Become an Intel® Certified Developer—MLOps Professional

Learn the skills, tools, and techniques to build and deploy performant AI systems. 

 

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Overview

Whether you are deploying an AI project into production or adding AI to an existing application, building a performant and scalable machine learning operations (MLOps) environment is crucial to maximizing your resources.

This MLOps Professional Training Package teaches you to incorporate compute awareness into the AI solution design process to maximize performance across the AI pipeline. Get access to video lessons, hands-on labs, Intel experts, and the Intel® Developer Cloud. The course content and labs prepare you for the credentialing exam where, upon passing, you are recognized as an Intel® Certified Developer—MLOps Professional.

Curriculum

The MLOps Professional Training Package equips you to: 

  • Design and build compute-aware AI solutions to maximize performance across the AI pipeline.
  • Apply MLOps best practices to implement critical components like model version control, distributed training, observability, and optimized deployments.
  • Identify opportunities to improve performance, quality, and user experience.
  • Prove your MLOps skills through a capstone project and professional certification exam. 

The MLOps course includes: 

  • 7 modules
  • 8 hands-on labs 
  • 1 capstone project with a live workshop 
  • Q&A with an Intel expert  
  • Access to a community of learners on Discord*
  • 1 practice certification exam  

Prerequisite Skills:

  • Proficient in Python*
  • Basic understanding of machine learning algorithms
  • Basic experience with programming IDEs (such as VSCode)
  • Linux* command line basics

Course Modules

  • Define the motivations and goals of AI applications.
  • Identify opportunities for material value through the implementation of AI in a business process.
  • Define how AI contributes to the architecture of the application design.
  • Identify opportunities for optimization in AI systems.

  • Describe the types of design patterns used in AI applications.
  • Identify when to use a design pattern based on the application workload or goal.

  • Design and implement an API using popular Python frameworks. 
  • Use HTTP protocols, and build and use REST APIs.
  • Define interactions between servers and clients in modern software applications.

  • Build software application architecture diagrams (inclusive of an appropriate software design pattern).
  • Audit an architecture diagram to implement an AI solution based on best practices. Define which parts are responsible for client/server interactions, and recognize the other critical components of application.
  • Design cloud-native AI applications based on Amazon Web Services (AWS)*.

  • Define the value and components of MLOps.
  • Implement data pipelines, model registries, observability and triggering, and version control.
  • Build MLOps components using popular open source frameworks (MLFlow and Kubeflow*).
  • Identify opportunities for MLOps components to improve performance, quality, and user experience in real-world applications.

  • Define the requirements of different AI workloads and how they impact application design.
  • Design applications for high-inference throughput.
  • Design applications for distributed training.
  • Activate hardware-level accelerations that includes Intel-optimized software.
  • Use OpenMP, numactl, and Intel® oneAPI Math Kernel Library (oneMKL) to optimize the use of underlying hardware.
  • Perform basic performance profiling of AI applications.
  • Identify the correct opportunities for using a general purpose compute system versus an accelerator.
  • Identify best practices for MLOps, hardware selection and workload management for the needs of a particular use case.

  •  Build a project during a live workshop where you practice and apply the concepts learned in previous modules.

 

Success Stories

Traiano G. Welcome

Cloud migration and implementation senior manager, Accenture*

"Few, if any, industry certifications are available for the rapidly growing field of MLOps and none provide suitable training on integrating MLOps tools and processes with features at the hardware level.

"Intel Certified Developer–MLOps Professional training not only serves as a ground-up introduction to machine learning and MLOps, but provides unique knowledge on using the advanced features of Intel® processors created specifically to boost machine learning workloads for competitive advantage.

"This course empowers architects to integrate hardware feature design into machine learning architectures and engineers to implement automation code capable of exploiting the AI/machine learning enhancements available in Intel processors."

Nitin Saraswat

Founder, Aiproff.ai

"Having attended the Intel oneAPI sessions, it was an easy decision for me to go for the MLOps Professional certification. The Intel Developer Cloud access and preparation material were super helpful in exam readiness and resulted in cracking the certification with flying colors. Model deployment, serving, and security are key aspects where even experienced machine learning engineers struggle. The certification bridges this gap by sharing lots of real-world interesting insights and use cases."

Bachu Venkata Thanush

Student, Vellore Institute of Technology, Andhra Pradesh (VIT-AP) 

"I found the Intel MLOps course to be highly valuable. One of the standout features was the introduction of new concepts, which greatly enhanced my understanding of the subject matter. The course's division into modules proved to be instrumental, facilitating easy access to specific topics for focused study. Additionally, the inclusion of hands-on labs for each module on the platform significantly contributed to an improved learning experience. Overall, I appreciate the comprehensive structure and resources provided by this course, enhancing my grasp of machine learning operations."

Ravi Mandal

Data analytics manager, GlaxoSmithKline (GSK)*

 "The training covered a wide range of topics related to MLOps, providing a solid foundation for integrating machine learning into production environments. The practical labs were invaluable for applying theoretical knowledge. They helped bridge the gap between concepts and real-world implementation. The instructors were well-versed in the subject matter and were able to answer complex questions, providing insights beyond the course material.

"Overall, the combination of theoretical knowledge, hands-on experience, Capstone project, and industry insights makes it a standout component of the overall program. I feel well-equipped to tackle the challenges of deploying and monitoring machine learning models in real-world scenarios."

Bharathi Athinarayanan

AI and machine learning evangelist and technologist, IQVIA India

"The course’s comprehensive and cutting-edge content was truly impressive. The way architectural patterns were elucidated through practical case studies facilitated a more tangible understanding of the concepts. Additionally, the information provided about the labs proved to be extremely valuable, enhancing the overall learning experience."

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Intel Employee Registration Instructions
 

Schedule Exam
$199 USD
Schedule the Professional Certification Exam through Pearson VUE.