In this chapter, we have discussed Anaconda Cloud. Some topics included the Jupyter Notebook in depth, different formats of the Jupyter Notebook, how to share notebooks with your partner, how to share different projects over different platforms, how to share your working environments, and how to replicate others' environments locally.
For the next chapter, we will discuss distributed computing and Anaconda Accelerate. When our data or tasks become more complex, we'll need a good system or a set of tools to process data and run a complex algorithm. For this purpose, distributed computing is one solution. In particular, we will explain many concepts, such as compute nodes, project add-ons, parallel processing advanced Python for data parallelism. In addition, we will give several examples showing how to use distributed computing.