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graphics, GPUs are highly parallelized, which means they can perform large numbers of similar
calculations at the same time. It turns out that massive parallelism is extremely useful in speeding up
the training of Machine Learning AI models and in running those models operationally. For many types

of Machine Learning, using GPUs can speed up the training process by 10-20 times, while reducing
computer hardware costs. Access to cloud storage is also very helpful, since organisations can rapidly
access massive computing resources on demand and limit purchases of computing power to only what
they need, when they need it.

Improved Machine Learning Algorithms
The first Machine Learning algorithms are decades old, and some decades-old algorithms remain
incredibly useful. In recent years, however, researchers have discovered many new algorithms that
have greatly sharpened up the field’s cutting-edge. These new algorithms have made Machine Learning
models more flexible, more robust, and more capable of solving different types of problems.

Open-source Code Libraries and Frameworks
The cutting-edge of Machine Learning is not only better than ever, but also more easily available. For
a long time, Machine Learning was a specialized niche within computer science. Developing Machine
Learning systems required a lot of specific expertise and custom software development that made it
out of reach for most organizations. Now, there are many open-source code libraries and developer
tools that allow organisations to use and build upon the work of external communities. As a result, no
team or organization has to start from scratch, and many parts that used to require highly specialised
expertise have been largely automated. The difficulty of developing an AI model has reduced to the
point where even non-experts and beginners can create useful AI tools. In some cases, open-source ML
models can be entirely reused.

TYPES OF MACHINE LEARNING
Like Artificial Intelligence, Machine Learning is also an umbrella term, and there are four different broad
families of Machine Learning algorithms. There are also many different subcategories and combinations
under these four major families, but a good understanding of these four broad families will be sufficient

to understand the techniques used.
The four categories differ based on what
types of data their algorithms can work
with. The important distinction is not
whether the data is audio, images, text, or
numbers. Rather, it is whether or not the
training data is labeled or unlabeled and
how the system receives its data inputs.
The image given illustrates labeled and
unlabeled training data for a classifier of
images of cats and dogs.

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