MLOps Bootcamp: Mastering AI Operations for Success – AIOps
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
Product Brand: Udemy
4.6
Udemy Coupon Code for MLOps Bootcamp: Mastering AI Operations for Success – AIOps Course. Unlock success in AI Operations with our MLOps Bootcamp – mastering tools,techniques, AIOps for cutting-edge expertise
Created by Manifold AI Learning | 35 hours on-demand video course
MLOps Bootcamp Course Overview
MLOps Bootcamp: Mastering AI Operations for Success – AIOps Course Coupon Code. Welcome to our extensive MLOps Bootcamp (AI Ops Bootcamp), a transformative learning journey designed to equip you with the skills and knowledge essential for success in the dynamic field of Machine Learning Operations (MLOps). This comprehensive program covers a diverse range of topics, from Python and Data Science fundamentals to advanced Machine Learning workflows, Git essentials, Docker for Machine Learning, CI/CD pipelines, and beyond.
What you’ll 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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