Mastering MLOps: The Ultimate Online Training Program with Hands-On Projects
In the field of AI, businesses are deploying machine learning models at scale to drive automation, enhance decision-making, and improve customer experiences. However, deploying and managing these models in production is a complex challenge that requires expertise in Machine Learning Operations (MLOps). MLOps bridges the gap between machine learning, software engineering, and DevOps, ensuring seamless deployment, monitoring, and maintenance of ML models in real-world applications.
As organizations prioritize scalable AI solutions, the demand for skilled MLOps professionals has surged. From model versioning and pipeline automation to real-time monitoring and retraining, MLOps plays a crucial role in ensuring that ML models remain accurate, reliable, and production-ready.
Program Overview
Mastering MLOps with Real-World Projects
Key Features of our MLOps Program
- Hands-on MLOps Training with industry-driven real-world projects
- Practical learning experience with end-to-end ML pipelines
- Mastering MLOps with industry best practices for scalable and efficient AI solutions
- 3.5 Months duration with interview preparation and project explanation
- Cloud-based MLOps training covering automated deployment, monitoring, and model scaling
Program Curriculum
Module-Wise Breakdown: Step-by-Step Learning Journey
Module 1: Foundations of MLOps – Building a Complete ML Pipeline
Project: Build a Machine Learning Pipeline with MLOps Basics
- Train a Regression Model to Predict House Prices:
Work with real-world housing price datasets
Use Python and Scikit-learn for model training
Apply feature engineering and data preprocessing techniques
- Use MLflow for Experiment Tracking and Model Versioning:
Track model parameters, hyperparameters, and evaluation metrics
Compare different model runs to identify the best performing version
Implement automated logging for reproducible ML experiments
- Deploy the Model Locally Using MLflow’s REST API:
Expose the trained model as a REST API
Test model predictions through API endpoints
Prepare the model for containerized deployment in later modules
Key Takeaways from Module 1:
- Understanding MLOps fundamentals and building a structured ML pipeline
- Implementing model versioning and experiment tracking with MLflow
- Deploying a trained model locally for real-world testing
Module 2: Advanced MLOps – Deploying a Production-Grade ML Pipeline
This module moves beyond basic deployment and focuses on automating ML pipelines using Kubeflow and Kubernetes, essential tools for scalable and production-ready ML workflows.
Project: Build and Deploy a Production-Ready ML Pipeline Using Kubeflow
- Create a Kubeflow Pipeline for Fraud Detection or Image Classification:
Automate data ingestion, model training, and evaluation steps
Implement parameter tuning and hyperparameter optimization
Utilize Kubeflow Pipelines for workflow orchestration
- Automate Model Training and Deployment with Kubernetes:
Run training jobs in a containerized and scalable environment
Manage resources efficiently with Kubernetes clusters
Implement automated model retraining based on data updates
- Deploy the Model with KFServing and Scale Using Kubernetes:
Use KFServing for model serving in a serverless architecture
Scale deployments automatically based on traffic loads
Ensure zero downtime while updating model versions
Key Takeaways from Module 2:
- Mastering Kubeflow for automated ML workflows
- Deploying ML models in a scalable Kubernetes environment
- Implementing real-world ML automation and continuous training
Module 3: Model Monitoring, Performance Optimization, and Alerting
Project: Monitoring a Deployed ML Model Using Prometheus and Grafana
- Monitor a Sentiment Analysis Model in Production: Deploy a pre-trained NLP model for sentiment analysis Set up real-time monitoring for prediction accuracy and response time
- Detect Model Drift and Performance Degradation: Implement automated drift detection algorithms Use Prometheus to collect real-time performance metrics Set up Grafana dashboards for data visualization
- Automate Alerts for Anomalies and Performance Drops: Configure automated alerts for sudden drops in accuracy Integrate Slack or email notifications for anomaly detection Implement self-healing mechanisms for model retraining
Key Takeaways from Module 3:
- Implementing real-time model monitoring with Prometheus and Grafana
- Detecting model drift and performance degradation proactively
- Setting up automated alerting and anomaly detection workflows
Program Fee
₹ 39,999 ₹45,000/-
This Course Includes
- Eligibility: More than 2 years of experience
- 3.5 Months of Live Training
- 6 LIVE Projects
- Interview Preparation
- Resume Building
- Project Explanation
- LIVE Sessions will get Recorded & Shared
- Support via Email & Slack
Program Highlights
- Comprehensive Curriculum: Covering everything from advanced DevOps to MLOps workflows.
