▸ Data Quality is a key 🔑 2. Model ▸ Machine Learning Pipelines ▸ Train, Evaluate, Test 3. Software ▸ Model Serving & Predictions ▸ Deployment strategies & Infra 🐳☁
and design self-running software to automate predictive models. An ML Engineer builds artificial intelligence (AI) systems that leverage huge data sets to generate and develop algorithms capable of learning and eventually making predictions. - Brainstation.io ML Engineer Machine Learning Engineer คือ นักพัฒนาโปรแกรมที่วิจัย สราง และออกแบบ ซอฟตแวรที่สามารถรันโมเดลทำนายผลได และสรางระบบ AI โดยใชประโยชนจากขอมูลจำนวนมาก
tools work with DS / AI Automate the process of model training using pipelines and tools. (MLOps) Deploy ML model in production & monitoring Productionize the ML models including model versioning and optimization. Online prediction / API High performance model serving with low latency and high availability. Monitor the model prediction and performance for improvement. Integrate ML into application & monitoring
Deployment A/B Testing Data-Centric AI vs Model-Centric AI Monitoring Healthcheck Testing Data Version Control Experiment Tracking Data Lineage Lifecycle Automation Data Quality Data Labeling Data Augmentation Train & Evaluate Model Versioning Model Serving & Deployment Optimization Error Reporting
กอนตัดสินใจใช tool ที่เหมาะสม ⚒ 🎯 Pricing 💲: บาง tool มีคาใชจายคอนขางสูง 💸 ตองดูความคุมคาระยะยาว 🎯 Over-engineering : Perfect is the enemy of good xkcd by Randall Munroe. Automation takes a life of its own.
engineer you are, not like the great machine learning expert you aren’t. Martin Zinkevich https://developers.google.com/machine-learning/guides/rules-of-ml