What is MLOps?

MLOps (Machine Learning Operations) integrates machine learning (ML) systems with DevOps principles, enabling the effective deployment, management, and scaling of ML models in production. This course introduces essential tools, such as Kubernetes for container orchestration, TensorFlow for model building, MLflow for model tracking, and Apache Airflow for workflow automation.

Why MLOps is Important

MLOps is critical for organizations to streamline the ML lifecycle, ensuring consistency, efficiency, and automation from model training to deployment. Learning MLOps enables professionals to handle model versioning, track experiments, automate workflows, and facilitate collaboration, bridging the gap between data science and IT operations.

Course Features

• In-Depth Curriculum: Covers all stages of the ML lifecycle, from development to monitoring.
• Hands-On Labs: Real-world MLOps practices, enabling practical application.
• Expert Guidance: Led by experienced instructor Rajesh Kumar.
• Certification: Approved by DevOpsSchool and recognized in the industry.

Training Objectives

Participants will gain skills in:

• Building and deploying ML models efficiently using MLOps tools.
• Automating model workflows with tools like Apache Airflow.
• Managing and tracking models using MLflow and related frameworks.
• Monitoring and scaling ML models in production with Kubernetes.

Target Audience

Ideal for:

• Machine Learning Engineers and Data Scientists looking to operationalize ML models.
• DevOps Engineers interested in managing ML models.
• Professionals with a background in data science, DevOps, or software engineering.

Training Methodology

The training includes a blend of:

• Lectures and Workshops: For a solid theoretical foundation and practical application.
• Hands-On Labs: Real-world exercises to apply MLOps tools effectively.
• Q&A Sessions: Addressing real-world challenges and solutions.

Training Materials

Provided resources:

• Detailed Handouts and Guides
• Video Tutorials for after-course revision.
• Presentations and Lab Guides to reinforce learning.

Evaluation

Training effectiveness is measured by:

• Pre- and Post-Tests to assess knowledge gain.
• Practical Assignments for hands-on evaluation.
• Participant Feedback through surveys for continuous improvement.

Certifications Program

After completing the program, participants are eligible for:

• MLOps Master Certification from DevOpsSchool.
• Recognized credentials in MLOps lifecycle management and automation.

Agenda Daywise for MLOps Training Program

Day	Topics	Description
Day 1	Introduction to MLOps and Principles	Overview of MLOps and key concepts.
	Introduction to Docker and Kubernetes	Basics of containerization and orchestration for ML models.
	Lab Session: Setting Up Kubernetes for ML Models	Practical setup for ML workflows in Kubernetes.
Day 2	Working with TensorFlow for Model Building	Training and building models with TensorFlow.
	Introduction to MLflow for Experiment Tracking	Setting up MLflow for version control and tracking.
	Lab Session: Model Experimentation and Tracking with MLflow	Hands-on experiment tracking using MLflow.
Day 3	Using Apache Airflow for Workflow Automation	Automating ML pipelines with Airflow.
	Best Practices in ML Workflow Automation	Guidelines for efficient ML workflow management.
	Lab Session: Building Automated ML Pipelines with Airflow	Practical automation of model workflows.
Day 4	Monitoring ML Models in Production	Overview of monitoring practices and tools.
	Introduction to Prometheus and Grafana for ML Model Monitoring	Tools and methods for real-time model monitoring.
	Lab Session: Setting Up Model Monitoring Dashboards	Creating dashboards to track model performance.
Day 5	Using Git for Model Version Control and Collaboration	Version control practices for ML models.
	Final Project: End-to-End ML Pipeline Creation and Monitoring	Comprehensive pipeline project from model training to monitoring.
	Certification Exam Preparation and Q&A	Review and Q&A for certification.

Lab Setup

Participants should have:

• Docker and Kubernetes environments for container orchestration.
• MLflow, Airflow, and GitHub accounts for model tracking and versioning.
• Prometheus and Grafana instances for monitoring dashboards.

Trainers

Rajesh Kumar, a DevOps and MLOps expert with extensive experience in ML model deployment, tracking, and management.

FAQ

Question	Answer
What are the prerequisites for this course?	Basic knowledge of machine learning, DevOps, and version control.
Do I need programming skills to take this course?	Knowledge of Python is highly recommended but not mandatory.
How long is the certification valid?	Certification is valid for 3 years, after which recertification is recommended.
Can I take this course remotely?	Yes, the course is available online with live, interactive sessions.
Will there be a certification exam at the end of the course?	Yes, a certification exam will be conducted at the end of the program.
Are hands-on exercises included in the course?	Yes, hands-on labs and exercises are included for practical learning.
What kind of projects will I work on?	Projects include creating automated ML workflows and deploying ML models with MLOps tools.
Will I receive post-training support?	Yes, post-training support is provided to help with real-world challenges.
What certification will I receive?	The MLOps Master Certification recognized by DevOpsSchool.
Can I rewatch sessions if I miss them?	Yes, recorded sessions are available for all participants after each session.

What do aspirants think about our certification?

Aspirants of the MLOps Master Certification Program highly value the course for several reasons, and their feedback reflects the following positive sentiments:

1. Industry-Relevant Skills: Many participants find the certification essential for their careers, as it covers a practical skill set that aligns with the increasing industry demand for MLOps expertise.
2. Hands-On Experience: Learners appreciate the course’s lab-focused approach, where they work directly with tools like Kubernetes, MLflow, and Apache Airflow. This hands-on practice makes it easier for them to apply what they learn to real-world situations.
3. Structured and Comprehensive Curriculum: The organized and comprehensive curriculum, combined with structured day-wise agendas, helps them master the MLOps lifecycle, from development to deployment and monitoring.
4. Guidance from Expert Trainers: Participants benefit from having seasoned trainers like Rajesh Kumar, whose deep industry knowledge and practical insights make complex concepts accessible.
5. Boost in Career Opportunities: Many aspirants report that the certification gives them an edge in job interviews and advancement opportunities, providing recognized credentials that validate their expertise in MLOps.