MLOps Bootcamp: Mastering AI Operations for Success - AIOps
Unlock success in AI Operations with our MLOps Bootcamp – mastering tools,techniques, AIOps for cutting-edge expertise
4.59 (736 reviews)
10,286
students
36.5 hours
content
Feb 2025
last update
$89.99
regular price
What you will learn
Develop a solid foundation in Python, tailored for MLOps applications.
Streamline Machine Learning processes using Python's powerful capabilities.
Leverage Python for effective data manipulation and analysis in Data Science.
Understand how Python enhances the entire data science lifecycle.
Master version control using Git for collaborative development.
Learn to manage and track changes efficiently within MLOps projects.
Dive into the art of packaging Machine Learning models for easy deployment.
Ensure models are reproducible and deployable in diverse environments.
Effectively manage and track Machine Learning experiments using MLflow.
Utilize MLflow for enhanced experiment tracking and management.
Acquire essential skills in YAML for MLOps configuration and deployment.
Gain practical experience in writing and interpreting YAML files.
Explore Docker and its role in containerizing Machine Learning applications.
Understand the advantages of containerization for efficient MLOps.
Develop Machine Learning applications with FastAPI for efficient and scalable deployments.
Explore Streamlit and Flask for creating interactive web applications.
Implement Continuous Integration and Continuous Deployment pipelines for MLOps.
Automate development, testing, and deployment of ML models.
Gain a solid understanding of the Linux operating system.
Explore how Linux is essential for both DevOps and Data Scientists in MLOps.
Dive into Jenkins, an open-source automation server.
Learn to set up and configure Jenkins for automating MLOps workflows.
Develop insights into effective monitoring and debugging strategies for MLOps.
Utilize tools and techniques to identify and address issues in ML systems.
Set up continuous monitoring for MLOps using Prometheus and Grafana
Enhance observability in Machine Learning applications.
Extend Docker skills by mastering Docker Compose.
Learn to deploy multi-container applications seamlessly.
Explore tools and strategies for ongoing performance monitoring in MLOps.
Proactively address issues in production ML systems.
Utilize WhyLogs for efficient monitoring and logging of ML data.
Enhance the observability and traceability of ML systems.
Understand crucial steps for maintaining and updating ML models in a production environment.
Implement best practices for ensuring the long-term success of deployed ML systems.
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udemy ID
7/29/2023
course created date
1/26/2024
course indexed date
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