Every data science project consists of two parts; the data science/modelling part and the engineering/production part. 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 the 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.
In this eBook you will learn:
- What is ML Ops
- How ML Ops has evolved
- What is the ideal methodology required to execute data science projects
- How does the inference bridge solve problems associated with the traditional data science process