within organisations
Module 12: Testing in Production: Safe Experimentation - Designing safe canary launches with automatic rollback
- Shadow mode: validating new models using real traffic
- Chaos engineering for ML systems: simulating failures
- Canary analysis using statistical significance testing
- Automated goldenset evaluations in production
- Latency and load testing under peak traffic conditions
- Monitoring business KPIs during experimental launches
- Shadow databases for safe integration testing
- Safe rollback procedures and state recovery
- Post-mortem analysis of failed deployments
Module 13: High Availability and Disaster Recovery - Designing for 99.99% uptime in ML systems
- Automatic failover across availability zones and regions
- Backup and restore strategies for model artifacts and metadata
- Disaster recovery planning for ML platforms
- Load balancing across multiple model instances
- Rate limiting and circuit breakers for API protection
- Graceful degradation modes during partial failures
- Capacity planning for unexpected traffic spikes
- Monitoring health of dependent services and databases
- Automated recovery scripts and health checks
Module 14: Cost Management and Optimisation - Tracking compute costs per model and endpoint
- Right-sizing inference instances based on load patterns
- Auto-scaling strategies: horizontal and vertical
- Spot instances and preemptible VMs for cost-efficient training
- Model pruning and quantisation for efficient inference
- Batching strategies to reduce per-prediction cost
- Monitoring idle models and decommissioning unused endpoints
- Cost allocation tags and chargeback models
- Cloud billing alerts and budget thresholds
- Choosing between managed services and self-hosted solutions
Module 15: Real-World Implementation Projects - End-to-end project 1: Deploying a fraud detection model in finance
- Building a CI/CD pipeline for credit risk models
- Implementing drift detection and alerting system
- Creating a feature store for customer behaviour data
- Deploying a recommendation engine with A/B testing
- Setting up observability dashboards for API performance
- Implementing model governance in a healthcare use case
- Building a GDPR-compliant model deletion workflow
- Designing a disaster recovery plan for a mission-critical AI system
- Optimising a computer vision model for edge deployment
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts and decision frameworks
- Submitting a real-world implementation case study
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn, resumes, and professional profiles
- Leveraging certification in salary negotiations and promotions
- Joining a private network of certified ML Engineers
- Accessing advanced reading and supplemental resources
- Staying current with future updates and industry shifts
- Building a portfolio of production-grade implementation projects
- Feature stores: design, implementation, and integration
- Bulk and online feature serving patterns
- Feature versioning and consistency across training and serving
- Real-time feature computation with stream processing
- Data validation pipelines using Great Expectations and Soda Core
- Automated schema checking and data type enforcement
- Monitoring data freshness and completeness
- Handling missing data in production inference
- Feature lineage and impact analysis
- Building reusable, composable feature transformations
Module 9: Monitoring and Observability - Monitoring model performance: accuracy, precision, recall over time
- Tracking prediction latency, request rates, and error rates
- Concept drift detection using statistical tests and alerts
- Data drift detection with population stability index and KL divergence
- Feature drift and outlier detection in input data
- Monitoring for silent model failure
- Logging prediction requests and responses for audit trails
- Distributing tracing across microservices with Jaeger and OpenTelemetry
- Alerting strategies: threshold-based, anomaly detection, and machine learning
- Creating custom dashboards in Grafana and Prometheus
Module 10: Model Governance and Compliance - Establishing model risk management frameworks
- Model inventory and registry management
- Documentation requirements for regulatory compliance
- Model validation and audit processes
- Explainability reporting for regulatory submissions
- Data privacy considerations under GDPR, HIPAA, and CCPA
- Model fairness and bias audits across demographic groups
- Access control and role-based permissions for model access
- Rights to explanation and model contestability
- Creating and maintaining model risk logs
Module 11: Security and Ethical Considerations - Securing ML APIs with authentication, rate limiting, and encryption
- Protecting against model inversion and membership inference attacks
- Defending models from adversarial inputs and evasion attacks
- Model watermarking for IP protection
- Detecting and preventing data poisoning in training pipelines
- Securing pipeline dependencies and open-source packages
- Role of differential privacy in training and inference
- Ethical guidelines for AI deployment in sensitive domains
- Setting up model use policy enforcement
- Creating an AI ethics review board within organisations
Module 12: Testing in Production: Safe Experimentation - Designing safe canary launches with automatic rollback
- Shadow mode: validating new models using real traffic
- Chaos engineering for ML systems: simulating failures
- Canary analysis using statistical significance testing
- Automated goldenset evaluations in production
- Latency and load testing under peak traffic conditions
- Monitoring business KPIs during experimental launches
- Shadow databases for safe integration testing
