Optimize ML Models and Deploy Human-in-the-Loop Pipelines from Coursera

In the third course of the Practical Data Science Specialization, you will learn a series of performance-improvement and cost-reduction techniques to automatically tune model accuracy, compare prediction performance, and generate new training data with human intelligence. After tuning your text classifier using Amazon SageMaker Hyper-parameter Tuning (HPT), you will deploy two model candidates into an A/B test to compare their real-time prediction performance and automatically scale the winning model using Amazon SageMaker Hosting. Lastly, you will set up a human-in-the-loop pipeline to fix misclassified predictions and generate new training data using Amazon Augmented AI and Amazon SageMaker Ground Truth. Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources. One of the biggest benefits of developing and running data science projects in the cloud is the agility and elasticity that the cloud offers to scale up and out at a minimum cost. The Practical Data Science Specialization helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages and want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud.

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Develops practical skills to effectively deploy data science projects in AWS cloud

Leverages industry-standard Amazon SageMaker

Series of courses provide comprehensive learning

Hands-on labs and interactive materials enhance learning experience

Taught by experienced instructors Antje Barth, Shelbee Eigenbrode, Sireesha Muppala, and Chris Fregly

Reviews summary

Practical supervised ml and human-in-the-loop deployment

For learners at the advanced level, this course covers how to optimize and deploy machine learning models and to create pipelines for human-in-the-loop deployments. While some students described the course as 'amazing', others highlighted that it contained too much structure and hand-holding. Overall, those who enjoyed the course emphasized the informative and well-organized content. Those who found the course less valuable emphasized that most of the coding was already written, making it more of an intro-level course.

This course assumes prior proficiency in AWS and focuses on using AWS for ML deployment.

"The course is good for the person who wants to use AWS to deploy or manage his ML models. it is basically bringing the model to AWS. thats it and how can we use AWS features for that."

This course includes labs and exercises to supplement the learning experience.

"Well organized. Videos correlate well with programming exercises."

"Perfect. The vocareum labs are very helpful..."

This course prepares learners to build and manage human-in-the-loop deployments.

"In this course I learn about... deploying and monitoring Models in AWS. The ideas about Human-in-the-loop pipelines is pretty cool."

Some learners found the code structure too restrictive, while others appreciated the more structured approach.

"Coding exercises are a bit too structured, there isn't as much learning as I would have liked..."

"... the students should be asked to write their own SQL/pandas/python code..."

Some learners found the course material well-organized, while others found it lacking in explanation.

"... the assignment is not that hard and hope could have more explanation."

"Introduce the ML workflow nicely,..."

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 Optimize ML Models and Deploy Human-in-the-Loop Pipelines with these activities:

Read "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow"

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Gain a comprehensive understanding of machine learning techniques and algorithms by reading this introductory book that covers key concepts and practical applications.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read chapters relevant to the course material
Work through the code examples provided in the book

Review basic machine learning concepts

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Refresh your understanding of fundamental machine learning concepts such as supervised and unsupervised learning to better grasp the course material.

Browse courses on Machine Learning

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Re-read notes from a previous machine learning course or book
Watch online tutorials or videos on machine learning basics
Solve practice problems or coding exercises related to machine learning

Follow tutorials on Amazon SageMaker

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Familiarize yourself with Amazon SageMaker's capabilities by following guided tutorials that provide hands-on experience with the platform.

Browse courses on Amazon SageMaker

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Complete the SageMaker Getting Started tutorial
Explore other tutorials relevant to the course content

Four other activities

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Join a study group or online forum

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Connect with other students or professionals to discuss course concepts, share ideas, and provide support.

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Find a study group or online forum related to the course
Participate in discussions and ask questions

Practice tuning ML models

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Enhance your ability to tune ML models by practicing with different techniques to improve model accuracy.

Browse courses on Model Tuning

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Use Amazon SageMaker Hyperparameter Tuning to tune a provided model
Experiment with different hyperparameter settings and evaluate the results
Apply regularization techniques to prevent overfitting
Perform cross-validation to assess model performance

Develop a case study on model comparison

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Solidify your understanding of model comparison by creating a case study that evaluates the performance of different models on a real-world dataset.

Browse courses on Model Comparison

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Choose a dataset and define the problem statement
Build and train multiple models using different algorithms or techniques
Deploy the models and perform A/B testing to compare their performance
Analyze the results and identify the best model for the given task

Build an end-to-end ML pipeline

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Deepen your understanding of the ML workflow by building a complete ML pipeline that covers data preprocessing, model training, and deployment.

Browse courses on Machine Learning Pipeline

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Collect and preprocess a dataset
Choose and train an ML model
Deploy the model using Amazon SageMaker Hosting
Monitor and evaluate the performance of the deployed model

Career center

Learners who complete Optimize ML Models and Deploy Human-in-the-Loop Pipelines will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer builds and deploys machine learning models to solve real-world problems. Machine Learning Engineers may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for Machine Learning Engineers who want to improve the performance of their machine learning models and deploy them into production.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist uses applied mathematics and statistics to extract knowledge and insights from data. Data Scientists may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for Data Scientists who want to improve the performance of their machine learning models.

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Data Architect

A Data Architect designs and builds data warehouses and data marts. Data Architects may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for Data Architects who want to improve the performance of their machine learning models.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. Software Engineers may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for Software Engineers who want to improve the performance of their machine learning models.

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Data Analyst

A Data Analyst collects, analyzes, and interprets data to help organizations make informed decisions. Data Analysts may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for Data Analysts who want to improve the performance of their machine learning models.

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Database Administrator

A Database Administrator manages and maintains databases. Database Administrators may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for Database Administrators who want to improve the performance of their machine learning models.

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Data Engineer

A Data Engineer designs and builds data pipelines. Data Engineers may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for Data Engineers who want to improve the performance of their machine learning models.

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Cloud Architect

A Cloud Architect designs and builds cloud computing solutions. Cloud Architects may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for Cloud Architects who want to improve the performance of their machine learning models.

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Quantitative Analyst

A Quantitative Analyst uses mathematics and statistics to analyze financial data. Quantitative Analysts may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for Quantitative Analysts who want to improve the performance of their machine learning models.

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Business Intelligence Analyst

A Business Intelligence Analyst uses data to help organizations make better decisions. Business Intelligence Analysts may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for Business Intelligence Analysts who want to improve the performance of their machine learning models.

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DevOps Engineer

A DevOps Engineer is responsible for the development and deployment of software applications. DevOps Engineers may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for DevOps Engineers who want to improve the performance of their machine learning models.

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Operations Research Analyst

An Operations Research Analyst uses mathematics and statistics to solve business problems. Operations Research Analysts may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for Operations Research Analysts who want to improve the performance of their machine learning models.

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Product Manager

A Product Manager is responsible for the development and launch of new products. Product Managers may work in a variety of fields, including finance, marketing, healthcare, and manufacturing. The Optimize ML Models and Deploy Human-in-the-Loop Pipelines course can be useful for Product Managers who want to improve the performance of their machine learning models.

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