If we have decided to carry out the training process on a desktop, we have to do it in the cloud or on our humble local server, based on our needs.

If we decide to use the cloud, again we have the following two options:

• Generic cloud computing
• Hosted machine learning

Generic cloud computing is similar to utilizing the cloud service provider to carry out our work. We want to carry out machine learning training. So, in order to carry this out, whatever is required, say hardware, storage, and so on, must be obtained from them. We can do whatever we need with these resources. We need to place our training dataset here, run the training logic/algorithms, build the model, and so on.

Once the training is done and the model is created, the model can be taken anywhere for usage. To the cloud provider, we pay the charges for utilizing the hardware and storage only.

Amazon Web Services (AWS) and Azure are some of the cloud-computing vendors.

The benefits of using this approach are as follows:

• The hardware/storage can be procured and used on the go. There is no need to worry about increasing storage and so on, when the amount of training data increases. It can be incremented when needed by paying the charges.
• Once the training is done and the model is created, we can release the computing resources. Costs incurred on computing resources are only for the training period and hence if we are able to finish the training quickly, we save a lot.
• We are free to download the trained model and use it anywhere.

What we need to be careful about when we go for this approach is the following:

• We need to take care of the entire training work and the model creation. We are only going to use the compute resources required to carry out this work.
• So, we need to know how to train and build the model.

Several companies, such as Amazon, Microsoft, and Google, now offer machine learning as a service on top of their existing cloud services. In the hosted machine learning model, we neither need to worry about the compute resources nor the machine learning models. We need to upload the data for our problem set, choose the model that we want to train for our data from the available list of models, and that's all. The machine learning services take care of training the model and providing the trained model to us for usage.

This approach works really well when we are not so well-versed to write our own custom model and train it, but also do not want to go completely to a machine learning provider to use their service, but want to do something in between. We can choose between the models, upload our unique dataset, and then train it for our requirements.

In this type of approach, the provider usually makes us tied to their platform. We may not be able to download the model and deploy it anywhere else for usage. We may need to be tied to them and utilize their platform from our app for using the trained model.

One more thing to note is that if at a later point in time, we decide to move to another provider, the trained model cannot be exported and imported to the other provider. We may need to carry out the training process again on the new provider platform.

In this approach, we might need to pay for the compute resources –hardware/storage –plus, after the training, to use the trained model, we may need to pay on an ongoing per-usage basis, that is, an on-demand basis; whenever we use it, we need to pay for what we use.

The benefits of using this approach are as follows:

• There is no need to worry about the compute resources/storage required for training the data.
• There is no need to worry about understanding the details of machine learning models to build and train custom models.
• Just upload the data, choose the model to use for training and that's it. Get the trained model for usage
• There is no need to worry about deploying the model to anywhere for consumption from the mobile application.

What we need to be careful about when we go for this approach is as follows:

• Mostly, we may get tied to their platform after the training process in order to use the model obtained after training. However, there are a few exceptions, such as Google's Cloud platform.
• We may be able to choose only from the models provided by the provider. We can only choose from the available list.
• A trained model from one platform cannot be moved to another platform. So, if we decide to change the platform later, we may need to retain again in their platform.
• We may need to pay for compute resources and also pay on an ongoing basis for usage of the model.

Using our private cloud/simple server is similar to training on the generic cloud, except that we need to manage the compute resources/storage. In this approach, the only thing we miss out on is the flexibility given by generic cloud solution providers that include increasing/decreasing the compute and storage resources, the overhead to maintain and manage these compute resources, and so on.

The major advantage we get with this approach is about the security of the data we get. If we think our data is really unique and needs to be kept completely secured, this is a good approach to use. Here, everything is done in-house using our own resources.

The benefits of using this approach are as follows:

• Absolutely everything is in our control, including the compute resources, training data, model, and so on
• It is more secure

What we need to be careful about when we go for this approach is the following:

• Everything needs to be managed by us
• We should be clear with the machine learning concepts, data, model, and training process
• Continuous availability of compute resources/hardware is to be managed by us
• If our dataset is going to be huge, this might not be very effective, as we may need to scale the compute resources and storage as per the increasing dataset size