- Safe rollback procedures and state recovery
- Post-mortem analysis of failed deployments
Module 13: High Availability and Disaster Recovery - Designing for 99.99% uptime in ML systems
- Automatic failover across availability zones and regions
- Backup and restore strategies for model artifacts and metadata
- Disaster recovery planning for ML platforms
- Load balancing across multiple model instances
- Rate limiting and circuit breakers for API protection
- Graceful degradation modes during partial failures
- Capacity planning for unexpected traffic spikes
- Monitoring health of dependent services and databases
- Automated recovery scripts and health checks
Module 14: Cost Management and Optimisation - Tracking compute costs per model and endpoint
- Right-sizing inference instances based on load patterns
- Auto-scaling strategies: horizontal and vertical
- Spot instances and preemptible VMs for cost-efficient training
- Model pruning and quantisation for efficient inference
- Batching strategies to reduce per-prediction cost
- Monitoring idle models and decommissioning unused endpoints
- Cost allocation tags and chargeback models
- Cloud billing alerts and budget thresholds
- Choosing between managed services and self-hosted solutions
Module 15: Real-World Implementation Projects - End-to-end project 1: Deploying a fraud detection model in finance
- Building a CI/CD pipeline for credit risk models
- Implementing drift detection and alerting system
- Creating a feature store for customer behaviour data
- Deploying a recommendation engine with A/B testing
- Setting up observability dashboards for API performance
- Implementing model governance in a healthcare use case
- Building a GDPR-compliant model deletion workflow
- Designing a disaster recovery plan for a mission-critical AI system
- Optimising a computer vision model for edge deployment
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts and decision frameworks
- Submitting a real-world implementation case study
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn, resumes, and professional profiles
- Leveraging certification in salary negotiations and promotions
- Joining a private network of certified ML Engineers
- Accessing advanced reading and supplemental resources
- Staying current with future updates and industry shifts
- Building a portfolio of production-grade implementation projects
- Establishing model risk management frameworks
- Model inventory and registry management
- Documentation requirements for regulatory compliance
- Model validation and audit processes
- Explainability reporting for regulatory submissions
- Data privacy considerations under GDPR, HIPAA, and CCPA
- Model fairness and bias audits across demographic groups
- Access control and role-based permissions for model access
- Rights to explanation and model contestability
- Creating and maintaining model risk logs
Module 11: Security and Ethical Considerations - Securing ML APIs with authentication, rate limiting, and encryption
- Protecting against model inversion and membership inference attacks
- Defending models from adversarial inputs and evasion attacks
- Model watermarking for IP protection
- Detecting and preventing data poisoning in training pipelines
- Securing pipeline dependencies and open-source packages
- Role of differential privacy in training and inference
- Ethical guidelines for AI deployment in sensitive domains
- Setting up model use policy enforcement
- Creating an AI ethics review board within organisations
Module 12: Testing in Production: Safe Experimentation - Designing safe canary launches with automatic rollback
- Shadow mode: validating new models using real traffic
- Chaos engineering for ML systems: simulating failures
- Canary analysis using statistical significance testing
- Automated goldenset evaluations in production
- Latency and load testing under peak traffic conditions
- Monitoring business KPIs during experimental launches
- Shadow databases for safe integration testing
- Safe rollback procedures and state recovery
- Post-mortem analysis of failed deployments
Module 13: High Availability and Disaster Recovery - Designing for 99.99% uptime in ML systems
- Automatic failover across availability zones and regions
- Backup and restore strategies for model artifacts and metadata
- Disaster recovery planning for ML platforms
- Load balancing across multiple model instances
- Rate limiting and circuit breakers for API protection
- Graceful degradation modes during partial failures
- Capacity planning for unexpected traffic spikes
- Monitoring health of dependent services and databases
- Automated recovery scripts and health checks
Module 14: Cost Management and Optimisation - Tracking compute costs per model and endpoint
- Right-sizing inference instances based on load patterns
- Auto-scaling strategies: horizontal and vertical
- Spot instances and preemptible VMs for cost-efficient training
- Model pruning and quantisation for efficient inference
- Batching strategies to reduce per-prediction cost
- Monitoring idle models and decommissioning unused endpoints
- Cost allocation tags and chargeback models
- Cloud billing alerts and budget thresholds
- Choosing between managed services and self-hosted solutions
Module 15: Real-World Implementation Projects - End-to-end project 1: Deploying a fraud detection model in finance
- Building a CI/CD pipeline for credit risk models
- Implementing drift detection and alerting system
- Creating a feature store for customer behaviour data
- Deploying a recommendation engine with A/B testing
- Setting up observability dashboards for API performance
- Implementing model governance in a healthcare use case
- Building a GDPR-compliant model deletion workflow
