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MLOps | Machine Learning Operations Specialization
Specialization - 4 course series
This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You'll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps
Through this series, you will begin to learn skills for various career paths:
1. Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.
2. Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems.
3. Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner.
4. Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.
Applied Learning Project
Explore and practice your MLOps skills with hands-on practice exercises and Github repositories.
1. Building a Python script to automate data preprocessing and feature extraction for machine learning models.
2. Developing a real-world ML/AI solution using AI pair programming and GitHub Copilot, showcasing your ability to collaborate with AI.
4. Creating web applications and command-line tools for ML model interaction using Gradio, Hugging Face, and the Click framework.
3. Implementing GPU-accelerated ML tasks using Rust for improved performance and efficiency.
4. Training, optimizing, and deploying ML models on Amazon SageMaker and Azure ML for cloud-based MLOps.
5. Designing a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.
6. Fine-tuning and deploying Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face. Creating interactive demos to effectively showcase your work and advancements.
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Course 1
•
43 hours
What you'll learn
- Work with logic in Python, assigning variables and using different data structures.
- Write, run and debug tests using Pytest to validate your work.
- Interact with APIs and SDKs to build command-line tools and HTTP APIs to solve and automate Machine Learning problems.
Skills you'll gain
Category: Python Programming
Python Programming
Category: Object Oriented Programming (OOP)
Object Oriented Programming (OOP)
Category: Software Testing
Software Testing
Category: NumPy
NumPy
Category: Pandas (Python Package)
Pandas (Python Package)
Category: Command-Line Interface
Command-Line Interface
Category: MLOps (Machine Learning Operations)
MLOps (Machine Learning Operations)
Category: Scripting
Scripting
Category: Data Structures
Data Structures
Category: Numerical Analysis
Numerical Analysis
Category: Application Programming Interface (API)
Application Programming Interface (API)
Category: Data Manipulation
Data Manipulation
Category: Program Development
Program Development
Category: Data Import/Export
Data Import/Export
Category: Debugging
Debugging
Category: Machine Learning
Machine Learning
Category: Test Automation
Test Automation
Course 2
•
44 hours
What you'll learn
- Build operations pipelines using DevOps, DataOps, and MLOps
- Explain the principles and practices of MLOps (i.e., data management, model training and development, continuous integration and delivery, etc.)
- Build and deploy machine learning models in a production environment using MLOps tools and platforms.
Skills you'll gain
Category: MLOps (Machine Learning Operations)
MLOps (Machine Learning Operations)
Category: DevOps
DevOps
Category: Containerization
Containerization
Category: CI/CD
CI/CD
Category: Rust (Programming Language)
Rust (Programming Language)
Category: Docker (Software)
Docker (Software)
Category: Machine Learning
Machine Learning
Category: Generative AI Agents
Generative AI Agents
Category: Artificial Intelligence and Machine Learning (AI/ML)
Artificial Intelligence and Machine Learning (AI/ML)
Category: Cloud Solutions
Cloud Solutions
Category: Application Frameworks
Application Frameworks
Category: Big Data
Big Data
Category: Command-Line Interface
Command-Line Interface
Category: Serverless Computing
Serverless Computing
Category: Data Ethics
Data Ethics
Category: Python Programming
Python Programming
Category: GitHub
GitHub
Category: Applied Machine Learning
Applied Machine Learning
MLOps Platforms: Amazon SageMaker and Azure ML
Course 3
•
31 hours
What you'll learn
- Apply exploratory data analysis (EDA) techniques to data science problems and datasets.
- Build machine learning modeling solutions using both AWS and Azure technology.
- Train and deploy machine learning solutions to a production environment using cloud technology.
Skills you'll gain
Category: MLOps (Machine Learning Operations)
MLOps (Machine Learning Operations)
Category: Microsoft Azure
Microsoft Azure
Category: Containerization
Containerization
Category: Exploratory Data Analysis
Exploratory Data Analysis
Category: Cloud Solutions
Cloud Solutions
Category: Amazon Web Services
Amazon Web Services
Category: Python Programming
Python Programming
Category: Serverless Computing
Serverless Computing
Category: Data Analysis
Data Analysis
Category: Machine Learning Algorithms
Machine Learning Algorithms
Category: Machine Learning
Machine Learning
Category: AWS SageMaker
AWS SageMaker
Category: Artificial Intelligence and Machine Learning (AI/ML)
Artificial Intelligence and Machine Learning (AI/ML)
Category: Feature Engineering
Feature Engineering
Category: Data Pipelines
Data Pipelines
Category: Predictive Modeling
Predictive Modeling
MLOps Tools: MLflow and Hugging Face
Course 4
•
25 hours
What you'll learn
- Create new MLflow projects to create and register models.
- Use Hugging Face models and datasets to build your own APIs.
- Package and deploy Hugging Face to the Cloud using automation.
Skills you'll gain
Category: MLOps (Machine Learning Operations)
MLOps (Machine Learning Operations)
Category: Containerization
Containerization
Category: Docker (Software)
Docker (Software)
Category: Microsoft Azure
Microsoft Azure
Category: Application Deployment
Application Deployment
Category: Machine Learning Software
Machine Learning Software
Category: GitHub
GitHub
Category: Development Environment
Development Environment
Category: Application Programming Interface (API)
Application Programming Interface (API)
Category: Cloud Applications
Cloud Applications
Category: CI/CD
CI/CD
Category: Cloud Computing
Cloud Computing
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