On a scale of one to 10, I would rate AI and ML Development an eight to nine. I choose eight to nine out of 10 because there have been some issues. Regarding accuracy and reliability of output, I find that accuracy is good, and the output is also good, as 95 percent of the time it gives the correct approach and output. I generally trust the results, as 95 percent of cases show the output is correct, but for the other 5 percent, I find that the output is good and meets my expectations. AI and ML Development is deployed in my organization using on-premises resources within the on-premises structure. I rated AI and ML Development a 9 out of 10 overall.
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.
On a scale of one to 10, I would rate AI and ML Development an eight to nine. I choose eight to nine out of 10 because there have been some issues. Regarding accuracy and reliability of output, I find that accuracy is good, and the output is also good, as 95 percent of the time it gives the correct approach and output. I generally trust the results, as 95 percent of cases show the output is correct, but for the other 5 percent, I find that the output is good and meets my expectations. AI and ML Development is deployed in my organization using on-premises resources within the on-premises structure. I rated AI and ML Development a 9 out of 10 overall.
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.