Investing in your tech stack
Understanding the tech stack and languages that will give you the most flexibility is the most important part of beginning your tech stack journey. In this phase, you’ll work closely with your data science and data engineering teams to create the proper channels for delivering the relevant data to your models in a reliable way so that all the other stakeholders involved in building your product can trust the infrastructure in place.
Managing ML experimentation is a formidable undertaking in and of itself, and we’ve seen tools such as MLflow and Weights & Biases used for managing versions and experiments. You can also use tools such as Cloudera Data Science Workbench, Seldon, Dataiku, DataRobot, Domino, SageMaker, and TensorFlow to support your data scientists with a workstation for building, experimenting with, deploying, and training ML models.
As a PM, you’re regularly thinking about the value of building something compared...
