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Bradford Tuckfield
Enhance your skills with our Operational Machine Learning Training Course using SageMaker. Enroll today and learn advanced machine learning skills with AWS.
Register for this course and see more details by visiting:
OpenCourser.com/course/so02m9/operationalizing
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What's inside
Syllabus
In this introductory lesson, we will give you a course overview of topics and design. We will also introduce what exactly operationalizing machine learning means as well as how it applies.
Read more
Syllabus
In this introductory lesson, we will give you a course overview of topics and design. We will also introduce what exactly operationalizing machine learning means as well as how it applies.
Read more
This lesson is about managing computing resources effectively. We’ll talk about lowering costs and getting more with less.
This lesson is about training models on large datasets. We’ll talk about distributed models, distributed data, and some skills related to distributed training.
This lesson is about high throughput, low latency models. Essentially, this means that we’ll be talking about preparing your projects to deal with high traffic and minimal time delays.
Our final lesson is about security. Security is crucial for all major machine learning projects, so these skills can be very helpful in your career.
Your goal in this project will be to use several important tools and features of AWS to adjust, improve, configure, and prepare the model you started with for production-grade deployment.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers essential skills for machine learning, such as managing resources, training models on big datasets, and ensuring high throughput and low latency
Strong for learners who want to gain practical experience in deploying machine learning models using AWS SageMaker
Instructors Bradford Tuckfield are recognized for their expertise in operational machine learning
Recommended for experienced machine learning practitioners who want to enhance their skills in operationalizing machine learning models
May require learners to have some prior experience with machine learning and AWS services
Does not cover some advanced topics in operational machine learning, such as MLOps and monitoring
Save this course
Save Operationalizing Machine Learning on SageMaker to your list so you can find it easily later:
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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 Operationalizing Machine Learning on SageMaker with these
activities:
Review fundamentals of AWS infrastructure
Show steps
Reinforce your understanding of the basics of AWS and cloud computing before starting this course.
Show steps
-
Rewatch AWS introductory videos
-
Revise AWS documentation
-
Recreate basic AWS architecture using a toy dataset
Seek mentorship from experienced practitioners
Show steps
Accelerate your learning and gain valuable insights by connecting with experienced professionals in the field of operational machine learning.
Show steps
-
Identify potential mentors through online platforms or professional networks
-
Reach out to mentors and express your interest
-
Establish regular check-ins or meetings
Organize course notes and resources
Show steps
Stay organized and maximize your learning by compiling all relevant course notes, assignments, and resources.
Show steps
-
Review notes from each class
-
Organize materials into a single folder or notebook
-
Add notes or annotations to clarify concepts
Five other activities
Expand to see all activities and additional details
Show all eight activities
Hands-on practice with SageMaker
Show steps
Develop practical skills in using SageMaker by completing hands-on exercises and projects.
Show steps
-
Walk through interactive SageMaker tutorials
-
Set up a SageMaker instance
-
Build and deploy a simple machine learning model using SageMaker
Discuss course concepts with peers
Show steps
Reinforce your understanding and gain different perspectives by discussing course concepts with your fellow students.
Show steps
-
Form a study group
-
Choose a topic to discuss
-
Prepare talking points
-
Engage in respectful dialogue and exchange ideas
Explain operational machine learning process
Show steps
Solidify your understanding of the operational machine learning process by creating a presentation or document outlining the steps involved.
Show steps
-
Identify the different stages of operational machine learning
-
Describe the tools and techniques used in each stage
-
Create a diagram or infographic to illustrate the process
Contribute to open source projects related to operational machine learning
Show steps
Gain hands-on experience and enhance your understanding by contributing to open source projects in the field of operational machine learning.
Show steps
-
Identify open source projects related to SageMaker or operational machine learning
-
Choose a project to contribute to
-
Read the project documentation and familiarize yourself with the codebase
-
Make a pull request with your contribution
Deploy a production-grade model
Show steps
Gain practical experience in deploying a machine learning model for production use.
Show steps
-
Choose a suitable deployment platform
-
Configure the deployment environment
-
Deploy the model
-
Monitor and maintain the deployed model
Review fundamentals of AWS infrastructure
Show steps
Reinforce your understanding of the basics of AWS and cloud computing before starting this course.
