400+ Students
Design architectures that efficiently handle data pipelines and model inference at scale.
Implement versioning, monitoring, and automated deployment (CI/CD) for production models.
Develop both the backend API and the frontend application layer for an AI service.
Practical skills in deploying AI solutions on major cloud platforms.
Solid experience with Python, familiarity with Docker and Kubernetes concepts, and experience working with at least one major cloud platform (AWS, Azure, or GCP).
Developers who want to transition into building the infrastructure and application layer for AI products.
Professionals who want to move beyond model building into the production and deployment lifecycle.
Individuals focused on automating the deployment, scaling, and monitoring of AI systems
Senior team members responsible for defining the technical strategy for AI product development.
Participate in dynamic, hands-on sessions led by expert instructors to gain practical skills in a supportive learning environment.
Implement all stages of the MLOps lifecycle, from data handling to model monitoring, resulting in a scalable, deployed application.
You will complete a hands-on project that can be added to your professional portfolio to demonstrate your skills to potential employers.
Gain continuous learning with one year of unlimited access to all course materials, tools, and resources, allowing you to study at your own pace and revisit topics as needed.
Everything you need to know about our top rated course.
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