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#Experiment Tracking

Showing: 33 courses
MLOps World: Machine Learning in Production

Complete ML Lifecycle with MLflow - Learn Its Four Components

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Explore MLflow's four components to simplify the machine learning lifecycle, from experimentation to production, enhancing reproducibility and accelerating development.
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1 Lesons
1 hour 29 minutes
On-Demand
Free-Video
MLOps World: Machine Learning in Production

Using Reproducible Experiments to Create Better Machine Learning Models

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Enhance ML model efficiency through reproducible experiments. Learn to track hyperparameter, code, and data changes using DVC for grid and random search tuning methods.
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1 Lesons
30 minutes
On-Demand
Free-Video
MLOps World: Machine Learning in Production

Reproducible Machine Learning - How Not to Repeat the Past

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Explore techniques for organizing and reproducing deep learning experiments, including standardized logging, versioning, and templated workflows. Enhance collaboration and minimize duplication in ML projects.
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1 Lesons
37 minutes
On-Demand
Free-Video
The Machine Learning Engineer

MLOps: Databricks MLFlow y Optuna para Ajuste de Hiperparámetros - Español

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Optimiza modelos de machine learning con Databricks, MLflow y Optuna. Aprende técnicas de ajuste de hiperparámetros usando XGBoost en un entorno práctico.
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1 Lesons
40 minutes
On-Demand
Free-Video
The Machine Learning Engineer

MLOps with MLflow: Fine-Tuning LLMs, Tracking Experiments, and Logging Models

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Aprende a utilizar MLflow para registrar y gestionar el proceso de ajuste fino de modelos de lenguaje, facilitando el seguimiento de experimentos y la inferencia.
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1 Lesons
53 minutes
On-Demand
Free-Video
The Machine Learning Engineer

MlOps Mlflow: Trabajar con HuggingFace Transformers en Mlflow - Español

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Aprende a integrar HuggingFace Transformers con MLflow para optimizar el desarrollo y despliegue de modelos de lenguaje avanzados.
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1 Lesons
37 minutes
On-Demand
Free-Video
The Machine Learning Engineer

MLOps MLflow: Deep Learning Frameworks PyTorch and TensorFlow in MLflow - Spanish

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Aprende a integrar Tensorflow y Pytorch con MLflow para optimizar tus proyectos de Deep Learning y mejorar el seguimiento de experimentos.
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1 Lesons
27 minutes
On-Demand
Free-Video
Databricks

Managing the Complete Machine Learning Lifecycle with MLflow - Introduction to MLflow Tracking

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Introduction to MLflow: tracking experiments, packaging projects, deploying models, and managing lifecycle. Learn to record, reproduce, and compare ML runs using MLflow's components and UI.
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17 Lesons
58 minutes
On-Demand
Free-Video
NashKnolX

Databricks for MLOps - Experiment Tracking, Model Packaging, and Deployment

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Master MLflow in Databricks: Experiment tracking, model packaging, and deployment for efficient machine learning operations.
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1 Lesons
47 minutes
On-Demand
Free-Video
The Full Stack

Tracking Experiments - Full Stack Deep Learning - March 2019

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Learn to track deep learning experiments using Weights & Biases for a text recognizer project, enhancing your model development workflow and results analysis.
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1 Lesons
32 minutes
On-Demand
Free-Video
The Full Stack

Infrastructure and Tooling - Full Stack Deep Learning - March 2019

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Explore infrastructure and tooling for deep learning projects, covering GPU machines, experiment tracking, and all-in-one solutions like AWS SageMaker.
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1 Lesons
1 hour 14 minutes
On-Demand
Free-Video
Conf42

A Hitchhiker's Guide to the MLOps Experience

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Explore MLOps essentials: data versioning, visualizations, experiment tracking, and model registry. Learn key components and UX tips for streamlining machine learning workflows.
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18 Lesons
24 minutes
On-Demand
Free-Video
Venelin Valkov

Reproducible Machine Learning and Experiment Tracking Pipeline with Python and DVC

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Learn to build reproducible machine learning pipelines using Python and DVC. Track experiments, manage data versions, and compare model metrics for improved ML workflow efficiency.
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6 Lesons
41 minutes
On-Demand
Free-Video
Data Professor

MLOps 101 - A Practical Tutorial on Creating a Machine Learning Project with DagsHub

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Step-by-step tutorial on using DagsHub for machine learning projects, covering project creation, environment setup, remote storage configuration, version control, experiment tracking, and hypothesis exploration.
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12 Lesons
28 minutes
On-Demand
Free-Video
Weights & Biases

Supercharge Your Training With PyTorch Lightning and Weights & Biases

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Learn to enhance ML pipelines using PyTorch Lightning and Weights & Biases. Discover efficient experiment tracking, advanced logging techniques, and how to leverage callbacks for improved model development and visualization.
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11 Lesons
33 minutes
On-Demand
Free-Video
Open Data Science

Deploying Optimized Deep Learning Pipelines

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Survey of deep learning pipeline optimization techniques, exploring quantization, distillation, and math library selection. Discusses trade-offs for deployment and provides insights for efficient ML implementation.
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13 Lesons
45 minutes
On-Demand
Free-Video
Databricks

Simplifying Model Development and Management with MLflow - New Features and Innovations

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Explore MLflow's latest features for simplified experiment tracking, improved model portability, schema management, and faster deployment in machine learning lifecycle management.
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20 Lesons
23 minutes
On-Demand
Free-Video
Databricks

Data Versioning and Reproducible ML with DVC and MLflow

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Learn to enhance ML reproducibility using DVC and MLflow. Explore data versioning, model tracking, and artifact management for efficient ML development and deployment.
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16 Lesons
27 minutes
On-Demand
Free-Video
Weights & Biases

Instrumenting Weights & Biases for PII Data Detection - ML Pipeline Tutorial

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Instrumenting W&B for PII detection: Learn to track experiments, analyze data, and perform error analysis in ML pipelines. Apply knowledge to a Kaggle competition using W&B Tables and Artifacts.
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23 Lesons
58 minutes
On-Demand
Free-Video
Linux Foundation

Building Reproducible ML Processes with an Open Source Stack

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Explore open-source tools for reproducible machine learning, covering code, data, and environment management. Includes hands-on demonstration of experiment replication using KubeFlow, Git, Minio, and lakeFS.
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1 Lesons
33 minutes
On-Demand
Free-Video
The Machine Learning Engineer

MLOps MLFlow: Building Pipelines with MLflow Projects

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Build MLflow project pipelines for efficient machine learning workflows. Learn to create and execute data science pipelines using MLflow Projects.
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1 Lesons
33 minutes
On-Demand
Free-Video
The Machine Learning Engineer

MLOps with MLflow: Fine-Tuning LLMs - Tracking Experiments and Logging Models

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Fine-tune LLMs, track experiments, and log models using MLflow for efficient MLOps practices in data science and machine learning projects.
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1 Lesons
49 minutes
On-Demand
Free-Video
PyCon US

Transforming a Jupyter Notebook into a Reproducible Pipeline for ML Experiments

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Learn to transform Jupyter Notebooks into reproducible ML pipelines using Git and DVC. Explore techniques for experiment tracking, modularization, and result comparison in a text2image project with Stable Diffusion.
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1 Lesons
26 minutes
On-Demand
Free-Video
EuroPython Conference

MLtraq - Track Your ML/AI Experiments at Hyperspeed

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Explore MLtraq's superior speed and capabilities for tracking ML/AI experiments. Compare leading tools, understand performance factors, and learn efficient Python object serialization techniques.
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1 Lesons
22 minutes
On-Demand
Free-Video
Databricks

Advanced Experiment Tracking for LLM-Powered Applications with Customized Open-Source MLFlow

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Discover Intuit's use of open-source MLFlow for enhancing ML models and LLM applications through experiment tracking, reproducibility, model lineage, and streamlined review workflows.
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1 Lesons
18 minutes
On-Demand
Free-Video
Devoxx

MLflow - Platform for Complete Machine Learning Lifecycle

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Discover how MLflow simplifies machine learning development by tracking experiments, registering projects, and streamlining model deployment across various platforms.
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1 Lesons
31 minutes
On-Demand
Free-Video
Conf42

MLOps: From Jupyter to Production - Conf42 ML 2024

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Learn to transition machine learning projects from Jupyter notebooks to production environments, covering MLOps best practices and real-world implementation strategies.
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5 Lesons
30 minutes
On-Demand
Free-Video
MLOps.community

Experiment Tracking in the Age of LLMs - MLOps Podcast

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Explore experiment tracking in the age of LLMs with Neptune's CEO. Gain insights on managing ML experiments, prompt engineering, and achieving control and confidence in model development.
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15 Lesons
45 minutes
On-Demand
Free-Video
The Machine Learning Engineer

MLOps MLflow: Deep Learning Frameworks - PyTorch and TensorFlow Integration

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Explore integrating deep learning frameworks Pytorch and Tensorflow with MLflow for enhanced model management and tracking.
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1 Lesons
30 minutes
On-Demand
Free-Video
The Machine Learning Engineer

MLOps: Integrating HuggingFace Transformers with MLflow

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Integrate HuggingFace Transformers with MLflow for efficient MLOps, enhancing model tracking and deployment in machine learning projects.
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1 Lesons
35 minutes
On-Demand
Free-Video
Canonical Ubuntu

Kubeflow vs MLFlow: Choosing the Right MLOps Platform

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Explore production-grade MLOps with Kubeflow and MLFlow. Compare features, benefits, and use cases to choose the right open-source tool for your machine learning operations.
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18 Lesons
43 minutes
On-Demand
Free-Video
Canonical Ubuntu

Kubeflow vs MLFlow: Comparing Open-Source MLOps Platforms

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Compare Kubeflow and MLFlow for MLOps, exploring their similarities, differences, and selection criteria. Gain insights into open-source machine learning operations and Canonical's solutions.
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8 Lesons
42 minutes
On-Demand
Free-Video
Canonical Ubuntu

Introduction to MLFlow: Managing Machine Learning Workflows

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Explore MLFlow's role in managing machine learning workflows, its key components, and how it fits into the MLOps ecosystem. Learn about Charmed MLFlow and its applications.
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22 Lesons
46 minutes
On-Demand
Free-Video