- Hands-On Learning: Real-world projects and practical sessions for every module.
- Expert Instructors: 10+ years of experience in IT with Cloud, DevOps, and MLOps.
- Flexible Learning: Live sessions + self-paced modules for a balanced experience.
Skills You'll Acquire:
MLflow
DVC
Docker
Kubernetes
Kubeflow Pipelines
Prometheus
Grafana
Alertmanager
Tools You'll Learn:
Why Choose Mindbox
Career Services for MLOps Training Program
Interview preparation
Get thoroughly prepared for MLOps-specific interviews with mock sessions focusing on machine learning workflows, automation pipelines, and deployment best practices.
Portfolio building
Develop a strong portfolio with LIVE projects, including setting up CI/CD pipelines, implementing monitoring with tools like Prometheus and Grafana, and automating ML model retraining using Kubeflow.
Mock test
Take part in mock tests that simulate real-world MLOps challenges, like debugging production pipelines and optimizing ML model performance.
Project explanation
Gain in-depth explanations of key MLOps project aspects, guided by expert trainers to ensure a comprehensive understanding of advanced workflows.
Our Expert Trainers
A Veteran in Cloud, DevOps, and MLOps
- Our lead instructor is a seasoned expert with over 18 years of hands-on experience in Cloud Computing, DevOps, and MLOps. A recognized technology thought leader, he has been a featured speaker at top tech events, delivering insightful sessions on cutting-edge innovations in AI and ML operations. His deep expertise spans across multiple industries, where he has successfully implemented scalable MLOps solutions as a freelancer and consultant.
- Beyond his technical expertise, he brings a decade-long entrepreneurial journey in the IT and technology space, shaping businesses through strategic cloud and DevOps transformations. His commitment to high-quality, practical training ensures that learners gain real-world, industry-relevant skills, not just theoretical knowledge.
- What truly sets him apart is his hands-on approach to teaching—every module in this program is crafted from his direct experiences working on live projects. He believes in experience-driven learning, ensuring that every student gains insights that are applicable to real-world MLOps challenges.
- With a passion for mentoring and empowering tech professionals, he has helped countless learners elevate their careers, master MLOps tools, and secure high-paying roles in the AI industry. His training sessions are highly interactive, practical, and focused on real-world problem-solving, making this program one of the best online MLOps training courses with industry experts.
- Join this program to learn from the best, gain hands-on experience with MLOps tools, and build end-to-end ML pipelines under the guidance of a seasoned industry leader.
Our Project Work
- Build a complete machine learning pipeline with MLOps basics.
- Train a regression model to predict house prices.
- Use MLflow to track experiments, log metrics, and manage model versions.
- Deploy the trained model locally using MLflow’s REST API for testing.
- Build and deploy a production-grade ML pipeline using Kubeflow.
- Create a pipeline for fraud detection or image classification.
- Automate model training and evaluation.
- Deploy the trained model with KFServing and scale it using Kubernetes.
- Monitor a deployed ML model (e.g., sentiment analysis) using Prometheus and Grafana.
- Detect model drift and automate alerts for performance degradation.
Who Can Apply for the Course?
This MLOps training program is ideal for professionals looking to master AI model deployment, monitoring, and automation. Whether transitioning from IT roles or deepening your MLOps expertise, this course provides hands-on skills to accelerate your career.
- IT Professionals & DevOps Engineers – Shift from traditional IT roles to high-demand MLOps positions by integrating CI/CD, cloud, and automation into AI workflows.
- Data Scientists & ML Engineers – Learn to operationalize ML models with scalable pipelines, versioning, and automated retraining.
- Beginners with Basic Python & ML Knowledge – Gain foundational MLOps skills and step into AI-driven careers.
- Software Developers & AI Practitioners – Master MLOps tools like Docker, Kubernetes, and Kubeflow for seamless ML deployments.
- Business Analysts & AI Project Managers – Understand MLOps best practices to align AI models with business goals.
