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Mohamed Echout
Enhance your machine-learning models!
This course will teach you
the
tools and techniques to effectively monitor and evaluate model
performance during training.
Read more
Enhance your machine-learning models! This course will teach you the tools and techniques to effectively monitor and evaluate model performance during training.
Read more
Enhance your machine-learning models! This course will teach you the tools and techniques to effectively monitor and evaluate model performance during training.
Ensuring that machine learning models perform optimally during training can be a challenging task, often leading to inefficiencies and inaccuracies in predictive outcomes. In this course, Monitor and Evaluate Model Performance During Training, you’ll gain the ability to effectively assess and enhance your machine learning models. First, you’ll explore the crucial metrics used for evaluating model performance, such as accuracy, precision, recall, F1 score, and the area under the ROC curve. Next, you’ll discover how to visualize training progress and understand the importance of loss curves, confusion matrices, and the use of ROC and precision-recall curves for binary classification. Finally, you’ll learn how to utilize real-time monitoring tools like TensorBoard, Weights & Biases, and MLflow to track and improve your model's training process. When you’re finished with this course, you’ll have the skills and knowledge of machine learning model evaluation needed to ensure your models are trained effectively, yielding reliable and robust predictive results.
Register for this course and see more details by visiting:
OpenCourser.com/course/d1qml9/monitor
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What's inside
Syllabus
Course Overview
Understanding Key Metrics and Visualizing Training Progress
Real-time Monitoring, Anomaly Detection, and Feedback Integration
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches model monitoring and evaluation techniques essential for machine learning practitioners
Led by Mohamed Echout, an experienced instructor in machine learning
Emphasizes real-time monitoring and anomaly detection, crucial for model optimization
Suitable for intermediate-level learners with a foundation in machine learning
May require additional resources for hands-on practice and project implementation
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Monitor and Evaluate Model Performance During Training with these
activities:
Guided Tutorial on Using TensorBoard
Show steps
Enhance understanding of TensorBoard by following a guided tutorial, ensuring proficiency in using this important tool for model monitoring.
Show steps
-
Follow an online tutorial or documentation on using TensorBoard.
-
Apply the learned concepts to a sample model training project.
Create a Presentation on Evaluation Metrics
Show steps
Develop a comprehensive presentation to reinforce understanding of the various evaluation metrics used in model evaluation, including strengths and weaknesses.
Show steps
-
Identify and describe key evaluation metrics, such as accuracy, precision, recall, F1 score, and ROC-AUC.
-
Explain the strengths and limitations of each metric.
-
Share examples of how these metrics are used in practice.
Evaluate Model Performance on New Dataset
Show steps
Apply learned techniques to evaluate model performance on a different dataset for deeper understanding and reinforcement.
Show steps
-
Obtain a new dataset.
-
Apply the learned model evaluation metrics (accuracy, precision, recall, F1 score, ROC-AUC) to the new dataset.
-
Analyze the results and compare with the original evaluation results.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Visualize Model Training Progress
Show steps
Create visualizations to gain a graphical understanding of model training progress and identify areas for improvement.
Show steps
-
Plot loss curves to monitor the reduction in loss function during training.
-
Generate confusion matrices at different training iterations to visualize model performance over time.
-
Create ROC curves to assess the trade-off between true positive rate and false positive rate.
Practice Using Monitoring Tools
Show steps
Gain hands-on experience with monitoring tools to effectively track and debug model training.
Show steps
-
Set up TensorBoard or other monitoring tools for a real-world model training project.
-
Utilize the tools to visualize model metrics, such as loss, accuracy, and ROC curves.
-
Identify potential issues or areas for improvement in the training process.
Summarize Anomaly Detection Techniques
Show steps
Create a comprehensive summary of anomaly detection methods to enhance understanding and identify potential areas of application.
Show steps
-
Research and describe different anomaly detection techniques, such as statistical methods, machine learning algorithms, and deep learning approaches.
-
Provide examples of use cases where anomaly detection is valuable.
-
Discuss the strengths and limitations of each technique.
Contribute to an Open-Source Model Monitoring Library
Show steps
Engage with the open-source community and contribute to a project related to model monitoring, deepening understanding and gaining practical experience.
Show steps
-
Identify an open-source model monitoring library or project.
-
Contribute code, documentation, or other resources to enhance the project.
-
Collaborate with other developers to improve the library's functionality.
Guided Tutorial on Using TensorBoard
Show steps
Enhance understanding of TensorBoard by following a guided tutorial, ensuring proficiency in using this important tool for model monitoring.
