WalkSelf · AI, Tech & Engineering · Generative AI & Deep Learning

MLOps for Generative AI

Focus on the operational challenges of deploying, monitoring, and maintaining large-scale generative models. Learn about model versioning, performance tracking, cost management, and building robust production pipelines.

MLOps for Generative AI

LLMOps with OpenVINO: Converting Florence-2 Models to IR

Apache Tika for Data Pipelines and MLOps

OpenVINO and LLMOps: CPU Inference for Generative AI

MLOps Fundamentals with MLflow for Reproducible Machine Learning

MLOps for LLMs: Convert Phi3 to GGUF with LLaMA.cpp

MLOps: Scaling ML Inference with Distributed Messaging

MLOps for LLMs: Fine-tuning, Quantization, and Evaluation

Beginner MLOps: MLflow for BLIP Model Deployment

MLOps for Small Language Models with MLflow, GGUF, and ONNX

Build AI Solutions with ML.NET and C#

MLOps with MLflow, Databricks, and Optuna

MLOps Fundamentals with MLflow and Docker

MLOps and MLflow for Deploying Multiple LLMs

MLOps with MLflow and LangChain for LLM Applications

Fine-Tuning Mistral 7B with PEFT, QLoRA, and MLflow

LLMOps with OpenVINO: INT4 Quantization and CPU Inference

OpenVINO LLMOps: 4-Bit Model Quantization and CPU Inference

MLOps and Time Series Forecasting with MLflow and Prophet

Databricks MLOps and LLMOps with MLflow and OpenAI