Future Directions in MLOps
Emerging trends and speculative advancements.
Chapter 61: Neurosymbolic MLOps Applications: Knowledge-driven ML, explainable AI Challenges: Scalability, integration Chapter 62: Quantum MLOps Quantum ML algorithms Hybrid classical-quantum pipelines Tools: Qiskit, PennyLane Chapter 63: Sustainable MLOps Carbon footprint of training Energy-efficient inference Green AI initiatives Chapter 64: General AI Operations Speculative: Self-improving models, autonomous pipelines Ethical considerations