Building ML in the cloud
Cloud computing is a technology that delivers on-demand IT resources that can grow and shrink at any time, depending on the need. There is no more buying and maintaining computer servers or data centers. It is much like utilities in your home, such as water, which is there when you turn on the faucet. If you turn it all the way, you get a high-pressure water stream. If you turn it down, you conserve water. If you don't need it anymore, you turn it off completely. With this model, developers and teams get the following benefits from on-demand cloud computing:
- Agility: Quickly spin up resources as you need them. Develop and roll out new apps, experiment with new ideas, and fail quickly without risks.
- Elasticity: Scale your resources as you need them. Cloud computing takes away "undifferentiated heavy lifting" – racking up additional servers and planning capacity for the future. These are things that don't help address your core business problems.
- Global availability: With a click of a button, you can spin up resources that are closest to your customers/users without relocating your physical compute resources.
How does this impact the field of ML? As compute resources become easier to acquire, information exchange becomes much more frequent. As that happens, more data is generated and stored. And more data means more opportunities to train more accurate ML models. The agility, elasticity, and scale that cloud computing provides accelerates the development and application of ML models from weeks or months down to a much shorter cycle so that developers can now generate and improve ML models faster than ever. Developers are no longer constrained by physical compute resources available to them. With better ML models, businesses can make better decisions and provide better product experiences to customers.
For cloud computing, we will be using Amazon Web Services, which is the provider of Amazon SageMaker Studio, throughout the book.
