> However, it's incredibly difficult to harness the compute power of Colab for anything beyond Jupyter notebooks. For Machine Learning engineers that want to pr

> However, it's incredibly difficult to harness the compute power of Colab for anything beyond Jupyter notebooks. For Machine Learning engineers that want to productionize their models and bring them out of the notebook stage, this is a particularly relevant issue; notebooks, while perfect for exploration, don't play well with more advanced MLOps tools that codify the training process into a formal pipeline. That isn't what Colab is intended for. Google has better and more productive tools for companies who can fit the bill, which is getting cheaper over time. AI Notebooks behave the same in practice as Google Colab with one-click one/off for model testing + JupyterLab. If you want to minimize costs via spot instances, you can deploy a Compute Engine with the Deep Learning VM image, which also includes a running JupyterLab on launch if need to use that workflow, and also saves time by including your framework of choice. A spot VM with a T4 GPU is about $0.18/hour.