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AIOps vs LLMOps vs MLOps. I’ve explained each approach in simple steps below. 𝗔𝗜𝗢𝗣𝗦 AIOps applies AI to monitor, detect, and fix problems in IT systems. 1. Decide what issue you want AI to solve (like preventing system crashes). 2. Collect logs, metrics, and alerts from servers, apps, or networks. 3. Review the raw signals that show system health and performance. 4. Organize and structure the raw data so AI can understand it. 5. Remove errors, duplicates, or inconsistent entries. 6. Select AI monitoring and automation platforms. 7. Create models that can recognize unusual activity or performance issues. 8. AI identifies any abnormal behavior (like sudden traffic spikes). 9. Locate what caused the problem (server overload, failed update, etc.). 10. Let the system take small actions automatically (restart a service, balance load). 11. Implement the model in real systems and track its behavior. 12. Keep improving the model using new data and feedback. ____________________________________ 𝗟𝗟𝗠𝗢𝗣𝗦 LLMops is about using large language models (LLMs) reliably: preparing data, tuning prompts/models and monitoring outputs. 1. Define what you want your model to do (summarize text, write emails, etc.). 2. Pick the right large language model (e.g., GPT, Claude etc.). 3. Prepare training or example data for your use case. 4. Adjust the model using your data to improve relevance. 5. Craft and refine prompts that produce high-quality answers. 6. Connect the model with APIs, apps, or databases. 7. Evaluate model responses for accuracy and usefulness. 8. Ensure the model is correct, fair, and safe in responses. 9. Release the model for real users to interact with. 10. Track response quality, latency, and cost. 11. Watch for when the model starts producing less reliable results. 12. Update prompts, retrain, or fine-tune to keep quality high. ____________________________________ 𝗠𝗟𝗢𝗣𝗦 MLOps covers the full lifecycle of traditional ML models (data → model → deployment → maintenance). 1. Clearly state the problem (e.g., predict demand, classify images). 2. Gather both structured (tables) and unstructured (text, images) data. 3. Format and clean the data for training. 4. Remove wrong or missing values and fix inconsistencies. 5. Convert data into useful variables (like “average spending”). 6. Choose the right ML model (like Random Forest or Neural Network). 7. Teach the model using the data and tweak for accuracy. 8. Optimize hyperparameters (learning rate, depth, etc.). 9. Test results with unseen data to ensure reliability. 10. Push the trained model into a live environment. 11. Make the pipeline handle larger loads automatically. 12. Track performance and retrain when data or behavior changes. ✅ 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁: • 𝗔𝗜𝗢𝗽𝘀 = AI for automating IT operations. • 𝗟𝗟𝗠𝗢𝗽𝘀 = Managing large language models efficiently. • 𝗠𝗟𝗢𝗽𝘀 = Streamlining ML model lifecycles. ✅ Repost for others in your network who can benefit from this.