Learn how to operationalize ML models with new AWS course

A VentureBeat report notes that 87% of data science projects never make it to production. This isn’t surprising if we consider an International Data Corporation (IDC) survey that points to the lack of machine learning and operations (MLOps) skills as one of the top three challenges in operationalizing artificial intelligence (AI). 52% of the participants in a Forrester study believe that investing in MLOps will lead to better customer experiences.

What is MLOps?

MLOps is a practice similar to the development and operations (DevOps) practice. The DevOps practice bridges the coordination gap between software developers and operations with a set of tools, automations, processes, and teamwork leading to faster and more frequent code deployments to production.

MLOps is more challenging than DevOps for three reasons: 1) Unlike the DevOps practice, we need to coordinate work among four roles instead of two (i.e., data engineers and data scientists) in addition to software developers and operations; 2) in MLOps, we’re deploying both model and data in addition to code; and 3) due to the probabilistic nature of ML prediction, we need to monitor prediction quality continuously to ensure alignment with the business metrics.

Learning to address these three key challenges is critical for operationalizing ML models. If you’re a DevOps engineer, an ML engineer, a developer, or operations staff with the responsibility to operationalize ML models, our new three-day, virtual classroom course, MLOps Engineering on AWS, can help you develop the skills you need to be part of the shift from piloting to operationalizing ML models. ML engineers are one of the top 10 AI jobs in America with an average salary of $125,000 (USD), according to a 2021 report from Indeed.

About the course

MLOps Engineering on AWS is an intermediate-level course that will allow you to practice building, training, deploying, monitoring, and troubleshooting models in a hands-on environment with the help of expert AWS instructors. You’ll also learn to extend the DevOps concept of code pipelines to models by automating the build, train, and deploy stages of the ML workflow, which are critical to success in operationalizing ML models.

The course begins with a discussion of similarities and differences between MLOps and DevOps, helping you understand the need for increased collaboration and communication in practicing MLOps. You’ll also explore different tools for ML workflow automation. Our accredited AWS instructors will guide you through different deployment strategies to minimize risk. You’ll also learn to monitor resource consumption and latency in a deployed model in addition to monitoring for alignment of the model’s prediction with business requirements.

Along the way, you’ll reflect on lesson and lab content and complete an MLOps action plan that you can evolve and implement in your organization.

Learners interested in registering for this course are expected to have one or more years of DevOps experience and foundational knowledge of ML on AWS. You can also satisfy this requirement by completing the DevOps Engineering on AWS course and the Practical Data Science with Amazon SageMaker course.

Whether you attend the class virtually or in-person, you’ll have the opportunity to ask questions, work through solutions, and get real-time feedback from accredited AWS instructors with deep technical knowledge.

Is the AWS Certified Machine Learning – Specialty your goal?

If you want to earn an industry-recognized credential from AWS that validates your expertise in AWS ML services, you may want to consider the AWS Certified Machine Learning – Specialty certification. While the MLOps Engineering on AWS course explores deploying and operationalizing ML solutions topics, take advantage of additional information to help you prepare for the AWS Certified Machine Learning – Specialty exam.

What resources are available if I want to learn more?

If you’re interested in learning more about our AWS Training and Certification offerings for ML, download our AWS Machine Learning Ramp-Up Guide. You can also explore our library of ML courses. We offer many free, on-demand digital resources as well as several virtual instructor-led courses.