Machine Learning Operations Training | MLOps Online Training
                     MLOps: Unifying 
Machine Learning and 
Operations
Seamlessly bridging the gap between ML development and 
deployment.
+91-7032290546
Introduction
Machine Learning is transforming industries globally. Deploying ML models at scale 
presents challenges. MLOps solves these real-world deployment problems. It aligns ML 
with DevOps practices. It automates the entire ML lifecycle. This enables seamless 
collaboration across teams.
ML Transformation Deployment Bridging Gaps
Challenges
Machine learning is MLOps connects 
revolutionizing industries. It Deploying ML models at scale development and 
brings new levels of is complex. It requires robust operations. It ensures 
automation and insight. infrastructure and processes. smooth and efficient model 
deployment.
Team Collaboration
MLOps fosters teamwork. 
Data scientists, engineers, 
and operations work 
together.
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What is MLOps?
MLOps merges Machine Learning and DevOps. It automates development, deployment, and monitoring. 
This helps deliver reliable, scalable ML solutions. It combines ML engineering with operations. It supports 
continuous integration/delivery (CI/CD). MLOps ensures repeatable and traceable workflows.
Machine Learning DevOps
• Model Development • Automation
• Experiment Tracking • CI/CD Pipelines
• Performance Evaluation • Monitoring
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Why is MLOps Important?
MLOps significantly reduces deployment time. It enhances team collaboration and efficiency. It ensures comprehensive model 
versioning and governance. It enables robust monitoring of live ML models. MLOps helps maintain model accuracy and 
performance. This is crucial for enterprise-grade ML systems.
1 Faster Deployment 2 Improved Collaboration
Accelerates model from creation to production. Streamlines communication and efficiency.
3 Model Governance 4 Performance Monitoring
Ensures versioning and compliance. Keeps live models accurate and reliable.
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Key Components of MLOps
MLOps comprises several critical components. Data management includes versioning, 
pipelines, and validation. Model development involves experiment tracking and robust 
training. Model deployment focuses on CI/CD automation. Monitoring covers model drift and 
performance tracking. Governance ensures reproducibility and compliance. Collaboration is 
key between data scientists, ML engineers, and DevOps teams.
Data Management
Versioning, pipelines, and validation are essential.
Model Development
Includes experiment tracking and efficient training.
Model Deployment
Leverages CI/CD for automated release processes.
Monitoring & Governance
Tracks performance, drift, and ensures compliance.
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MLOps Workflow
The MLOps workflow begins with data ingestion and preprocessing. Next is model training, evaluation, and tuning. This leads to model tracking and versioning. Deployment 
happens using automated pipelines (CI/CD). Models are continuously monitored in production environments. Finally, there's continuous feedback and retraining for 
improvement.
Data Prep
Ingestion and preprocessing of raw data.
Model Training
Training, evaluation, and fine-tuning models.
Versioning
Tracking and managing model versions.
Deployment
Automated release via CI/CD pipelines.
Monitoring
Continuous tracking in production.
Feedback Loop
Continuous improvement and retraining.
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Tools Used in MLOps
Various tools support the MLOps lifecycle. Git and DVC manage 
versioning. MLflow and Weights & Biases track experiments. 
Docker, Kubernetes, and Seldon facilitate deployment. Kubeflow 
and Airflow automate workflows. Prometheus and Grafana are used 
for monitoring. Jenkins and GitHub Actions power CI/CD processes.
Versioning Git, DVC
Experiment Tracking MLflow, Weights & Biases
Deployment Docker, Kubernetes, Seldon
Workflow Automation Kubeflow, Airflow
Monitoring Prometheus, Grafana
CI/CD Jenkins, GitHub Actions
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MLOps Best Practices
Automating the ML lifecycle with pipelines is crucial. Use version control for both data and models. Implement CI/CD for efficient training and deployment. 
Monitor models for drift and performance degradation. Keep all experiments reproducible and traceable. Foster strong collaboration across data science, ML 
engineering, and operations teams.
Automate Everything
1 Pipelines for ML lifecycle.
Version Control
2
Track data and models.
CI/CD Implementation
3
For training and deployment.
Continuous Monitoring
4
Detect drift and performance issues.
Reproducibility
5
Ensure traceable experiments.
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Conclusion
MLOps effectively bridges data science and IT operations. It ensures scalability, automation, and reliability for ML systems. This 
approach significantly reduces risks in ML deployments. It enables continuous improvement of models in production. MLOps supports 
real-world, production-ready machine learning. It is a must-have skill in the modern AI landscape.
Scalability
1
Handle increasing data and model complexity.
Automation
2
Streamline repetitive tasks for efficiency.
Reliability
3
Ensure consistent and dependable model performance.
Continuous Improvement
4
Iteratively enhance models based on feedback.
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Contact Us
Reach out to the MLOps Team for more information.
• Address: Flat no: 205, 2nd Floor, Nilgiri Block, Aditya Enclave, Ameerpet, Hyderabad-1
• Phone: +91-7032290546
• Website: WWW.VISUALPATH.IN
• Email: [email protected]
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Thank You for Your 
Time
We appreciate your engagement and look forward to 
collaborating on your MLOps journey.
+91-7032290546