Part 4: MLOps & AI Frameworks

The convergence of DevOps practices with machine learning operations represents one of the most dynamic areas in modern technology. In this section, we address questions about transitioning to MLOps roles and navigating the rapidly evolving landscape of AI frameworks.

Q11. How about moving from DevOps from MLOps? How far Kodekloud is helping on that?

KodeKloud is significantly expanding our MLOps curriculum to support professionals transitioning from traditional DevOps to ML-focused operations. We've already launched our MLOps Fundamentals course, which provides essential background on the unique challenges of operationalizing machine learning workflows.

Our expanded offerings include hands-on courses for Langchain, PyTorch, and prompt engineering - critical skills as organizations implement generative AI capabilities. MLOps represents a natural extension of DevOps principles, applying automated pipelines, infrastructure as code, and operational best practices to the machine learning lifecycle.

We recognize MLOps as a key growth area alongside cloud, containerization, and platform engineering. By leveraging your existing DevOps expertise while building specialized ML pipeline knowledge, you'll be well-positioned for this evolving space. I encourage you to explore our current MLOps offerings while we continue expanding our curriculum in this rapidly developing field.

Q12. Do we plan to have a CI/CD course on end to end for Model deployments?

We're developing comprehensive ML deployment content focused on practical implementation strategies. Currently, most organizations leverage managed services like AWS SageMaker, Azure ML, or Google's Vertex AI for model deployment, or they integrate with API-based services like OpenAI and Anthropic.

End-to-end model deployments on custom infrastructure remain specialized, typically limited to organizations with specific security requirements or performance needs. However, this is evolving rapidly with improved GPU accessibility and specialized ML infrastructure.

We're particularly excited about our collaboration with NVIDIA's certification programs. We're currently facilitating a Discord study group for their foundational certifications, and beginning in April, we'll expand our curriculum to include NVIDIA's infrastructure certifications. These courses will cover the full spectrum of model deployment, including infrastructure requirements, ML operations, and deployment strategies across NVIDIA's ecosystem.

As this field matures, we'll continue expanding our offerings to address the growing need for specialized ML deployment expertise.

Q13. AI related frameworks are continuously growing langchain, langgraph. Any plans on doing that?

We're actively developing our AI framework curriculum, with Langchain and PyTorch courses already available. We're closely monitoring the rapid evolution in this space, including Langchain, LangGraph, and foundational technologies like Pydantic.

While building our curriculum, we're carefully evaluating which frameworks demonstrate production reliability rather than just development convenience. For instance, we've observed community feedback regarding Langchain's production stability challenges, so we're assessing alternative approaches that might provide more robust solutions for enterprise implementation.

We're also determining the optimal balance between data science aspects and operational DevOps considerations in our courses. Your feedback is crucial in shaping our roadmap - I encourage you to visit our feedback page and vote for specific AI framework courses you'd find valuable. We prioritize our development based on community demand and are committed to providing relevant, practical content for production AI implementation.

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