Every data science project consists of two parts; the data science experimentation & modelling phase and the inference production & engineering process. There are several methodologies which tackle the arduous task of getting these two gears to match and work in conjunction. And this is not just a technical problem; it’s a people problem as well as a culture + process problem.
This eBook takes a look at the various approaches available with us today to tackle this issue, right from rewrite/translate to state of the art ONNX. It identifies the ideal methodology required. Then we illustrate our innovative solution in MLOps solution called the Inference Bridge which closes the gap between modelling and engineering. In conclusion, this eBook explains how the modxchange approach can be leveraged for faster end-to-end iterations and greater return on investment.
- What are the key challenges in MLOps
- What is the ideal methodology required to execute data science projects
- How has model packaging evolved in MLOps
- How does the inference bridge solve problems associated with the traditional data science process