Show steps
Show steps
Show steps
- Follow an online tutorial or documentation on using TensorBoard.
- Apply the learned concepts to a sample model training project.
Create a Presentation on Evaluation Metrics
Show steps
Develop a comprehensive presentation to reinforce understanding of the various evaluation metrics used in model evaluation, including strengths and weaknesses.
Show steps
Show steps
Show steps
- Identify and describe key evaluation metrics, such as accuracy, precision, recall, F1 score, and ROC-AUC.
- Explain the strengths and limitations of each metric.
- Share examples of how these metrics are used in practice.
Evaluate Model Performance on New Dataset
Show steps
Apply learned techniques to evaluate model performance on a different dataset for deeper understanding and reinforcement.
Show steps
Show steps
Show steps
- Obtain a new dataset.
- Apply the learned model evaluation metrics (accuracy, precision, recall, F1 score, ROC-AUC) to the new dataset.
- Analyze the results and compare with the original evaluation results.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Visualize Model Training Progress
Show steps
Create visualizations to gain a graphical understanding of model training progress and identify areas for improvement.
Show steps
Show steps
Show steps
- Plot loss curves to monitor the reduction in loss function during training.
- Generate confusion matrices at different training iterations to visualize model performance over time.
- Create ROC curves to assess the trade-off between true positive rate and false positive rate.
Practice Using Monitoring Tools
Show steps
Gain hands-on experience with monitoring tools to effectively track and debug model training.
Show steps
Show steps
Show steps
- Set up TensorBoard or other monitoring tools for a real-world model training project.
- Utilize the tools to visualize model metrics, such as loss, accuracy, and ROC curves.
- Identify potential issues or areas for improvement in the training process.
Summarize Anomaly Detection Techniques
Show steps
Create a comprehensive summary of anomaly detection methods to enhance understanding and identify potential areas of application.
Show steps
Show steps
Show steps
- Research and describe different anomaly detection techniques, such as statistical methods, machine learning algorithms, and deep learning approaches.
- Provide examples of use cases where anomaly detection is valuable.
- Discuss the strengths and limitations of each technique.
Contribute to an Open-Source Model Monitoring Library
Show steps
Engage with the open-source community and contribute to a project related to model monitoring, deepening understanding and gaining practical experience.
Show steps
Show steps
Show steps
- Identify an open-source model monitoring library or project.
- Contribute code, documentation, or other resources to enhance the project.
- Collaborate with other developers to improve the library's functionality.
Career center
Learners who complete Monitor and Evaluate Model Performance During Training will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models for a variety of applications. They use their knowledge of machine learning algorithms, data analysis, and software engineering to build models that can be used to solve real-world problems. This course can help you become a Machine Learning Engineer by providing you with the skills and knowledge you need to build and evaluate machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Data Scientist
Data Scientist
Data Scientists analyze data to extract insights and knowledge that can be used to make better decisions. They use their skills in statistics, data mining, and machine learning to identify patterns and trends in data. This course can help you become a Data Scientist by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use their knowledge of computer science, software engineering, and mathematics to build software that meets the needs of users. This course can help you become a Software Engineer by providing you with the skills and knowledge you need to build and evaluate machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They use their skills in mathematics, statistics, and finance to develop models that can be used to make investment decisions. This course can help you become a Quantitative Analyst by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Business Analyst
Business Analyst
Business Analysts analyze business processes and systems to identify areas for improvement. They use their skills in business analysis, data analysis, and process improvement to develop solutions that can be used to improve business performance. This course can help you become a Business Analyst by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Product Manager
Product Manager
Product Managers are responsible for the development and marketing of products. They work with engineers, designers, and marketers to develop products that meet the needs of users. This course can help you become a Product Manager by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Data Analyst
Data Analyst
Data Analysts analyze data to extract insights and knowledge that can be used to make better decisions. They use their skills in statistics, data mining, and machine learning to identify patterns and trends in data. This course can help you become a Data Analyst by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. They use their skills in finance, accounting, and economics to develop models that can be used to predict the performance of stocks, bonds, and other financial instruments. This course can help you become a Financial Analyst by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Market Researcher
Market Researcher
Market Researchers analyze market data to identify trends and opportunities. They use their skills in statistics, data mining, and marketing to develop insights that can be used to make better marketing decisions. This course can help you become a Market Researcher by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They use their skills in mathematics, statistics, and operations research to develop models that can be used to improve business processes. This course can help you become an Operations Research Analyst by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Risk Analyst
Risk Analyst
Risk Analysts analyze risk to make informed decisions. They use their skills in risk management, finance, and economics to develop models that can be used to predict the likelihood and impact of risks. This course can help you become a Risk Analyst by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Statistician
Statistician
Statisticians use statistical methods to analyze data. They use their skills in statistics, mathematics, and programming to develop models that can be used to make informed decisions. This course can help you become a Statistician by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines. They use their skills in data engineering, software engineering, and cloud computing to develop pipelines that can be used to move data from one place to another. This course may help you become a Data Engineer by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They use their skills in mathematics, statistics, and computer science to develop algorithms that can be used to solve real-world problems. This course may help you become a Machine Learning Researcher by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
Software Developer
Software Developer
Software Developers design, develop, and maintain software systems. They use their skills in software engineering, programming, and mathematics to develop software that meets the needs of users. This course may help you become a Software Developer by providing you with the skills and knowledge you need to evaluate and improve machine learning models. You will learn how to use key metrics to assess model performance, visualize training progress, and use real-time monitoring tools to track and improve your model's training process.
