Solution Planning Tasks


Before you jump in, make sure you plan your solution architecture and requirements. You should know the answers to questions such as the following (refer to the Concepts page for more information if required. You may also want to go through the Use Cases first):

  • What kinds of components will the solution need? - xpresso.ai supports jobs, pipeline jobs, databases, libraries, services and inference services

  • Which programming languages will you be using? - xpresso.ai supports python, java and SQL “flavors” of components (not all flavors are supported for all types of components, though)

  • How many environments will the solution be deployed to? - xpresso.ai supports 5 environments for each solution - DEV, INT, QA, UAT and PROD. If you’re just trying things out, a DEV environment might be enough for you.

  • Does the solution need Big Data support? - if your solution is likely to involve large amounts (100s of GBs) of data, consider using xpresso.ai’s built-in Big Data support by deploying to Spark environments. (Caveat: This will involve coding in python for Spark)

  • How much disk space will the solution need? - xpresso.ai provides disk space (up to 10 GB) for each solution on a shared NFS drive

  • Will the solution need data / model pipelines? - xpresso.ai has special support for pipelines - if you’re planning to use pipelines, it’s better to use components of type “pipeline_job” in them

  • Will the solution need special data management functions? - consider using xpresso.ai inbuilt libraries, or components from the xpresso.ai Component Library if

    • the solution needs to pull in data from external data sources (databases, local / remote / cloud file systems)

    • the solution needs to perform basic data exploration and visualization

    • the solution needs data versioning capabilities

What do you want to do next?