Overview
MLOps (Machine Learning Operations) is a discipline that strengthens collaboration between data scientists and operations teams by automating the development, deployment, monitoring, and maintenance of machine learning models. This training introduces the core concepts, tools, and processes of MLOps, enabling participants to make their machine learning projects more efficient, scalable, and sustainable.
What Is It?
MLOps brings together model development, training, deployment, monitoring, and continuous integration/continuous delivery (CI/CD) processes to productionize machine learning models and manage their full lifecycle. This training covers the core building blocks of MLOps from data preparation to model monitoring.
Who Is It For?
This training is suitable for:
• Data scientists and machine learning engineers
• DevOps and system administrators
• Software developers and IT professionals
• Project managers and business analysts
• All technology enthusiasts looking to bring machine learning projects into production
Why MLOps Fundamentals Training?
• Model Productionization: Seamlessly deploying and scaling developed models.
• Automation: Increasing efficiency by automating model training, testing, deployment, and monitoring workflows.
• Collaboration: Improving workflows between data scientists, engineers, and operations teams.
• Performance and Monitoring: Continuously monitoring production model performance and updating models when needed.
• Sustainability: Managing the model lifecycle with continuous integration and delivery (CI/CD) tools.
Curriculum
Advanced MLOps Strategies and Concepts
Advanced Model Lifecycle Management
Detailing the model development · training · deployment · continuous improvement cycle
Model drift · efficiency measurement · quality control strategies
A/B Testing and Canary Deployment
Simultaneous testing of different model versions
Managing gradual rollout processes with canary deployment strategies
Automated Model Retraining
Automated retraining mechanisms upon detecting performance degradation
Feedback loops and model update policies
Designing Scalable MLOps Infrastructure
Big Data Processing and Real-Time Data Streams
Real-time data processing techniques · data stream management
Apache Kafka · Spark Streaming integration
Containerization and Orchestration
Building scalable environments using Docker · Kubernetes
Serverless architectures · microservice integration
Distributed Systems and Load Balancing
Load balancing strategies · distributed storage solutions
Ensuring high availability · fault tolerance
Advanced Automation and CI/CD Strategies
Advanced CI/CD Pipelines
Automated model integration and deployment in CI/CD workflows
Using Jenkins · GitLab CI · MLflow · Kubeflow
Automated Testing · Validation · Rollback
Model validation · performance testing · automated rollback mechanisms
Improving continuous integration workflows through test automation
Pipeline Monitoring and Error Management
Pipeline monitoring · log analysis · error detection
Alerting systems · automated remediation strategies
Model Monitoring · Logging · Performance Optimization
Real-Time Monitoring and Anomaly Detection
Model performance · latency · resource utilization metrics
Anomaly detection · model drift · performance degradation analysis
Logging · Alerting Systems · Dashboards
Prometheus · Grafana integration
Log analysis · alerting mechanisms · visual dashboard design
Performance Optimization and Resource Management
Optimization strategies for improving model performance
Scalability · efficient management of resource utilization
Security · Compliance · Ethical Approaches
Advanced Data and Model Security
Data encryption · access control · authentication mechanisms
Safeguards against adversarial attacks and model manipulation
Compliance Standards and Regulations
Data privacy and compliance requirements such as GDPR · HIPAA
Ethical AI use · fair modeling · bias management strategies
Risk Management and Contingency Plans
Incident response plans for security breaches · data loss · system failures
Continuous improvement · risk mitigation strategies
Case Studies · Hands-On Workshops · Discussion
Real-World Examples and Success Stories
Case studies from large-scale enterprise MLOps projects
Success factors · challenges · solution strategies
Interactive Hands-On Workshops
Advanced MLOps pipeline setup and simulations
Real-time problem-solving sessions through group exercises
Group Discussions and Experience Sharing
Examples from participant projects · sharing proposed solutions
Q&A sessions · advanced discussions
Other Courses in This Category
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Systematic experiment design, hyperparameter optimisation with Optuna and Ray Tune, metric/artefact standards, reproducibility and comparative setup of tracking tools (MLflow, W&B, Neptune), for data science teams.
- Live Course
- 5 modules
-
MLOps – Advanced Level
Enterprise-grade MLOps covering model drift monitoring, A/B testing, canary deployments, automated retraining, CI/CD pipelines, Kafka/Spark Streaming, and Kubernetes orchestration.
- Live Course
- 6 modules
-
MLflow Tracking Server architecture, PostgreSQL/S3 setup, autologging, custom model packaging with pyfunc, MLflow Evaluate (classical ML and LLMs) and Model Registry integration, for teams standardising on open-source MLflow.
- Live Course
- 5 modules
-
W&B Experiments, Sweeps, Artifacts (data and model lineage), visual error analysis with Tables, LLM tracing with Weave and collaborative Reports, for deep learning and generative AI teams.
- Live Course
- 5 modules
-
Synchronised versioning of large datasets, labels and model weights alongside Git using DVC, LakeFS and Iceberg/Delta time-travel, with end-to-end lineage for teams building auditable ML systems.
- Live Course
- 5 modules
-
Central Model Registry architecture, packaging standards, @champion/@challenger promotions, automated validation gates, model cards and security scanning, for teams governing how models move into production.
- Live Course
- 5 modules
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