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MLOps Bootcamp: Mastering AI Operations for Success – AIOps

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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

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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 | 39 hours on-demand video course

MLOps Bootcamp Course Overview

MLOps Bootcamp: Mastering AI Operations for Success – AIOps

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.

Embark on this comprehensive MLOps Bootcamp to transform your skills and elevate your proficiency in the dynamic and ever-evolving field of Machine Learning Operations. Whether you are a seasoned professional or just starting your journey in MLOps, this program provides the knowledge, tools, and practical experience needed to succeed in implementing robust and efficient Machine Learning workflows. Join us and become a master of MLOps, ready to tackle the challenges of the modern AI landscape with confidence and expertise.

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.

Python Programming for MLOps – AIOps – DevOps

Python Programming for MLOps – AIOps – DevOps

Optimize MLOps, AIOps, and DevOps Workflows with Python

Master the essential Python skills you need to streamline DevOps workflows, implement intelligent MLOps pipelines, and optimize AIOps practices. This Python Programming for MLOps – AIOps – DevOps course dives into Python fundamentals, file automation, command-line mastery, Linux utilities, package management, Docker, CI/CD with AWS, infrastructure automation, and even advanced monitoring and logging techniques.

Complete Machine Learning,NLP Bootcamp MLOPS & Deployment

Complete Machine Learning,NLP Bootcamp MLOPS & Deployment

Master the theory, practice, and math behind ML,NLP. Build real-world projects with MLOPS, git, Dockers with deployment

Are you looking to master Machine Learning (ML) and Natural Language Processing (NLP) from the ground up? This Complete Machine Learning,NLP Bootcamp MLOPS & Deployment course is designed to take you on a journey from understanding the basics to mastering advanced concepts, all while providing practical insights and hands-on experience.

By the end of this course, you’ll have a comprehensive understanding of machine learning and natural language processing, from the basics to advanced concepts. You’ll be able to apply your knowledge to build real-world projects, and you’ll have the skills needed to pursue a career in ML and NLP.

Who this course is for

  • Data scientists seeking to extend their skills into the operational aspects of deploying and maintaining machine learning models.
  • Software developers interested in mastering the tools and practices for integrating machine learning into real-world applications.
  • DevOps professionals aiming to specialize in MLOps and enhance their proficiency in deploying and managing machine learning systems.
  • Data engineers looking to broaden their skill set by incorporating MLOps practices into data pipelines.
  • IT professionals wanting to understand the integration of machine learning models within operational workflows.
  • Individuals passionate about the latest advancements in technology and eager to explore the practical aspects of MLOps.
  • Entrepreneurs and business professionals seeking to understand how MLOps can drive innovation and competitive advantage in their organizations.
  • Students and researchers in the fields of computer science, data science, and related disciplines looking to expand their knowledge in MLOps.
  • Individuals transitioning into roles that involve machine learning operations and deployment.
  • Enthusiasts who are keen to explore the convergence of machine learning and operations, regardless of their current role or background.

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Taught by Manifold AI Learning

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