For more career information including salaries, visit:
OpenCourser.com/course/d1qml9/monitor
Reading list
We've selected 18 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Monitor and Evaluate Model Performance During Training.
Save
This comprehensive textbook provides an in-depth overview of deep learning concepts and architectures. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, making it a valuable reference for researchers and practitioners in the field.
This comprehensive textbook covers a wide range of statistical learning methods, including linear regression, logistic regression, and decision trees. It provides a solid theoretical foundation for understanding and applying statistical learning techniques.
Provides a comprehensive overview of deep learning using Python. It valuable resource for anyone interested in learning about deep learning.
Provides a comprehensive guide to machine learning concepts and techniques, with a focus on practical implementation using popular Python libraries like Scikit-Learn, Keras, and TensorFlow. It covers essential topics such as data preprocessing, feature engineering, model selection, and performance evaluation, making it a valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It valuable resource for anyone interested in learning about the theoretical foundations of machine learning.
This classic textbook covers a wide range of machine learning topics, including supervised learning, unsupervised learning, and statistical modeling. It provides a comprehensive overview of the field and is commonly used as a textbook in academic institutions.
Provides a comprehensive overview of reinforcement learning. It valuable resource for anyone interested in learning about the theoretical foundations of reinforcement learning.
Provides a comprehensive overview of computer vision using Python. It valuable resource for anyone interested in learning about computer vision.
Save
This textbook provides a comprehensive overview of machine learning algorithms, with a focus on their mathematical and algorithmic foundations. It covers topics such as linear algebra, optimization, and statistical modeling, making it suitable for advanced learners and researchers.
Provides a comprehensive overview of natural language processing using Python. It valuable resource for anyone interested in learning about natural language processing.
Save
This practical guide provides a comprehensive overview of data mining techniques, with a focus on practical implementation. It covers topics such as data preprocessing, feature selection, and model evaluation, making it suitable for beginners and experienced practitioners alike.
Presents machine learning from a probabilistic perspective, providing a solid theoretical foundation for understanding and developing machine learning algorithms. It covers topics such as Bayesian inference, graphical models, and reinforcement learning, making it suitable for advanced learners and researchers.
This practical guide focuses on implementing machine learning algorithms in Python. It provides hands-on examples and code snippets, making it suitable for beginners and experienced programmers alike.
This introductory textbook provides a clear and concise overview of machine learning concepts and algorithms. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning, making it suitable for beginners.
This practical guide focuses on implementing machine learning algorithms in R. It provides hands-on examples and code snippets, making it suitable for beginners and experienced programmers alike.
Provides a practical introduction to machine learning for those with no prior experience. It great resource for getting started with machine learning.
This beginner-friendly guide provides a clear and concise introduction to machine learning concepts and algorithms. It uses plain English explanations and avoids technical jargon, making it suitable for anyone with no prior knowledge of the field.
Provides a gentle introduction to machine learning for beginners. It great resource for getting started with machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/d1qml9/monitor
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Time
48 hours
Level
Introductory
Via
Pluralsight
Instructor
Mohamed Echout
Language
English
Good to know
Teaches model monitoring and evaluation techniques essential for machine learning practitioners
Led by Mohamed Echout, an experienced instructor in machine learning
Emphasizes real-time monitoring and anomaly detection, crucial for model optimization
Suitable for intermediate-level learners with a foundation in machine learning
May require additional resources for hands-on practice and project implementation
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