Industry Trends
The Rapid Growth of MLOps Careers
Annual Salary
MLOps professionals earn an average annual salary of 7 LPA and above, depending on experience and skills.
Companies Hiring
Top companies like TCS, IBM, Accenture, Cognizant, HCL, Oracle, and Capgemini actively seek MLOps experts to enhance their machine learning operations.
Demand
The demand for MLOps professionals is skyrocketing, with growth expected to triple or quadruple in the coming decade.
Market Growth
The MLOps sector is projected to grow at a CAGR of 24.2% from 2021 to 2030, showcasing immense opportunities.
Market value
The global DevOps market size (closely tied to MLOps) was valued at $6.78 billion in 2020 and is expected to reach $57.90 billion by 2030.
Job Growth
Job opportunities in the MLOps domain are expected to grow by 15% between 2021 and 2031, indicating a robust career path.
Reviews
Gowtham Sai Charan
Students
Dinesh
Students
Naveen D
Students
Ravi Dasoju
Students
Krishnaveni Valluru
Students
Raj Kalam
Students
Santosh Patra
Students
Vinay Krishna
Students
Suryakant Samal
Students
Aparna Panicker
Students
Prasanth Dalvi
Students
Padmapriya Vijayakumar
Students
FAQ'S
Mastering these MLOps tools allows professionals to:
- Automate machine learning workflows, reducing manual intervention
- Ensure model reliability and scalability in production environments
- Optimize deployment strategies using Docker, Kubernetes, and Kubeflow
- Monitor, track, and improve ML models using Prometheus and Grafana
- Build expertise in cloud-based MLOps workflows, making AI applications more efficient
By the end of this program, learners will have gained practical, job-ready skills that are essential for success in MLOps careers. These tools are widely used in top companies, making this course the perfect stepping stone for anyone looking to advance in AI, ML engineering, and DevOps.
MLOps (Machine Learning Operations) is a set of best practices and tools that streamline the development, deployment, and monitoring of machine learning models in production. It ensures scalability, reliability, and automation, making it essential for ML engineers to manage end-to-end model lifecycles efficiently.
Yes, this program covers MLflow for experiment tracking and model versioning and Kubeflow for orchestrating ML pipelines, ensuring you gain hands-on experience with both tools for managing ML workflows.
After completing this course, you can apply for roles such as MLOps Engineer, AI/ML Engineer, Machine Learning Engineer, DevOps Engineer (AI), and Cloud ML Engineer, among others, in top tech companies and AI-driven enterprises.
Yes, this course teaches real-time model monitoring using Prometheus and Grafana, helping you track model performance, detect drift, and set up automated alerts for production environments.
This program offers hands-on, real-world projects, expert-led training, and a comprehensive curriculum covering ML pipelines, Kubernetes, model monitoring, and cloud deployments, ensuring you gain job-ready MLOps skills.
The course is structured to be completed in 8–12 weeks, but proficiency depends on your prior experience and practice. With consistent learning and project work, you can master MLOps workflows in a few months.
Similar Courses
DevOps Master
Program
- 130+ Hrs
- Who needs to Enroll (Perfect Curriculum for Freshers & Less than 3 Years Experienced in IT)
- 3.5 Months Duration
- Basic to Advanced
- 5 LIVE Projects | 100% Career Centered Curriculum
- Self-paced learning Video Option Available
MLOps Master Program
- 120+ Hrs
- Who needs to Enroll (Perfect Curriculum for Freshers & Less than 3 Years Experienced in IT)
- 3.5 Months Duration
- Basic to Advanced
- 5 LIVE Projects | 100% Career Centered Curriculum
- Self-paced learning Video Option Available
Power BI Master Program
- 45+ Hrs
- Who needs to Enroll (Perfect Curriculum for Freshers & Less than 3 Years Experienced in IT)
- 3.5 Months Duration
- Basic to Advanced
- 5 LIVE Projects | 100% Career Centered Curriculum
- Self-paced learning Video Option Available
ServiceNow Program
- 45+ Hrs
- Who needs to Enroll (Perfect Curriculum for Freshers & Less than 3 Years Experienced in IT)
- 3.5 Months Duration
- Basic to Advanced
- 5 LIVE Projects | 100% Career Centered Curriculum
- Self-paced learning Video Option Available
