MLOps: Deploying & Managing Machine Learning Models Training Course

Introduction

The journey of a Machine Learning (ML) model doesn't end with training; it truly begins when it's deployed and managed in production. This MLOps: Deploying & Managing Machine Learning Models Training Course is crucial for ML engineers, data scientists, DevOps engineers, data engineers, and technical leads aiming to bridge the gap between experimental ML development and reliable, scalable operational systems. MLOps (Machine Learning Operations) is a discipline that integrates development (Dev), ML (Machine Learning), and operations (Ops) to ensure the continuous and efficient delivery of ML models.

Participants will gain practical expertise in establishing robust MLOps pipelines, automating the entire ML lifecycle from data ingestion to model deployment and monitoring. The curriculum covers essential practices such as data and model versioning, continuous integration/delivery (CI/CD) for ML, efficient model serving, and comprehensive model monitoring to detect drift and performance degradation. By mastering these principles and tools, you will be able to build, deploy, and manage production-grade ML systems that deliver continuous value and ensure the reliability and reproducibility of your AI solutions.

Target Audience

• Machine Learning Engineers
• Data Scientists with an interest in productionizing models
• DevOps Engineers looking to specialize in ML systems
• Data Engineers involved in ML data pipelines
• MLOps Practitioners
• Technical Leads and Architects overseeing AI/ML initiatives

Duration

10 days

Course Objectives

1. Understand the core principles of MLOps and its importance in the machine learning lifecycle.
2. Design and implement robust data versioning and validation strategies for ML datasets.
3. Establish continuous integration (CI) and continuous delivery (CD) pipelines tailored for machine learning models.
4. Master techniques for efficient model serving, inference, and scalable deployment strategies.
5. Implement comprehensive model monitoring systems to detect performance degradation, data drift, and concept drift.
6. Ensure reproducibility and auditability of ML experiments and deployed models.
7. Develop strategies for model retraining automation and continuous model improvement.
8. Navigate common MLOps tools and platforms to build and manage production-ready ML systems.

Course Content

Module 1. Introduction to MLOps

• What is MLOps: Definition, goals, and core principles
• The unique challenges of putting ML models into production compared to traditional software
• MLOps vs. DevOps: Similarities and key differences
• The end-to-end ML lifecycle and where MLOps fits in
• Benefits of adopting MLOps: Faster deployment, reliability, scalability, reproducibility

Module 2. ML Project Lifecycle & MLOps Stages

• Overview of the ML project lifecycle: Business understanding, data preparation, modeling, evaluation, deployment
• The iterative and continuous nature of MLOps
• Key stages of the MLOps pipeline: Experimentation, development, testing, deployment, monitoring, retraining
• Collaboration model: Data Scientists, ML Engineers, DevOps Engineers, Business Stakeholders
• Setting up an MLOps development environment

Module 3. Data Versioning & Validation

• Importance of data versioning for ML reproducibility and auditability
• Tools for data versioning (e.g., DVC - Data Version Control)
• Data validation in pipelines: Ensuring data quality and schema consistency
• Using libraries like Great Expectations or Deequ for data quality checks
• Data lineage: Tracking data transformations from source to model input

Module 4. Model Versioning & Registry

• Managing ML model artifacts: Trained models, preprocessing logic, metadata
• Model versioning strategies: Semantic versioning for ML models
• Centralized Model Registry: Storing, discovering, and managing models
• Role of MLflow Tracking and Model Registry for experiment and model management
• Staging models for different environments (development, staging, production)

Module 5. Continuous Integration (CI) for ML

• Adapting CI principles for machine learning projects
• Code quality and static analysis for ML code
• Unit testing ML components: Data preprocessing, model training functions
• Integration testing: Ensuring ML pipeline components work together
• Using CI tools (e.g., Jenkins, GitLab CI, GitHub Actions, Azure DevOps) to automate builds and tests

Module 6. Continuous Delivery & Deployment (CD) for ML

• Automating the deployment of ML models to production environments
• Deployment strategies: Blue/Green deployment, Canary releases, A/B testing for models
• Containerization for ML models (e.g., Docker)
• Orchestration with Kubernetes for scalable model serving
• Infrastructure as Code (IaC) for ML infrastructure (e.g., Terraform, CloudFormation)

Module 7. Model Serving & Inference

• Designing APIs for ML model inference (RESTful APIs, gRPC)
• Batch inference vs. real-time (online) inference
• Model serving frameworks and tools (e.g., TensorFlow Serving, TorchServe, Triton Inference Server)
• Optimizing models for inference: Quantization, pruning, ONNX
• Cold start problems and performance considerations for serving

Module 8. Model Monitoring & Alerting

• Why monitor ML models in production: Detecting performance degradation, data drift, concept drift
• Key metrics for model performance monitoring (accuracy, precision, recall, F1-score, RMSE)
• Data drift detection: Monitoring changes in input data distribution
• Concept drift detection: Monitoring changes in the relationship between input and target variables
• Tools for ML monitoring and observability (e.g., Evidently AI, Arize, Fiddler, Prometheus, Grafana)
• Setting up alerts for anomalies and performance issues

Module 9. Model Retraining & Automation

• Triggers for model retraining: Scheduled, performance decay, data drift, new data availability
• Designing automated retraining pipelines
• Strategies for active learning and human-in-the-loop ML systems
• Managing model updates and rollbacks
• Continuous feedback loops for model improvement

Module 10. MLOps Platforms & Tools

• Overview of end-to-end MLOps platforms:
  • Cloud-based: AWS SageMaker, Azure Machine Learning, Google Cloud Vertex AI
  • Open-source: Kubeflow, MLflow, ZenML, Seldon Core
• Comparing features and use cases of different MLOps tools
• Building an MLOps stack for your specific needs
• Integration with existing data and DevOps ecosystems

Module 11. MLOps Governance & Reproducibility

• Ensuring reproducibility of ML experiments and production models
• Audit trails for data, code, model versions, and training runs
• Compliance with regulations (e.g., GDPR, HIPAA) for ML systems
• Security best practices for ML models and data
• Explainable AI (XAI) in MLOps: Monitoring model interpretability and fairness

Module 12. Advanced MLOps Patterns & Future Trends

• A/B testing and multi-armed bandit strategies for ML models in production
• Advanced deployment patterns: Ensemble models, multi-model deployment
• MLOps for Generative AI and Large Language Models (LLMOps)
• Edge MLOps: Deploying and managing ML models on edge devices
• Responsible AI in MLOps: Bias mitigation, fairness, transparency frameworks
• The evolving landscape of MLOps: New tools, paradigms, and challenges

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank