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#Data Drift

Showing: 27 courses
MLOps World: Machine Learning in Production

MLOps at Acerta - Automating the Machine Learning Lifecycle for Manufacturing

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Automate the machine learning lifecycle for manufacturing, enhancing efficiency, scalability, and reproducibility. Learn to build a robust MLOps platform for production-grade systems.
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1 Lesons
34 minutes
On-Demand
Free-Video
MLOps World: Machine Learning in Production

Machine Learning Monitoring in Production - Lessons Learned from 30+ Use Cases

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Discover practical strategies for effective machine learning monitoring in production, drawing insights from over 30 real-world use cases.
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1 Lesons
56 minutes
On-Demand
Free-Video
MLOps World: Machine Learning in Production

How Not to Let Your Data and Model Drift Away Silently

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Discover strategies to prevent data and model drift, ensuring ML models maintain performance and value over time in production environments.
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1 Lesons
40 minutes
On-Demand
Free-Video
MLOps World: Machine Learning in Production

Introduction to Drifter ML: A Testing Framework for Machine Learning Models

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Explore Drifter-ML, a testing framework ensuring ML models meet training assumptions. Learn motivations and practical examples for model testing.
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1 Lesons
36 minutes
On-Demand
Free-Video
MLOps World: Machine Learning in Production

Building Production ML Monitoring from Scratch - Live Coding Session

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Build a cloud-native ML monitoring stack using open-source tools. Create a live web dashboard to measure model drift, feature statistics, and performance metrics in production, with Python-based custom metrics integration.
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1 Lesons
1 hour 29 minutes
On-Demand
Free-Video
Data Science Dojo

How to Detect Silent Failures in ML Models

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Learn techniques to detect and address silent failures in machine learning models, including data drift detection, performance estimation, and monitoring strategies, without relying on ground truth data.
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7 Lesons
59 minutes
On-Demand
Free-Video
Data Science Dojo

Building and Maintaining High-Performance AI

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Learn strategies for developing and maintaining high-performance AI models, including testing methods, drift detection, and systematic debugging. Gain insights on model monitoring and iteration for optimal performance.
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14 Lesons
51 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

The Missing Piece of MLOps: Observability in Machine Learning Applications

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Explore ML observability's role in MLOps, learn to prepare for it, and discover how it showcases business value and recognizes data scientists' work.
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1 Lesons
1 hour 2 minutes
On-Demand
Free-Video
EuroPython Conference

One Analysis a Day Keeps Anomalies Away - Detecting and Analyzing Data Anomalies

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Explore Python techniques for data observability, anomaly detection, and root cause analysis in multivariate time series. Learn to distinguish anomalies from data drift and turn insights into opportunities.
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1 Lesons
28 minutes
On-Demand
Free-Video
Databricks

Efficient MLOps: Developing and Deploying ML Models with Databricks

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Explore efficient MLOps framework design for ML model development and deployment, covering environment management, CI/CD pipelines, and tools like Unity Catalog and MLflow for model monitoring and progression.
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1 Lesons
28 minutes
On-Demand
Free-Video
Conf42

The Next Frontier - Observability in ML Systems

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Explore the frontier of ML systems observability, covering key components, challenges, and future trends in various domains including LLMs and computer vision.
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21 Lesons
15 minutes
On-Demand
Free-Video
The Full Stack

Monitoring ML Models - Full Stack Deep Learning - Spring 2021

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Learn techniques for monitoring ML models in production, including detecting data drift, measuring changes, and using tools to maintain model health and performance over time.
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9 Lesons
37 minutes
On-Demand
Free-Video
Open Data Science

Dataset Management for Computer Vision - Important Component to Delivering Computer Vision Solutions

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Explore dataset management's crucial role in computer vision solutions. Learn to align business goals, express assumptions, and maintain datasets for successful real-world implementations.
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11 Lesons
41 minutes
On-Demand
Free-Video
Databricks

