Summary

As you can see, there is a lot to cover across the AI/ML world, and we have just scratched the surface of what lies beneath.

In this chapter, we covered a bit of the history of AI/ML in order to understand how we got to this point. We also saw how a huge amount of data is feeding the AI revolution with cheap storage and more sources creating it. Business value is also a main driving force pushing this tech forward, and we saw how some companies such as Netflix and Google are leveraging it to solve different problems. Moving on to out-of-date models, we learned what drift is, and how it can impact your ability to deliver consistent predictions on production data as the real-world changes. Finally, we got a bit more hands-on by installing packages with Anaconda's package manager, conda.

With this knowledge, you're now equipped to be able to distinguish between different models and problem types to use in certain scenarios, including supervised and unsupervised. You'll also be able to know whether the trade-offs for online versus batch learning are worth it and be able to determine which is right for you. Lastly, you should now be able to install Anaconda Individual Edition and use it to create an environment and download the packages that you will need for the rest of this book.

In our next chapter, you'll learn everything you need to know about the bedrock of AI: open source. You'll get hands-on experience with the most popular tools, NumPy and pandas, learn about the open source community and how to take part, and practice how to import anything you need using conda and the conda-forge community into your project in seconds.