88 docs tagged with "Machine Learning"

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