Testing ML Models in Production - Detecting Data and Concept Drift

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Explore robust ML model testing in production using MLflow, SciPy, and statsmodels. Learn key statistical tests, metrics for drift detection, and gain practical insights for maintaining model effectiveness.
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35 Lesons
55 minutes
On-Demand
Free-Video
CNCF [Cloud Native Computing Foundation]

Prometheus in the MLOps Lifecycle

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Explore Prometheus for monitoring ML deployments, addressing data drift, concept drift, and model metrics. Learn to enhance existing setups and integrate with Flyte, Seldon Core, or FastAPI.
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1 Lesons
28 minutes
On-Demand
Free-Video
Linux Foundation

Securing ML Workloads with Kubeflow and MLOps - Pwned By Statistics

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Learn to secure machine learning workloads using Kubeflow and MLOps. Explore techniques like model extraction, hidden data attacks, and leakage detection to protect your ML pipelines and models.
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25 Lesons
51 minutes
On-Demand
Free-Video
PyCon US

Avoid the Top 5 Web Data Pitfalls When Developing AI Models

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Explore strategies to overcome common challenges in AI model development, including data bias, insufficient variety, overfitting, poor quality, and data drift. Learn to create more robust and effective AI models.
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1 Lesons
40 minutes
On-Demand
Free-Video
USENIX

Ekya - Continuous Learning of Video Analytics Models on Edge Compute Servers

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Continuous learning for edge video analytics models, addressing data drift and balancing inference-retraining tradeoffs. Ekya optimizes resource allocation for improved accuracy with fewer GPU resources.
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16 Lesons
17 minutes
On-Demand
Free-Video
USENIX

Observability in the MLOps Lifecycle with Prometheus

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Explore MLOps monitoring with Prometheus, covering ML deployment challenges, data drift, and model metrics. Learn to enhance existing systems and integrate with tools like Flyte, Seldon Core, and FastAPI.
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1 Lesons
24 minutes
On-Demand
Free-Video
MLOps.community

Monitoring Unstructured Data - Lightning Session

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Explore ML observability for unstructured data with Arize. Learn about embeddings, monitoring challenges, and solutions through expert insights and a product demo.
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16 Lesons
13 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Federated Learning for Healthcare: Private and Personal Clinical Modeling

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Explore federated learning's application in healthcare, focusing on training machine learning models across distributed clinical datasets without compromising sensitive data privacy and security.
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1 Lesons
24 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

How Your ML Model Will Fail - And How to Prepare For It

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Explore ML model failure modes, analyze production performance, and set up pragmatic monitoring strategies to prevent degradation and breakage in production.
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1 Lesons
27 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Minimize Risk and Accelerate MLOps with ML Monitoring and Explainability

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Discover strategies to minimize risk and accelerate MLOps through ML monitoring and explainability. Learn to efficiently monitor, troubleshoot, and document ML models for improved performance and compliance.
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18 Lesons
40 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Real-Time Event Streaming Patterns for AI-Native Applications

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Explore how event streaming patterns power AI applications through real-time analytics, data enrichment, and stream processing for enhanced intelligence and reliable infrastructure.
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1 Lesons
30 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

A Guide to Putting Together a Continuous ML Stack

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Implement continuous ML training by automating pipelines, detecting performance issues, and triggering model updates with fresh data for improved MLOps maturity.
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1 Lesons
55 minutes
On-Demand
Free-Video
MLOps.community

Obtaining New Insights on Model Behavior with Fiddler - MLOps Meetup #110

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Gain insights on model behavior using Fiddler for monitoring, explainability, and analysis. Learn to instrument ML models for observability, register models, and publish events to understand data drift and underperforming cohorts.
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30 Lesons
54 minutes
On-Demand
Free-Video
Data Science Festival

Your First Data Science Project: Planning for Success

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Learn effective project management techniques for data science, covering agile and waterfall methodologies, autoML, model monitoring, and data drift to ensure successful implementation of data-driven initiatives.
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1 Lesons
1 hour 3 minutes
On-Demand
Free-Video