Compilers often need to make estimates of hardware characteristics during early optimization passes, which are available only later such as execution unit utilization, number of register spills, latency, throughput etc. Often a hand-written static/analytical hardware cost model is built into the compiler, for example, LLVM's TTI. However, the need for more sophisticated and varied predictions has become more pronounced with the development of deep learning compilers which need to optimize dataflow graphs. Such compilers usually employ a much higher level MLIR form as an IR representation. A static/analytical cost model is cumbersome and error prone for such systems. We develop a machine learning-based cost model for high-level MLIR which can predict different target variables of interest such as CPU/GPU/xPU utilization, instructions executed, register usage etc. by considering the incoming MLIR as a text input a la NLP models. The learnt model can be deployed to estimate the target variable of interest for a new computation graph. We report early work in progress results of developing such a model and show that these models can provide reasonably good estimates with low error bounds.