What is MLOps | MLOps Roadmap | Getting Started with MLOps | Machine Learning Operations

[@HarpaAI]

🎯 Key Takeaways for quick navigation:
00:17 🚀 *Introduction to MLOps*
- Introduction to MLOps and its importance in modern businesses,
- Highlighting the need for automation in the machine learning cycle from development to deployment.
01:10 🧭 *What is MLOps?*
- Explanation of MLOps (Machine Learning Operations) and its role in taking ML models to production,
- Comparison of MLOps to creating a fully developed park, emphasizing the importance of infrastructure for ML models.
02:33 📚 *Foundational Knowledge for MLOps*
- The importance of a solid foundation in data science, machine learning, and coding for MLOps,
- Emphasis on the need for expertise in programming languages like Python, understanding ML models, libraries, frameworks, and database management.
03:28 🔄 *Connection between SDLC and MLOps*
- Explanation of Software Development Lifecycle (SDLC) and its connection to MLOps,
- How the incorporation of ML into products has influenced SDLC principles and led to the rise of MLOps.
04:23 🔀 *Importance of DevOps in MLOps*
- Distinction between DevOps and MLOps, with a focus on their respective roles,
- Why learning DevOps is essential for building a foundation for MLOps, emphasizing key concepts like CI/CD, infrastructure as code, and programming languages.
05:19 📊 *Basics of MLOps: Data Versioning*
- Introduction to data versioning, its purpose in keeping track of data changes,
- Mention of popular tools used for data versioning in MLOps.
06:10 🧪 *Basics of MLOps: Model Versioning and Experiment Tracking*
- The importance of model versioning in MLOps, tracking ML models, parameters, and experiments,
- Introduction to experiment tracking and its role in managing and organizing experiment components.
06:58 🚀 *Basics of MLOps: Model Deployment and CI/CD*
- Steps involved in model deployment in MLOps, including packaging ML models with Docker,
- The significance of continuous integration and continuous deployment (CI/CD) in ML, and the automation of code changes and deployments.
07:52 📈 *Model Monitoring in MLOps*
- Explanation of the model monitoring stage in MLOps, focusing on checking for model degradation and data drift,
- The purpose of ensuring model performance on real-world data and the tools used for model monitoring.
08:20 🛠️ *Projects for MLOps*
- The importance of portfolio projects for enhancing one's profile in MLOps,
- Guidelines for structuring MLOps projects for transparency, maintenance, reproducibility, and reusability,
- Suggestions for projects aligned with specific job roles and responsibilities in MLOps.
Made with HARPA AI

[@muhammadsamir2243]

thanks bro

[@HarpaAI]

You're welcome! @@muhammadsamir2243

[@ahamedarif9933]

i'm very intrested in ai . and i am not good at math. so can you tell me which ai skill i need to learn or what will be better for me?

[@Analyticsvidhya]

Certainly! If you're interested in AI and less confident in math, there are roles in the MLOps (Machine Learning Operations) and AIOps (Artificial Intelligence Operations) domains that are less math-intensive. Some of these roles include:
➡️ MLOps Engineer
This role focuses on deploying, monitoring, and maintaining machine learning models in production. It requires more technical skills in software engineering and infrastructure management than advanced math.
➡️ AI Product Manager
Managing AI projects, defining product objectives, and coordinating between technical and non-technical teams to ensure successful AI implementations.
➡️ AI Quality Assurance (QA) Specialist
Ensuring the quality and accuracy of AI models and systems, which involves testing, validation, and feedback without in-depth math requirements.
➡️ AI Data Engineer
Preparing and managing data for AI and machine learning, which involves data collection, transformation, and integration, with less emphasis on complex math.
➡️ AI Business Analyst
Focusing on understanding business requirements for AI projects, translating them into technical specifications, and working closely with technical teams.
These roles within MLOps and AIOps emphasize various skills, including technical, project management, and domain knowledge, while reducing the need for extensive math proficiency. It's important to explore these roles and find the one that aligns with your skills and interests in the AI field.

[@ahamedarif9933]

@@Analyticsvidhya thank you for your reply, it is very helpful . Hope we can work together in the upcoming future in the field of Ai. your videos encouraging me to go further .. keep making this kind of informative videos.
your well wisher ,
Ahmed arif..