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Beginner Level3 Gün

MLOps Fundamentals Training | Productionization, CI/CD, Monitoring & Model Lifecycle Management

MLOps fundamentals to productionize ML models: versioning, CI/CD, Docker/Kubernetes deployment, monitoring/logging, and lifecycle management.

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About This Course

MLOps (Machine Learning Operations) is a discipline that strengthens collaboration between data scientists and operations by automating the development, delivery, monitoring and maintenance of machine learning models. This training aims to introduce the basic concepts, tools and processes of MLOps, enabling technological developments to make their projects more efficient, scalable and sustainable.

Training Methodology

End-to-end lifecycle focus: development → deployment → monitoring → improvement in one frame

Principles + tools: MLflow/DVC, Docker, Kubernetes, Prometheus/Grafana taught with “why/where to use” logic

Pipeline mindset: reproducibility, traceability, rollback, and validation gates

Production realism: latency, resource usage, incident handling, anomaly detection

Governance awareness: security, ethics, compliance, adversarial risks in production

Hands-on demo & cases: a simple reference pipeline to ground learning

Who Is This For?

Data Scientists and ML Engineers who want to productionize models
DevOps / Platform / SRE teams operating ML workloads
Software Engineers and IT professionals involved in deployment and serving
Project Managers and Business Analysts coordinating ML deliveries and risk
Tech teams aiming to move from PoC to production with reliable processes

Why This Course?

1

Learn reliable productionization and scalability patterns for ML models

2

Increase efficiency via automation across training, testing, deployment, and monitoring

3

Standardize collaboration between data science, engineering, and operations

4

Build continuous monitoring and update mechanisms for production performance

5

Establish sustainable lifecycle management with CI/CD foundations

Learning Outcomes

Clearly model the MLOps ecosystem (data, training, deployment, monitoring, automation)
Design a dataset/experiment/model versioning strategy (MLflow/DVC approach)
Define the role of CI/CD and quality gates (testing/validation) in ML workflows
Plan Docker packaging and Kubernetes/cloud deployment strategies
Establish monitoring/dashboard logic using accuracy/latency/resource metrics (Prometheus/Grafana)
Improve operational resilience with logging, alerting, anomaly detection, and optimization
Build continuous improvement loops via feedback and retraining/update strategies
Integrate security, ethics, and compliance requirements into production processes

Requirements

Basic-intermediate Python literacy
Familiarity with ML fundamentals (train/test, metrics, overfitting)
Basic Git knowledge (repo/commit/branch concepts)
Docker/Kubernetes not required; concepts will be introduced from scratch (helps if known)
For corporate cohorts: sharing a sample use case/problem statement is recommended

Course Curriculum

1.1 Scope and Objectives of MLOps

1.1.1 What MLOps is (and is not)

1.1.2 “Model development” vs “ML product development”

1.1.3 Success criteria for production ML: reliability, sustainability, measurable impact

1.1.4 Why ML projects require “operations”: data and behavior variability

1.2 Relationship with DevOps and Key Differences

1.2.1 Code-centric lifecycle vs data+model-centric lifecycle

1.2.2 Non-deterministic training/inference and operational implications

1.2.3 “Offline good → Online bad”: distribution shift, label delay, feedback loops

1.2.4 Operational risk classes in ML: performance, security, compliance, cost

1.3 End-to-End ML Lifecycle

1.3.1 Data collection → preparation → training → evaluation

1.3.2 Packaging → serving → monitoring → retraining

1.3.3 Lifecycle artifacts: dataset, feature set, model artifact, pipeline run, deployment

1.3.4 Lifecycle roles: development, release, operations, governance

1.4 MLOps Component Map (Concept Map)

1.4.1 Data layer: sources, storage, access, schemas

1.4.2 Training layer: pipelines, experiment management, resource management

1.4.3 Model management: registry, stages, approval workflow

1.4.4 Deployment layer: serving, scaling, version transitions

1.4.5 Observability: metrics, logs, traces, alerts, dashboards

1.4.6 Governance: security, ethics, compliance, auditability

1.5 Common Anti-Patterns and Root-Cause Thinking

1.5.1 Notebook-only development → fragile production systems

1.5.2 Data leakage → wrong model selection

1.5.3 Train/serve skew → live performance degradation

1.5.4 No versioning → irreversible failures

1.5.5 No monitoring → “dark model” problem

Instructor

Şükrü Yusuf Kaya

Şükrü Yusuf Kaya

AI Consultant & Instructor

Şükrü Yusuf KAYA is an internationally experienced AI Consultant and Technology Strategist leading the integration of artificial intelligence technologies into the global business landscape. With operations spanning 6 different countries, he bridges the gap between the theoretical boundaries of technology and practical business needs, overseeing end-to-end AI projects in data-critical sectors such as banking, e-commerce, retail, and logistics. Deepening his technical expertise particularly in Generative AI and Large Language Models (LLMs), KAYA ensures that organizations build architectures that shape the future rather than relying on short-term solutions. His visionary approach to transforming complex algorithms and advanced systems into tangible business value aligned with corporate growth targets has positioned him as a sought-after solution partner in the industry. Distinguished by his role as an instructor alongside his consulting and project management career, Şükrü Yusuf KAYA is driven by the motto of "Making AI accessible and applicable for everyone." Through comprehensive training programs designed for a wide spectrum of professionals—from technical teams to C-level executives—he prioritizes increasing organizational AI literacy and establishing a sustainable culture of technological transformation.

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