Scaling scikit-learn Solutions from Pluralsight

This course covers the important considerations for scikit-learn models in improving prediction latency and throughput; specific feature representation and partial learning techniques, as well as implementations of incremental learning, out-of-core learning, and multicore parallelism.

Even as the number of machine learning frameworks and libraries increases rapidly, scikit-learn is retaining its popularity with ease. scikit-learn makes the common use-cases in machine learning - clustering, classification, dimensionality reduction and regression - incredibly easy.

In this course, Scaling scikit-learn Solutions you will gain the ability to leverage out-of-core learning and multicore parallelism in scikit-learn.

First, you will learn considerations that affect latency and throughput in prediction, including the number of features, feature complexity, and model complexity.

Next, you will discover how smart choices in feature representation and in how you model sparse data can improve the scalability of your models. You will then understand what incremental learning is, and how to use scikit-learn estimators that support this key enabler of out-of-core learning.

Finally, you will round out your knowledge by parallelizing key tasks such as cross-validation, hyperparameter tuning, and ensemble learning.

When you’re finished with this course, you will have the skills and knowledge to identify key techniques to help make your model scalable and implement them appropriately for your use-case.

What's inside

Syllabus

Course Overview

Understanding Strategies for Computational Scaling

Observing the Factors Affecting Prediction Latency

Implementing Scaling of Instances Using Out-of-core Learning

Implementing Multicore Parallelism in scikit-learn

Autoscaling of scikit-learn with Apache Spark

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores out-of-core learning and multicore parallelism, which are highly relevant to scaling machine learning solutions in industry

Taught by Janani Ravi, who are recognized for their work in the field of machine learning

Develops skills and knowledge that are useful for personal growth and development in machine learning

Builds a strong foundation for beginners in scaling scikit-learn solutions for machine learning

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 Scaling scikit-learn Solutions with these activities:

Review scikit-learn Foundation Concepts

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Solidify the understanding of fundamental scikit-learn concepts and machine learning principles.

Browse courses on scikit-learn

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Review scikit-learn documentation and tutorials.
Revisit key machine learning algorithms and their implementation in scikit-learn.

Collaborate on a Scikit-Learn Scaling Project

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Enhance understanding and practical application of scikit-learn scaling techniques by collaborating with peers on a hands-on project.

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Form a study group with peers.
Choose a real-world dataset and define scaling goals.
Apply scikit-learn scaling techniques and evaluate results.
Discuss findings, compare approaches, and refine solutions.

Classify Prediction Latency and Throughput Factors

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Identify and categorize different aspects that impact the speed of prediction and data processing within scikit-learn models.

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Create a list of factors influencing prediction latency and throughput.
Define and provide examples for each factor.
Categorize the factors based on their impact.

Four other activities

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Showcase Scikit-Learn Scaling Techniques in a Presentation

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Develop presentation skills while reinforcing scikit-learn scaling knowledge by creating and delivering a presentation on the techniques learned.

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Choose a topic related to scikit-learn scaling.
Research and gather relevant information about the topic.
Create presentation slides using visuals, examples, and clear explanations.
Rehearse and deliver the presentation to an audience.

Boost Model Scalability via Feature Representation

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Explore techniques for representing features and handling sparse data to enhance the scalability of scikit-learn models.

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Discuss different feature representation methods.
Explain the impact of sparse data on model performance.
Implement feature representation techniques to improve scalability.

Parallelize Key Tasks in scikit-learn

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Implement multicore parallelism techniques to speed up key tasks in scikit-learn, such as cross-validation, hyperparameter tuning, and ensemble learning.

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Identify the key tasks to parallelize in scikit-learn.
Research and choose appropriate multicore parallelism techniques.
Implement multicore parallelism for the identified tasks.
Evaluate the performance improvements achieved through parallelization.

Develop an Incremental Learning Implementation

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Implement incremental learning algorithms using scikit-learn estimators to enable out-of-core learning and handle large datasets.

Browse courses on Incremental Learning

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Research incremental learning algorithms and their benefits.
Select appropriate scikit-learn estimators for incremental learning.
Develop an implementation using the selected estimators.
Test and evaluate the implementation on a real-world dataset.

Career center

Learners who complete Scaling scikit-learn Solutions will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data to uncover patterns and trends. With the skills and knowledge gained from this course, you'll be well-equipped to implement scalable machine learning models to handle large datasets efficiently, a valuable skill for Data Scientists. Understanding how to scale models can help you build more efficient and effective solutions, leading to success in this role.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. By taking this course, you'll gain the expertise to scale machine learning models effectively, enabling you to handle large-scale data and improve model performance. The course covers techniques like out-of-core learning and multicore parallelism, which are essential for Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers design, develop, and maintain software systems. This course provides valuable knowledge for Software Engineers who want to specialize in building scalable machine learning applications. You'll learn how to optimize model performance and handle large datasets, making you a more competitive candidate in this field.

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Data Analyst

Data Analysts gather, clean, and analyze data to identify trends and patterns. This course can enhance your skills by providing techniques to handle large datasets and improve model performance, making you a more valuable asset as a Data Analyst.

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Research Scientist

Research Scientists conduct research to advance scientific knowledge. This course provides a foundation for Research Scientists who want to specialize in machine learning. You'll gain the skills to scale models to handle large datasets and improve performance, which is crucial in research.

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Business Analyst

Business Analysts identify and solve business problems using data analysis and modeling. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to make more informed decisions and deliver better solutions as a Business Analyst.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can be helpful by providing techniques to handle large datasets and improve model performance, allowing you to build more accurate and reliable financial models.

See salaries and explore the career path for Quantitative Analyst

Statistician

Statisticians collect, analyze, and interpret data to provide insights. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to conduct more comprehensive and accurate statistical analyses.

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Data Engineer

Data Engineers design and build data pipelines and infrastructure. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to build more efficient and scalable data pipelines.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical methods to solve complex business problems. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to develop more effective solutions.

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Risk Analyst

Risk Analysts identify and assess risks for organizations. This course can be useful by providing techniques to handle large datasets and improve model performance, allowing you to build more accurate and comprehensive risk models.

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Actuary

Actuaries use mathematical and statistical methods to assess and manage financial risks. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to build more accurate and reliable actuarial models.

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Financial Analyst

Financial Analysts analyze financial data to make investment recommendations. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to build more accurate and reliable financial models.

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Economist

Economists study the production, distribution, and consumption of goods and services. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to conduct more comprehensive and accurate economic analyses.

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Market Researcher

Market Researchers study consumer behavior to understand market trends. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to conduct more comprehensive and accurate market research studies.

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