Detailed Overview of Your ML Ops Training Course

Detailed Overview of Your ML Ops Training Course

Elevate Your Machine Learning Skills with Our Comprehensive MLOps Training

Unlock the full potential of machine learning workflows through our meticulously designed MLOps training program. Spanning 60 hours of live online sessions, this course equips participants with essential skills to manage, deploy, and monitor machine learning models effectively in production environments.

Whether you’re an aspiring MLOps engineer, a software developer venturing into AI, or a data scientist seeking to scale your workflows, this course offers a structured learning path with real-world applications.

Program Highlights

Duration and Delivery Mode

• Duration: 60 hours of intensive training
• Mode: Live online sessions with hands-on coding

Target Audience

Ideal for beginner to intermediate ML practitioners or software engineers eager to dive into the MLOps lifecycle, tools, and best practices.

What You'll Learn

Our training modules are structured to align with the MLOps lifecycle, starting from the basics and progressing to advanced topics:

1. Introduction to MLOps

• Gain a clear understanding of the MLOps philosophy and its distinction from DevOps.
• Learn about the end-to-end MLOps lifecycle and tools such as Docker, Kubernetes, and MLflow for managing workflows.

2. Data and Model Versioning

• Core Concepts:Track and version datasets using tools like Git-LFS and DVC.Manage model versions effectively with MLflow.
• Hands-On Activity: Version a dataset and experiment with ML models.

3. Containerization Essentials

• Learn: The differences between containers and virtual machines, Docker fundamentals, and building reusable ML containers.
• Practical Application: Create and run a Dockerized ML application.

4. CI/CD for ML Pipelines

• Understand the nuances of continuous integration and delivery for machine learning.
• Build and automate pipelines using GitHub Actions and Jenkins.

5. Testing and Validation

• Focus Areas:Automate unit, integration, and data quality tests using tools like Great Expectations.Ensure your pipeline validates models systematically.
• Practical Task: Build a robust automated validation pipeline.

6. Model Deployment

• Learn various deployment strategies: batch, online, and edge deployments.
• Explore popular tools such as TensorFlow Serving, FastAPI, and Flask for serving models.
• Hands-On Project: Deploy an ML model using Docker and FastAPI.

7. Monitoring and Logging

• Monitor model performance in production, including drift detection, latency tracking, and error handling.
• Implement tools like Prometheus, Grafana, and the ELK stack to build monitoring dashboards.

8. Orchestrating ML Workflows with Kubernetes

• Delve into Kubernetes architecture, including pods, deployments, and services.
• Deploy and manage scalable ML applications on a Kubernetes cluster.

9. Kubeflow for MLOps

• Learn how to streamline workflows using Kubeflow pipelines and components.
• Build and manage end-to-end pipelines for ML training and deployment.

10. Automating ML Pipelines

• Automate the lifecycle of ML models, from training to validation and deployment, using Apache Airflow or Kubeflow Pipelines.

11. Scaling ML Workflows

• Scale your machine learning workloads with distributed training frameworks like Horovod or TensorFlow.
• Use Kubernetes autoscaling for efficient resource allocation.

12. Advanced Topics

• Dive into cutting-edge areas such as explainable AI, model security, and continuous training pipelines.

13. Capstone Project

Apply everything you’ve learned by developing a scalable, monitored ML application.

• Example Use Case: Predict customer churn with real-time monitoring.
• Tasks Include:Data preprocessing and versioningAutomated training and deployment pipelineMonitoring and drift detection

Program Deliverables

Upon completing the course, participants will receive:

1. Comprehensive training slides and notes.
2. Access to recorded sessions for future reference.
3. Project templates and code repositories to build on post-training.
4. A Certification of Completion.

Tools and Technologies

The course emphasizes hands-on learning using the most relevant tools in the industry:

• Cloud Platforms: AWS, Google Cloud, Azure
• Collaboration Tools: Slack or Discord for Q&A
• Core Technologies: Docker, Kubernetes, MLflow, TensorFlow Serving, FastAPI, Kubeflow, Apache Airflow

Why Choose This Course?

Comprehensive Curriculum

From the foundational principles to advanced topics like security and explainability, the course covers everything you need to excel in MLOps.

Hands-On Experience

Participants will work on real-world projects and implement concepts learned during the sessions.

Expert Instruction

Our trainers are experienced professionals who blend theory with practice to ensure a deep understanding of MLOps principles.

Practical Focus

With activities such as containerizing applications, setting up CI/CD pipelines, and deploying scalable workflows, the course provides practical, job-ready skills.

Prerequisites

To make the most of this course, participants should have:

• Basic knowledge of Python and machine learning models.
• Familiarity with Git and version control.
• A foundational understanding of cloud services is advantageous but not mandatory.

Enroll Now

Seize this opportunity to master the art of MLOps and become a skilled practitioner capable of bridging the gap between data science and production environments. Join our comprehensive training program and transform your career today!

For inquiries or registration, please contact DM Me.