May 1, 2024
4 minute read
Machine Learning Pipelines (ML Pipelines) are a critical component of the modern data science workflow. They enable data scientists and engineers to automate and streamline the end-to-end process of building, training, and deploying machine learning models. By leveraging ML Pipelines, practitioners can improve efficiency, reduce errors, and accelerate the delivery of data-driven solutions.
Benefits of Learning About ML Pipelines
There are numerous benefits to learning about and gaining proficiency in ML Pipelines. These include:
kow5fn|
Find a path to becoming a ML Pipelines. Learn more at:
OpenCourser.com/topic/kow5fn/ml
Reading list
We've selected ten 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
ML Pipelines.
Focuses on the practical aspects of building and automating ML pipelines using MLOps principles. It covers topics such as version control, continuous integration and delivery, and monitoring.
Provides a gentle introduction to ML pipelines in Python. It covers topics such as data wrangling, feature engineering, and model selection.
Focuses on using Azure Machine Learning to automate ML pipelines. It covers topics such as data preprocessing, feature engineering, and model training.
Covers feature engineering techniques that are essential for building effective ML models. It valuable resource for data scientists who want to improve the performance of their ML pipelines.
Provides a comprehensive overview of ML concepts and techniques using PyTorch and Scikit-Learn. It covers topics such as data preprocessing, model training, and model evaluation.
Provides a hands-on introduction to deep learning using Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a hands-on introduction to natural language processing (NLP) using transformers. It covers topics such as text classification, text generation, and machine translation.
Provides a non-technical overview of data science for business professionals. It covers topics such as data mining, data analytics, and machine learning.
Provides a high-level overview of machine learning for engineers and practitioners. It covers topics such as machine learning algorithms, model selection, and performance evaluation.
Provides a practical introduction to machine learning for programmers and hackers. It covers topics such as data wrangling, feature engineering, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/kow5fn/ml
Save
