The following advice I give to others considering using AI and ML Development is to master the fundamentals before jumping to frameworks. Understanding linear algebra, statistics, and core machine learning algorithms makes you a significantly better ML engineer than someone who only knows how to call library functions. Use experiment tracking from day one. Implement MLflow or Weights & Biases from your very first experiment, not after things get complex. Retrofitting experiment tracking is painful. Build RAG before fine-tuning. For most LLM use cases, RAG delivers better results faster and cheaper than fine-tuning, so try RAG first. Monitor production models continuously. Model deployment is not the finish line. Model drift, data distribution shifts, and performance degradation are real production challenges. Containerize everything with Docker. Environment inconsistencies kill projects. Using Docker from the start saves enormous debugging time. Finally, contribute to open source. AI and ML Development community thrives on open-source collaboration. Contributing even small improvements builds reputation and network simultaneously. I rate my overall experience with AI and ML Development as an eight out of ten.
The following advice I give to others considering using AI and ML Development is to master the fundamentals before jumping to frameworks. Understanding linear algebra, statistics, and core machine learning algorithms makes you a significantly better ML engineer than someone who only knows how to call library functions. Use experiment tracking from day one. Implement MLflow or Weights & Biases from your very first experiment, not after things get complex. Retrofitting experiment tracking is painful. Build RAG before fine-tuning. For most LLM use cases, RAG delivers better results faster and cheaper than fine-tuning, so try RAG first. Monitor production models continuously. Model deployment is not the finish line. Model drift, data distribution shifts, and performance degradation are real production challenges. Containerize everything with Docker. Environment inconsistencies kill projects. Using Docker from the start saves enormous debugging time. Finally, contribute to open source. AI and ML Development community thrives on open-source collaboration. Contributing even small improvements builds reputation and network simultaneously. I rate my overall experience with AI and ML Development as an eight out of ten.