MLOPS UPSKILLING PROGRAM

The popularity of Machine Learning Operations (MLOps) is on the rise, reflecting the growing necessity to seamlessly transition machine learning models from development to production.
This demand highlights a robust job market for MLOps professionals, promising a future where expertise in this domain is crucial for businesses aiming to harness AI effectively.
As industries increasingly rely on AI, those skilled in MLOps are positioned for a bright career path, marking MLOps not just as a trend, but as a critical component of the modern technological ecosystem.
Join us on an exciting journey into the world of Machine Learning Operations (MLOps) with Xenon7's Upskilling Program designed specifically for beginners and junior professionals who are looking to steering careers in this direction.
About the Program.
This program is tailored to equip aspiring MLOps professionals with the knowledge and skills needed to seamlessly transition from basic machine learning model building and training to mastering the operational aspects of deploying, managing, and monitoring models at scale.
It is an ideal pathway for those who wish to fast-track their careers in this dynamic and critical field of technology.
Program Highlights.
Tailored Curriculum.
Learn the fundamentals and advanced concepts of MLOps, starting from the basics.
Bsides the mandatory Circullum, we will provide additional resources for learners who want to go an extra mile and gain deeper knowledge on specific topics Hands-on Approach.
Dive into practical exercises, labs, and projects using industry-standard tools and platforms.
Guidance from Experts.
Gain insights from experienced professionals who have successfully implemented MLOps workflows.
Networking Opportunities.
Connect with peers and industry veterans to expand your professional network.
Certification of Completion.
Showcase your newly acquired skills with a certificate recognized by industry leaders.
Besides Xenon7's certificate, top Learning get a voucher for obtaining Intel's MLOps Professional Certification Job Opportunities.
Top learnings get a chance of landing a job with Xenon7 upon successful cokmpletion of the program Curriculum Overview.
Introduction to MLOps.
Overview of the ML Lifecycle and Deployment, How to Select and Train a Model, Data Definition and Baselining Solution Architecture and Design.
AI solution Architectures and Design Patterns with practical application in AWS environment, Designing and Building back-end APIs using Pyhon Building MLOps flow.
Implementing Data pipelines, Model Registries, Observability and Triggering, Versioning using MLFlow and Kuberflow Optimization of Full-stack.
Use OpenMP, numactl, and Intel® oneAPI Math Kernel Library (oneMKL) to optimize the use of underlying hardware.
Managing workload requirements, designing for high-inference throughput, distributed training, performance profiling, and other techniques.
Project Assignments.
Complete 3 real-world projects and apply learned skills Program Duration.
weeks, part-time, offering flexible learning options to fit your schedule.
(12 weeks of content and 4 weeks to complete assignmenets and certification) Location.
Fully online, with opportunities for live sessions, discussions, and networking events.
Application Process.
- To apply, submit your application on our website.
The process includes the submission of a resume, motivation letter, and questionary.
- Asynchronous video interview that will assess your level of understanding of machine learning concepts, problem-solving skills and motivation for advancing in the MLOps field.
Price.
$100 (we offer 5 scholarships for best applicants) More information on.
https://www.
enon7.
om/programs/mlops \n Graduates with a degree in Computer Science, Data Science, or a related field.
Individuals with 1-2 years of experience in machine learning, possessing a basic understanding of ML concepts, including building and training models.
Aspiring MLOps professionals seeking to deepen their expertise and contribute to the operational excellence of AI projects.