Learn Ai
MLOps
With Techpratham, you can learn how to use MLOps to make the machine learning lifecycle easier, from building models to deploying them in production. Learn how to automate, keep an eye on, and grow AI workflows.
5/5(4,890 Reviews)
Level
Advanced
Duration
8 weeks
What is MLOps?
Techpratham's MLOps training gives students the tools they need to manage, automate, and improve the ML lifecycle. You'll learn a lot about CI/CD pipelines, model versioning, monitoring, and deployment strategies. The course also talks about containerization, cloud integration, and managing models in real time. By the end, you'll know how to make machine learning systems for businesses that can grow and are reliable.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
MLOps Course Curriculum
.
Introduction to MLOps
Section 1
Understand the fundamentals and importance of MLOps.
What is MLOps?
ML Lifecycle Management
DevOps vs MLOps
Benefits for Businesses
Real-World Use Cases
.
Machine Learning Lifecycle Management
Section 2
Learn about different stages of an ML project including data collection, preprocessing, model building, deployment, and monitoring.
Data Preparation
Model Training
Model Evaluation
Production Deployment
.
Version Control & Experiment Tracking
Section 3
Explore versioning strategies for datasets, models, and experiments to ensure reproducibility and transparency in ML projects.
Git & GitHub
DVC (Data Version Control)
MLflow Tracking
Experiment Logging
.
Continuous Integration & Continuous Deployment (CI/CD)
Section 4
Implement CI/CD pipelines for machine learning models to automate testing, integration, and deployment processes.
Jenkins / GitHub Actions
Automated Testing
Deployment Pipelines
Model Registry
.
Containerization & Orchestration
Section 5
Understand the role of Docker and Kubernetes in packaging ML models and managing scalable deployments in cloud environments.
Docker Basics
Kubernetes Pods & Services
Helm Charts
Cloud Deployment
.
Model Serving & APIs
Section 6
Learn how to serve ML models as APIs for integration with applications and enable real-time or batch inference.
Flask & FastAPI
REST & gRPC APIs
Batch Inference
Real-time Predictions
.
Monitoring & Logging in Production
Section 7
Set up monitoring systems to track model performance, drift, latency, and errors in production environments.
Model Drift Detection
Performance Metrics
Logging Tools
Alerting Systems
.
Data & Model Governance
Section 8
Explore best practices in model governance, fairness, explainability, and compliance for responsible AI deployment.
Data Privacy
Model Explainability
Bias Mitigation
Compliance Standards
.
Cloud Platforms for MLOps
Section 9
Work with leading cloud providers and their MLOps services to deploy, scale, and manage ML workloads.
AWS Sagemaker
Azure ML
GCP Vertex AI
Hybrid & Multi-Cloud
.
Automation & Advanced MLOps Tools
Section 10
Leverage advanced tools and automation frameworks to enhance efficiency, scalability, and reliability of ML pipelines.
Kubeflow
Airflow
TFX (TensorFlow Extended)
Automated ML
Key Projects
MLOps
End-to-End ML Pipeline with CI/CD
Use CI/CD tools like GitHub Actions or Jenkins to build a full ML pipeline that automates the process of getting data, training models, testing them, and deploying them. This project makes sure that students know how to use automation workflows.
Customer Churn Prediction System
Use CI/CD tools like GitHub Actions or Jenkins to build a full ML pipeline that automates the process of getting data, training models, testing them, and deploying them. This project makes sure that students know how to use automation workflows.
Real-Time Sentiment Analysis with Monitoring
Make a sentiment analysis model that can be used through APIs and add monitoring tools to keep an eye on how well the model is working, find drifts, and retrain it automatically. This project is all about making things work reliably in production.
MLOps – Associate Training Program
Category
Associate
Exam Name:
MLOps – Associate
Exam Code:
N.A.
Exam Duration:
N.A.
Exam Format:
N.A.
Passing Score:
N.A.
Our Testimonials
Frequently Asked Questions (FAQs)
Why is MLOps important in today’s AI industry?
MLOps makes sure that machine learning models in production can grow, work automatically, and be reliable. It helps businesses use AI more quickly, keep it accurate, and lower the risks that come with running it.
What is MLOps?
MLOps (Machine Learning Operations) is the practice of managing, automating, and scaling machine learning models in production. It combines ML, DevOps, and data engineering. TechPratham’s training helps learners understand the full lifecycle of ML models from development to deployment.
Why is MLOps important?
MLOps ensures machine learning models are reliable, scalable, and continuously monitored after deployment. It reduces failure rates and improves efficiency. TechPratham trains learners in industry best practices to make ML models production-ready.
What are the main components of MLOps?
MLOps includes model development, deployment, monitoring, versioning, and automation. TechPratham provides hands-on labs and projects to help learners master each component step by step.
Do I need coding skills for MLOps?
Yes, knowledge of Python, APIs, and cloud platforms is helpful for MLOps. However, beginners can still start learning gradually. TechPratham ensures a structured path so both beginners and experienced professionals can excel.
How does MLOps differ from DevOps?
While DevOps focuses on software deployment, MLOps specifically manages ML model pipelines, data, and retraining. It bridges data science and operations. TechPratham explains these differences clearly with real-world projects.
What industries use MLOps?
Industries like finance, healthcare, e-commerce, IT, and manufacturing use MLOps for predictive analytics, fraud detection, and automation. TechPratham aligns its projects with these industries to make training job-oriented.
What skills can I gain from MLOps training?
Learners acquire model deployment, CI/CD for ML, cloud integration, monitoring, and automation skills. TechPratham provides project-based learning to ensure these skills are directly applicable in the workplace.
Is MLOps only for large organizations?
No, even startups and mid-sized companies use MLOps to scale their AI workflows. TechPratham shows learners how both small and large businesses implement MLOps for efficiency.
How does MLOps improve AI projects?
MLOps streamlines deployment, reduces downtime, and enables continuous retraining of models with new data. It increases AI project success rates. TechPratham teaches practical frameworks for improving real-world AI projects.
ERP Partners
Other Learn Ai Courses
📚
No Related Courses Found
We couldn't find any other courses in the "Learn Ai" category at the moment.
Registered Office:
Noida Office:
Hyderabad Office:
Trending Certification Courses
Company
© 2025 TechPratham. All rights reserved.An ISO 9001:2015 Certified Company