Every organization wants to identify the right sales leads at the right time to optimize conversions. Lead scoring is a popular method for ranking prospects through an assessment of perceived value and sales-readiness. Scores are used to determine the order in which high-value leads are contacted, thus ensuring the best use of a salesperson’s time. Of course, lead scoring is only as good as the information supplied.

When operationalizing machine and deep learning, a production-first approach is essential for moving from research and development to scalable production pipelines in a much faster and more effective manner. Without the need to refactor code, add glue logic and spend significant efforts on data and ML engineering, more models will make it to production and with less issues like drift.

What if I want to serve a Huggingface model on ClearML? Where do I start? In general, machine learning engineers know by now that a good model serving engine is invaluable when serving models in production. These days, NVIDIA’s Triton inference engine is a popular option to do so, but it is lacking in some respects.

Like humans, Machine Learning (ML) models can recognize intricate patterns and anticipate the outcome of new data. On some natural language problems, ML models have even surpassed human performance. But much like people, ML models are susceptible to error. For every ML application in the real world, estimating how frequently a model will be inaccurate is essential. Intuitively presenting information and emphasizing the best ways to enhance a model are equally important.