The Secret of Delivering Machine Learning to Production
The vast majority of Machine Learning (ML) projects FAIL. — If you’re a Data Scientist, this probably comes as no surprise: you have seen many projects that start as a great promise and fade away, or worse — hit production and then rolled back due to poor performance and customer complaints. Some just treat this as a “part of life” in ML teams. But have you ever wondered whether there’s a common denominator for those failures?