- Designing a disaster recovery plan for a mission-critical AI system
- Optimising a computer vision model for edge deployment
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts and decision frameworks
- Submitting a real-world implementation case study
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn, resumes, and professional profiles
- Leveraging certification in salary negotiations and promotions
- Joining a private network of certified ML Engineers
- Accessing advanced reading and supplemental resources
- Staying current with future updates and industry shifts
- Building a portfolio of production-grade implementation projects
- Designing safe canary launches with automatic rollback
- Shadow mode: validating new models using real traffic
- Chaos engineering for ML systems: simulating failures
- Canary analysis using statistical significance testing
- Automated goldenset evaluations in production
- Latency and load testing under peak traffic conditions
- Monitoring business KPIs during experimental launches
- Shadow databases for safe integration testing
- Safe rollback procedures and state recovery
- Post-mortem analysis of failed deployments
Module 13: High Availability and Disaster Recovery - Designing for 99.99% uptime in ML systems
- Automatic failover across availability zones and regions
- Backup and restore strategies for model artifacts and metadata
- Disaster recovery planning for ML platforms
- Load balancing across multiple model instances
- Rate limiting and circuit breakers for API protection
- Graceful degradation modes during partial failures
- Capacity planning for unexpected traffic spikes
- Monitoring health of dependent services and databases
- Automated recovery scripts and health checks
Module 14: Cost Management and Optimisation - Tracking compute costs per model and endpoint
- Right-sizing inference instances based on load patterns
- Auto-scaling strategies: horizontal and vertical
- Spot instances and preemptible VMs for cost-efficient training
- Model pruning and quantisation for efficient inference
- Batching strategies to reduce per-prediction cost
- Monitoring idle models and decommissioning unused endpoints
- Cost allocation tags and chargeback models
- Cloud billing alerts and budget thresholds
- Choosing between managed services and self-hosted solutions
Module 15: Real-World Implementation Projects - End-to-end project 1: Deploying a fraud detection model in finance
- Building a CI/CD pipeline for credit risk models
- Implementing drift detection and alerting system
- Creating a feature store for customer behaviour data
- Deploying a recommendation engine with A/B testing
- Setting up observability dashboards for API performance
- Implementing model governance in a healthcare use case
- Building a GDPR-compliant model deletion workflow
- Designing a disaster recovery plan for a mission-critical AI system
- Optimising a computer vision model for edge deployment
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts and decision frameworks
- Submitting a real-world implementation case study
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn, resumes, and professional profiles
- Leveraging certification in salary negotiations and promotions
- Joining a private network of certified ML Engineers
- Accessing advanced reading and supplemental resources
- Staying current with future updates and industry shifts
- Building a portfolio of production-grade implementation projects
- Tracking compute costs per model and endpoint
- Right-sizing inference instances based on load patterns
- Auto-scaling strategies: horizontal and vertical
- Spot instances and preemptible VMs for cost-efficient training
- Model pruning and quantisation for efficient inference
- Batching strategies to reduce per-prediction cost
- Monitoring idle models and decommissioning unused endpoints
- Cost allocation tags and chargeback models
- Cloud billing alerts and budget thresholds
- Choosing between managed services and self-hosted solutions
Module 15: Real-World Implementation Projects - End-to-end project 1: Deploying a fraud detection model in finance
- Building a CI/CD pipeline for credit risk models
- Implementing drift detection and alerting system
- Creating a feature store for customer behaviour data
- Deploying a recommendation engine with A/B testing
- Setting up observability dashboards for API performance
- Implementing model governance in a healthcare use case
- Building a GDPR-compliant model deletion workflow
- Designing a disaster recovery plan for a mission-critical AI system
- Optimising a computer vision model for edge deployment
Module 16: Certification, Career Advancement, and Next Steps - Preparing for the Certificate of Completion assessment
- Reviewing key concepts and decision frameworks
- Submitting a real-world implementation case study
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn, resumes, and professional profiles
- Leveraging certification in salary negotiations and promotions
- Joining a private network of certified ML Engineers
- Accessing advanced reading and supplemental resources
- Staying current with future updates and industry shifts
- Building a portfolio of production-grade implementation projects
- Preparing for the Certificate of Completion assessment
- Reviewing key concepts and decision frameworks
- Submitting a real-world implementation case study
- Earning your Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn, resumes, and professional profiles
- Leveraging certification in salary negotiations and promotions
- Joining a private network of certified ML Engineers
- Accessing advanced reading and supplemental resources
- Staying current with future updates and industry shifts
- Building a portfolio of production-grade implementation projects