Show steps
Show steps
Show steps
- Rewatch AWS introductory videos
- Revise AWS documentation
- Recreate basic AWS architecture using a toy dataset
Seek mentorship from experienced practitioners
Show steps
Accelerate your learning and gain valuable insights by connecting with experienced professionals in the field of operational machine learning.
Show steps
Show steps
Show steps
- Identify potential mentors through online platforms or professional networks
- Reach out to mentors and express your interest
- Establish regular check-ins or meetings
Organize course notes and resources
Show steps
Stay organized and maximize your learning by compiling all relevant course notes, assignments, and resources.
Show steps
Show steps
Show steps
- Review notes from each class
- Organize materials into a single folder or notebook
- Add notes or annotations to clarify concepts
Five other activities
Expand to see all activities and additional details
Show all eight activities
Hands-on practice with SageMaker
Show steps
Develop practical skills in using SageMaker by completing hands-on exercises and projects.
Show steps
Show steps
Show steps
- Walk through interactive SageMaker tutorials
- Set up a SageMaker instance
- Build and deploy a simple machine learning model using SageMaker
Discuss course concepts with peers
Show steps
Reinforce your understanding and gain different perspectives by discussing course concepts with your fellow students.
Show steps
Show steps
Show steps
- Form a study group
- Choose a topic to discuss
- Prepare talking points
- Engage in respectful dialogue and exchange ideas
Explain operational machine learning process
Show steps
Solidify your understanding of the operational machine learning process by creating a presentation or document outlining the steps involved.
Show steps
Show steps
Show steps
- Identify the different stages of operational machine learning
- Describe the tools and techniques used in each stage
- Create a diagram or infographic to illustrate the process
Contribute to open source projects related to operational machine learning
Show steps
Gain hands-on experience and enhance your understanding by contributing to open source projects in the field of operational machine learning.
Show steps
Show steps
Show steps
- Identify open source projects related to SageMaker or operational machine learning
- Choose a project to contribute to
- Read the project documentation and familiarize yourself with the codebase
- Make a pull request with your contribution
Deploy a production-grade model
Show steps
Gain practical experience in deploying a machine learning model for production use.
Show steps
Show steps
Show steps
- Choose a suitable deployment platform
- Configure the deployment environment
- Deploy the model
- Monitor and maintain the deployed model
Career center
Learners who complete Operationalizing Machine Learning on SageMaker will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning models. A stepping stone to this job is an ample foundation in core machine learning principles, experience which this course develops. Enrolling in this course "Operationalizing Machine Learning on SageMaker" will allow you to build the ML engineering foundation which employers seek.
Data Scientist
Data Scientist
Data Scientists solve problems with data and machine learning. To do this, they need to know the fundamentals of machine learning, which are taught in this course. "Operationalizing Machine Learning on SageMaker" introduces the key concepts and skills necessary for a foundation in the field of data science.
Software Engineer (Machine Learning)
Software Engineer (Machine Learning)
Software Engineers who specialize in Machine Learning build and maintain software systems that use machine learning. This course provides foundational knowledge and practical skills in operationalizing machine learning models which are valuable for Software Engineers specializing in Machine Learning.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts (Quants) use mathematical and statistical models to analyze data and make predictions. Foundational knowledge in machine learning and practical skills in operationalizing ML models is increasingly important for Quants. "Operationalizing Machine Learning on SageMaker" can help Quants build the skills needed to succeed.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that supports data science and machine learning. This course, "Operationalizing Machine Learning on SageMaker", can be a helpful introduction to machine learning concepts and skills for those interested in a career as a Data Engineer.
Product Manager (Machine Learning)
Product Manager (Machine Learning)
Product Managers specializing in Machine Learning are responsible for the development and launch of machine learning products. This course, "Operationalizing Machine Learning on SageMaker", provides a helpful introduction to the practical aspects of bringing machine learning models to production.
Business Analyst
Business Analyst
Business Analysts help businesses make informed decisions by analyzing data and identifying trends. Machine learning skills are increasingly important for Business Analysts, and "Operationalizing Machine Learning on SageMaker" provides a basic introduction to these skills.
