Advanced Fine-Tuning
Advanced Fine-Tuning
Advanced Fine-Tuning
Notes from DataCamp's Understanding Machine Learning Course
Using AI for coding tasks, including best practices and techniques for effective prompting
Using AI models to assist in writing and understanding code
Starter Notes on AI
Attention Mechanisms
Automated Testing
Automated Experiment Tracking
Automation
Automation Levels
Building Blocks of LLM
Building the API
Adopting ChatGPT
CI/CD/CT/CM
CICD Pipelines
Notes from DataCamp's Understanding Machine Learning Course
Data Preparation
Data Quality and Ingestion
Data Versioning
Notes from DataCamp's Understanding Machine Learning Course
Preparing the model for deployment
Deployment Strategies
Deployment-Driven
Design Patterns
Design Phase
DVC for Data Versioning
Writing effective ML documentation
Experiment Tracking
Explainability and Interpretability
Exploratory Data Analysis
Feature Engineering
Feature Engineering
Feature Engineering
Feature Store
Feature Stores
Hyperparameter Tuning
Notes from DataCamp's Understanding Machine Learning Course
Introduction
Introduction
Notes from DataCamp's Understanding Machine Learning Course
Logging Experiments in MLFlow
Notes from DataCamp's Understanding Machine Learning Course
Writing Maintenable ML Code
Measuring AI Success
Metadata Store
ML Lifecycle
ML Model Lifecycle
MLOps Components
MLOps Lifecycle
MLOps Maturity
Model Build Pipelines
Notes from DataCamp's Understanding Machine Learning Course
Model Evaluation and Visualization
Model Governance
Understanding the constraints and challenges of AI models
Model Maintainance
Model Packaging
Model Registry
Model Reliability
Model Training
Integrating model training into a CI/CD pipeline using Github Actions.
Monitoring
Monitoring Models
Notes from DataCamp's Understanding Machine Learning Course
Optimizing AI performance using various prompt engineering techniques
Orchestration
Packaging Machine Learning Models
Pre-Training
Profiling and Versioning
Fundamentals of creating effective prompts for AI models.
Techniques for guiding AI model responses through prompts
Reference Architecture
Designing reproducible experiments
Retraining the Model
Scalability
Serving Modes
Starter Notes on LLM
Notes from DataCamp's Understanding Machine Learning Course
Testing
Testing Data
Testing Models
The Human Side of AI
The MLOps Mindset
Transformers
Notes from DataCamp's Understanding Machine Learning Course
Unit Tests
Notes from DataCamp's Understanding Machine Learning Course
Using AI models to assist in writing tests and identifying security issues in code