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Are you ready to bridge the gap between machine learning development and production deployment? "MLOps: From Zero to Hero" is your comprehensive guide to the entire lifecycle of machine learning operations. This course is designed for data scientists, machine learning engineers, and DevOps professionals looking to streamline the process of taking ML models from experimentation to scalable, production-grade systems.
Course Overview:
In this course, you'll journey through the complete MLOps pipeline, starting with the fundamentals and progressing to advanced topics in model deployment and monitoring. Through hands-on projects and real-world case studies, you will develop the skills needed to build robust, scalable, and maintainable AI systems. By the end of the course, you will be equipped to implement MLOps best practices in your organization, ensuring smooth collaboration between data scientists and operations teams.
What You Will Learn:
1. MLOps Fundamentals:
- Introduction to MLOps and AI project lifecycle
- Setting up MLOps pipelines with popular tools and frameworks
- Understanding Weights and Biases (WandB) for experiment tracking
2. Data Management and Processing:
- Building data preparation and processing pipelines
- Implementing exploratory data analysis (EDA) for fraud detection
- Techniques for data cleaning, validation, and feature engineering
- Detecting and handling data drift in production systems
3. Model Development and Training:
- Building and optimizing model training components
- Implementing hyperparameter tuning for improved model performance
- Best practices for model evaluation and selection
4. Model Deployment and Serving:
- Strategies for deploying ML models to production environments
- Containerization with Docker for consistent deployments
- Cloud deployment using Google Cloud Platform (GCP)
- Building and testing model deployment servers
5. MLOps Tools and Frameworks:
- Leveraging MLflow for end-to-end machine learning lifecycle management
- Using YAML for configuration management in ML projects
- Integrating continuous integration and continuous deployment (CI/CD) for ML workflows
6. Advanced Topics and Best Practices:
- Implementing A/B testing and model comparison in production
- Monitoring and maintaining ML models in live environments
- Addressing bias and ensuring fairness in AI systems
- Scaling MLOps practices for enterprise-level AI initiatives
Why Choose "MLOps: From Zero to Hero"?
- Comprehensive Learning Path: Cover the entire MLOps lifecycle, from data collection to model monitoring in production.
- Practical, Hands-On Experience: Engage in real-world projects that simulate actual MLOps challenges faced by organizations.
- Industry-Relevant Tools: Gain proficiency in popular MLOps tools and frameworks used by leading tech companies.
- Best Practices and Patterns: Learn established MLOps patterns and anti-patterns to build robust AI systems.
- Flexibility: Suitable for both beginners in MLOps and experienced practitioners looking to formalize their knowledge.
- Expert Guidance: Learn from instructors with extensive experience in implementing MLOps at scale.
Take Your ML Projects to the Next Level:
This course isn't just about learning MLOps concepts; it's about transforming how you approach machine learning projects from inception to production. Enroll in "MLOps: From Zero to Hero" today and start your journey towards becoming an MLOps expert capable of delivering impactful AI solutions at scale.
$35
/ MonthAccelerate your Career and boost your Earning Potential with our Expert Instructors & Community! What Youāll Unlock: - š 40+ Courses (over 200 hours!) - š 17 Specialized Career Paths - š Quizzes and Practice Activities - š Certificates for Courses & Career Paths - š Prizes for Learning - š§Ŗ Weekly Live Labs (plus recordings!) - šÆ Monthly Missions to practice even more - š¼ Monthly Career Booster Events - š¬ Full access to the SDS Community - ā” Monthly Speed Networking - š„ Monthly Resume Clinics - š„ Group Mentorship Program "In just a few months of learning at SDS, I landed a Data Analyst job!" ā Sanaz Afshar, California
$157
/ MonthAccelerate your Career and boost your Earning Potential with our Expert Instructors & Community! What Youāll Unlock: - š 40+ Courses (over 200 hours!) - š 17 Specialized Career Paths - š Quizzes and Practice Activities - š Certificates for Courses & Career Paths - š Prizes for Learning - š§Ŗ Weekly Live Labs (plus recordings!) - šÆ Monthly Missions to practice even more - š¼ Monthly Career Booster Events - š¬ Full access to the SDS Community - ā” Monthly Speed Networking - š„ Monthly Resume Clinics - š„ Group Mentorship Program Plus, Pro Plan perks just for you: - š A Personalized Career Path built around your goals - š§āš« 1-on-1 Mentoring Sessions every month - āļø Personalized Resume Reviews "In just a few months of learning at SDS, I landed a Data Analyst job!" ā Sanaz Afshar, California
Course content
Welcome Challenge!
MLOps Intro and AI Project Lifecycle
08:19
Walkthrough: Simple MLOps Pipeline
08:56
Introduction to Weights and Biases (WandB)
06:46
Exercise: Creating WandB Project - Part 1
08:08
Exercise: Creating WandB Project - Part 2
04:56
Hyperparameter Logging and Model Versioning Using WandB
12:08
Comparing Different Models in WandB
04:27
Dataset Versioning in WandB Library
07:34
WandB Sweeps: Hyperparameter Optimization
13:01
Introduction to MLFlow and Setting Local MLFlow Server
07:51
Logging a Training Run Using MLFlow
06:58
Model Versioning and Saving Using MLFlow
06:40
Introduction to YAML
07:05
YAML Exercise Solution
02:53
Building MLFlow Components - Part 1
07:59
Building MLFlow Components - Part 2
06:24
MLFlow and WandB Components
07:36
Building: EDA Component for Credit Card Fraud Detection Project - Part 1
06:56
Building: EDA Component for Credit Card Fraud Detection Project - Part 2
05:34
Building Data Cleaning Component
09:32
Building Data Validation Component
08:04
Exercise Solution: Data Validation Component
01:36
Building Feature Engineering Component
09:56
Data Drift Detection Component
05:52
Building Model Training Component
06:54
Exercise Solution: Model Training
07:17
Building Hyperparameter Tuning Component
05:43
Exercise Solution: Hyperparameter Tuning
09:57
Building Model Evaluation Component
01:07
Exercise Solution: Model Evaluation
02:27
Model Deployment Component
10:16
Testing Model Deployment Server Locally
09:30
Introduction to Docker and Deploying ML Models Using Docker
07:38
Deploying ML Model to Cloud Using GCP
02:16
Data Collection
07:20
Problems with Data Collection in Production
01:24
Bias in Your Data
04:56
Data Collection Techniques
06:58
Model Training
06:29
Decisions Before Training an ML Model
05:32
Model Evaluation
07:11
Techniques to Compare ML Models in Production
07:44
Model Deployment
05:38
Techniques of Deploying ML Models to Production Using FastAPI
07:07
Model Monitoring
07:21