Statistician
Statistician
Statisticians use data to solve problems and make predictions. Machine learning is a powerful tool for statisticians, and "Operationalizing Machine Learning on SageMaker" can help build the skills needed to apply machine learning to statistical problems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to improve the efficiency of systems. This course may be a useful addition to the skillset of an Operations Research Analyst, as it provides practical experience in operationalizing machine learning models.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. Machine learning is increasingly used in the insurance industry to assess risk, and "Operationalizing Machine Learning on SageMaker" can be a helpful introduction to these techniques for Actuaries.
Financial Analyst
Financial Analyst
Financial Analysts use data to make recommendations on investments. Machine learning is increasingly used in the financial industry to analyze data and make predictions, and "Operationalizing Machine Learning on SageMaker" can provide a helpful introduction to these techniques.
Risk Manager
Risk Manager
Risk Managers identify and assess risks to an organization. Machine learning can be a powerful tool for Risk Managers, and "Operationalizing Machine Learning on SageMaker" can provide a helpful introduction to these techniques.
Data Architect
Data Architect
Data Architects design and manage the architecture of data systems. Foundational knowledge in machine learning and operationalizing ML models is helpful to have in this role. This course "Operationalizing Machine Learning on SageMaker" can be a useful introduction to these concepts.
IT Manager
IT Manager
IT Managers plan and oversee the implementation of IT systems within an organization. Foundational knowledge in machine learning and operationalizing ML models is helpful to have in this role. This course "Operationalizing Machine Learning on SageMaker" can be a useful introduction to these concepts.
Project Manager
Project Manager
Project Managers plan and execute projects, ensuring that they are completed on time, within budget, and to the required standards. Project Managers in the technology sector may find some value from this course as foundational knowledge in machine learning is helpful in understanding the work of data scientists and machine learning engineers.
For more career information including salaries, visit:
OpenCourser.com/course/so02m9/operationalizing
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
Operationalizing Machine Learning on SageMaker.
Focuses on the practical aspects of data science using AWS cloud services. It provides step-by-step instructions on building and deploying machine learning models on AWS, complementing the hands-on training offered in this course.
Offers a comprehensive introduction to machine learning using Python. It covers a wide range of topics, from data preprocessing to model evaluation, providing a solid foundation for learners new to machine learning.
Offers a comprehensive guide to machine learning with Python, covering libraries such as Scikit-Learn, Keras, and TensorFlow. It serves as a valuable resource for learners who want to expand their practical skills beyond what is covered in this course.
Save
Covers the challenges and best practices involved in designing data-intensive applications. It provides valuable insights for learners who want to build robust and scalable machine learning systems.
Provides a Bayesian perspective on machine learning, offering a different approach to understanding and applying machine learning techniques. It is recommended for learners interested in exploring alternative approaches to machine learning.
Save
Provides a comprehensive overview of cloud computing concepts and technologies. It is recommended as background reading for learners who want to understand the underlying infrastructure and services used in cloud-based machine learning.
Covers the mathematical concepts and techniques essential for understanding machine learning algorithms. It is recommended as background reading for learners who want to develop a strong foundation in the mathematical underpinnings of machine learning.
Although this course does not focus on R, this book offers advanced techniques for predictive modeling using R. It can provide additional insights for learners familiar with R who want to enhance their machine learning skills.
While this course primarily covers operationalizing machine learning, this book provides a solid foundation in deep learning concepts and techniques. It is recommended as additional reading for those interested in exploring the theoretical underpinnings of deep learning models.
Provides a rigorous mathematical treatment of machine learning from a probabilistic perspective. It is recommended for learners with a strong mathematical background who want to deepen their understanding of the theoretical foundations of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/so02m9/operationalizing
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Effort
4 weeks
Via
Udacity
Instructor
Bradford Tuckfield
Language
English
Good to know
Covers essential skills for machine learning, such as managing resources, training models on big datasets, and ensuring high throughput and low latency
Strong for learners who want to gain practical experience in deploying machine learning models using AWS SageMaker
Instructors Bradford Tuckfield are recognized for their expertise in operational machine learning
Recommended for experienced machine learning practitioners who want to enhance their skills in operationalizing machine learning models
May require learners to have some prior experience with machine learning and AWS services
Does not cover some advanced topics in operational machine learning, such as MLOps and monitoring
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