What is Next?

Computers are developing more and more day by day, and the form factors of devices are changing tremendously. In the past, we would only see computers in offices; however, now we see them on our home desks, on our laps, in our pockets, and on our wrists. The market is becoming increasingly varied as machines are equipped with more and more intelligence.

Almost every adult currently carries a device with them, and it is estimated that we look at our smartphones at least 50 times a day, whether there is a need for it or not. These machines affect our daily decision-making processes. Devices are now equipped with applications such as Siri, Google Assistant, Alexa, or Cortana—these are features that are designed to mimic human intelligence. The ability to answer any query thrown at them presents these types of technology as superior to humans. On the backend of...

Popular ML–based cloud services

To begin your ML journey, you can use one of the existing cloud-based services on ML. ML as a Service (MLaaS) is widely used across all contemporary business sectors. Data is becoming cheaper, and data volume is growing exponentially. As a result, the processing power of devices is increasing at a much quicker rate. This trend has made way for multiple cloud-based services, such as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), now joined by MLaaS.

Even though we can run an ML model on our mobile device, it is still greatly impacted by limited memory, CPU and Graphics Processing Unit (GPU) resources. In this case, a cloud service comes in handy.

Starting with ML in the cloud, there are multiple services available, such as facial recognition, optical character recognition, image recognition...

Building your first ML model

With the knowledge that you have gained from this book, you can start to develop your own model that runs on a mobile phone. You will need to identify the problem statement first. There are many use cases where you will not need an ML model; we can't unnecessarily force ML into everything. Consequently, you need to follow a step-by-step approach before you build your own model:

1. Identify the problem.
2. Plan the effectiveness of your model; decide whether the data could be useful in predicting the output for future, similar cases. For example, collecting the purchase history for people of a similar age, gender, and location will be helpful in predicting a new customer's purchasing preferences. However, the data won't be helpful in predicting the height of a new customer, if that is the data that you are looking for.
3. Develop a simple model...

Summary

With all the basic ideas that you have gained from this book, you can start building your own application with ML capabilities. Furthermore, with all of the new ways to interact with devices such as Amazon Alexa, Google Home, or the Facebook portal, you will find more use cases to build ML applications. Ultimately, we are moving toward a world with more connected devices, bringing the connections and communications closer to us, and leading us to create better experiences with ML.

Further reading

There are a lot of ML courses that are available online. A few of these courses are listed as follows:

• If you are beginner, you can start with the Coursera tutorial on ML by Andrew Ng, which can be found at https://www.coursera.org/learn/machine-learning
• An ML crash course from Google can be found at https://developers.google.com/machine-learning/crash-course/
• One of the best (and most enlightening) ML based blog series, by Adam Geitgey, can be found at https://medium.comi@ageitgey/machine-learning-is-fun-80ea3ec3c471
• You can kick-start your skills in TensorFlow at https://codelabs.developers.google.com/codelabs/tensorflow-for-poets
• A more thorough look at TensorFlow can be found at https://petewarden.com/2016/09/27/tensorflow-for-mobile-poets/