125 docs tagged with "Machine Learning"

CPU, GPU, and TPU roles for AI training and inference

Advanced Fine-Tuning

Advanced settings for optimizing model performance

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

GCP AI Hypercomputer for large-scale AI workloads

AI-assisted techniques for improving performance of applications

AI-assisted techniques for improving software testing

Starter Notes on AI

Attention Mechanisms

Automated Testing

Automated Experiment Tracking

Automation

Automation Levels

Building applications using local large language models

Building Blocks of LLM

Building the API

Adopting ChatGPT

CI/CD/CT/CM

CICD Pipelines

Cloud TPU optimization principles and practical workload patterns

Cloud TPU architecture, scaling model, and core platform concepts

Using large language models for coding and content generation tasks

Notes from DataCamp's Understanding Machine Learning Course

Making systems flexible with configuration files

System Messages and Model parameters in Ollama

Data Preparation

Data Quality and Ingestion

Data Versioning

Using AI to assist in database design, schema mapping, duplicate detection, and query optimization

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

DVC Metrics and Plots

Writing effective ML documentation

Running LLM locally with LM Studio

Experiment Tracking

Explainability and Interpretability

Exploratory Data Analysis

Feature Engineering

Feature Engineering

Feature Engineering

Feature Store

Feature Stores

Google Cloud platforms for provisioning and running GPU clusters

CUDA, XLA, and framework portability patterns for GPU-accelerated AI workloads

ML Productivity Goodput and practical GPU optimization patterns for AI workloads

Google Cloud GPU machine families for AI training and inference

Hardware Requirements for Open LLMs

Hyperparameter Tuning

Hyperparameter Tuning with GitHub Actions

Notes from DataCamp's Understanding Machine Learning Course

Introduction

Introduction

Notes from DataCamp's Understanding Machine Learning Course

Running LLM locally with LM Studio

Logging Experiments in MLFlow

Notes from DataCamp's Understanding Machine Learning Course

Writing Maintenable ML Code

View and manage models in Ollama

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 parameters and their impact on performance

Model Registry

Model Reliability

Model Training

Integrating model training into a CI/CD pipeline using Github Actions.

Creating model blueprints using Modelfiles in Ollama

Monitoring

Monitoring Models

Multimodal capabilities in AI models

Notes from DataCamp's Understanding Machine Learning Course

Running LLM locally with Ollama

Managing Ollama server for local AI models

Optimizing AI performance using various prompt engineering techniques

Orchestration

Packaging Machine Learning Models

Pre-Training

Profiling and Versioning

Creating effective prompts for AI models.

Techniques for guiding AI model responses through prompts

Quantization in Open LLMs

Reference Architecture

Designing reproducible experiments

Retraining the Model

Scalability

Serving Modes

Setting up the Python environment for AI development

Start Here

Starter Notes on LLM

Starter Notes on Open LLMs

Using structured output with JSON schemas for better automation and data processing

Notes from DataCamp's Understanding Machine Learning Course

Testing

Testing Data

Testing Models

The Human Side of AI

The MLOps Mindset

Cloud TPU consumption options, quota planning, and DWS scheduling modes

Cloud TPU deployment options and workload scaling patterns

Overview of the Cloud TPU family

TPU runtime version choices and practical TPU and GPU interoperability patterns

Cloud TPU architecture, scaling model, and core platform concepts

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