ML and GCP
In this topic you will be introduced to the different options that exist in GCP when it comes to leveraging machine learning and First Though we will explore the relationship between machine learning, artificial intelligence and deep learning. A very common question asked what’s the difference between a I artificial intelligence machine learning and deep learning well one way to think about it is that AI is the discipline like physics, it refers to machines that are capable of acting autonomously to build machines that can solve problems by thinking and acting like humans, Machine learning within there is a toolset like Newton’s laws of physics and mechanics as you can use Newton’s laws to figure out how long will it take a ball to drop off and fall of the cliff, you can use machine learning to solve certain kinds of problems using data examples but without the need for any custom code. Deep learning is a type of machine learning that works even though the data consists of unstructured data like images, speech, video, natural language, text etc One kind of deep learning is image classification a machine can learn how to classify images into categories when it is shown lots of different examples and a really cool thing about deep learning is that often times in a really complex problems it can do better than a human at the basic difference between machine learning and other techniques in AI is that machine learning, machines learn they don’t start out intelligent they become intelligent. Back to our example lets say we built a machine learning model to find bad manufactured parts and we want to remove them. Quality control is now pretty inexpensive so what the business factor motivating us isn’t my business will save money it’s I could add quality control throughout our entire manufacturing process instead of just doing the quality control in the end of the manufacturing line, we can now insert everywhere improve overall quality. The opportunity is for organizations to take advantage of the ease of creating new models to continue to transform their business so now that you know what ml is and I hope that you are starting to come up with those ideas of your own really good together. Much of the hype about AI now is that the barriers to entry of building the models has fallen dramatically. You don’t have to be an astra physicist to do machine learning. The increasing availability of data the increasing maturity and sophistication of those ml algorithms for you to choose from and the increasing power in the availability of computer hardware and software through things like cloud computing. Imagine we want to build that model identify diseased leaves to predict the health of the trees remember we can do that using a standard algorithm for image classification processing you just need to know which algorithm should you choose off-the-shelf. Another critical ingredient for ml is that data, we need to collect lots of images of leaves. Finally we need the hardware and the software to make that happen and that’s easier now than it’s ever been in the past. We can use the cloud to power our ml model so that we can do it cost effectively. Different options exist when it comes to leveraging machine learning, advanced users who want more control over the building and training of their ml models use tools that offer the levels of flexibility that they looking for. This can involve developing custom models through an ML library like tensor flow thats supported on AI platform. This opton works well for data scientists with the skills and the need to create a custom tensorflow model. But inceasingly you don’t have to do that Google makes the power of machine learning available to you even if you have a limited knowledge of ML you can use Cloud AutoML to build on Google’s machine learning capabilities to create your own custom machine learning models that are tailored to your specific business needs and then intergrate those models in the applications or websites all without running a tensorflow code. Alternatively Google has a range of pretrained machine learning models that are ready for immediate use with an applications in ways that the respective APIs are designed to support such pretrained models are excellent ways to replace user input with machine learning.