MLOps Course in Hyderabad - MLOps Training Online
                     STEP-BY-STEP GUIDE 
TO MLOPS WORKFLOW 
AUTOMATION
INTRODUCTION TO MLOPS 
WORKFLOW AUTOMATION
• MLOps = Machine Learning + DevOps for AI lifecycle 
management.
• Focuses on automating model training, testing, and 
deployment.
• Improves reproducibility, scalability, and collaboration.
• Reduces time from data to production-ready models.
STEP 1 – DATA COLLECTION 
AND PREPARATION
• Collect and integrate data from multiple 
sources.
• Clean, preprocess, and normalize datasets.
• Automate feature engineering and validation 
pipelines.
• Tools: Apache Airflow, AWS Glue, Azure Data 
Factory.
STEP 2 – MODEL 
DEVELOPMENT AND TRAINING
• Design ML models using frameworks like 
TensorFlow or PyTorch.
• Automate hyperparameter tuning and optimization.
• Track experiments and results with MLflow or 
Weights & Biases.
• Save best models for deployment.
STEP 3 – CONTINUOUS 
INTEGRATION (CI) FOR ML
• Integrate code and model changes into 
version control (Git).
• Automate testing for model accuracy and 
code quality.
• Use CI pipelines for validation and artifact 
management.
• Tools: Jenkins, GitHub Actions, Azure DevOps.
STEP 4 – CONTINUOUS 
DEPLOYMENT (CD) FOR ML
• Deploy models automatically into production 
environments.
• Use containerization (Docker, Kubernetes) for 
scalability.
• Implement rollback and version control 
mechanisms.
• Tools: Kubeflow, TFX, AWS SageMaker, Azure 
ML.
STEP 5 – MONITORING AND 
MAINTENANCE
• Monitor model performance and detect data 
drift.
• Automate retraining when performance 
degrades.
• Use monitoring tools for alerts and reporting.
• Tools: Prometheus, Grafana, MLflow, 
Evidently AI.
CONCLUSION
• Faster and more reliable ML model 
deployment.
• Reduced human intervention and errors.
• Improved model accuracy and performance 
tracking.
• MLOps automation accelerates innovation in 
AI systems.
CONTACT US
Flat no: 205, 2nd Floor, NILGIRI 
Block, Aditya Enclave, Ameerpet, 
Hyderabad-16
Mobile No: +91 7032290546
[email protected]
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