OVERVIEW:-

An MLOps course provides a comprehensive exploration of foundational concepts essential for deploying machine learning models in real-world scenarios. Participants delve into effective data management strategies tailored for MLOps environments, ensuring robustness and reliability throughout the model lifecycle. The course emphasizes integrating both traditional machine learning algorithms and advanced deep learning techniques within MLOps frameworks, fostering a holistic understanding of model deployment and optimization.
Hands-on exercises using Google Cloud Platform equip learners with practical skills in setting up scalable and efficient machine learning workflows. They gain proficiency in leveraging cloud-based services for model training, deployment automation, and monitoring, essential for maintaining peak performance in production environments. Emerging technologies like Generative AI are also explored, with practical applications using tools such as Gemma, offering insights into cutting-edge developments transforming the AI landscape.
Moreover, the course addresses MLOps technologies designed to support scalable machine learning deployments. By examining these technologies, participants gain a deeper understanding of optimizing model scalability and operational efficiency. The course concludes with a forward-looking discussion on the evolving landscape of MLOps, highlighting trends, challenges, and future directions shaping the field’s role in advancing AI applications across industries.

Exam Included:
NO exam is included in the Fees.

Prerequisite:
No Prerequisite. Python basics will be added benifit.

MLOps

Introduction to MLOps
  • MLOps Fundamentals
  • The Machine Learning Lifecycle
Data Management for MLOps
  • The Value of Data
  • Data Accessibility and Management
Foundation for MLOps: Machine Learning Concepts
  • AI and ML Fundamentals
  • Mathematics and Data Science
Machine Learning with Google Cloud
  • Google Cloud AI and ML Solutions
    • Vertex AI
    • AutoML
    • Integration with MLOps workflows
    • Automated model training
    • Automated model deployment
Traditional Machine Learning and MLOps Integration (Hands On)
  • Supervised Learning with MLOps Integration
  • Unsupervised Learning with MLOps Integration
Deep Learning with MLOps Integration
  • Introduction to Deep Learning
  • Deep Learning with MLOps Integration
Introduction to Generative AI and Gemma
  • What is Generative AI (GenAI)?
  • Gemma and its Capabilities:
Generative AI with Gemma (Hands On)
  • Generative Adversarial Networks (GANs) with Gemma
MLOps Technologies for Scalable Machine Learning
  • What is KubeRay and its Role?
  • MLOps with Kubeflow:
Conclusion and Future of MLOps
  • Course Summary
  • Future Trends and Advancements in MLOps
 

Training Partners

95 percent Of students say they are better prepared to maximize the value of Red Hat products after training

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