How To Design Machine Learning System. FutureUniverseTV Presents Practical Concepts.
Machine learning is the study of algorithms that become more efficient with experience at completing a particular task. As part of this course, students will gain an understanding of how machine learning has led to many innovative applications in the real world. In addition, students will gain an in-depth understanding of a broad range of machine learning algorithms, including: naive Bayes, logistic regression, neural networks, clustering, probabilistic graphic models, reinforcement learning, and SVMs.
Assisted machine learning involves instructing a model with correct examples of the predictions that you would like it to make, and then training it to mimic human intuition as a result. There is also unsupervised machine learning, in which no instructions are provided to the model in order to find connections/patterns in the data. In addition to supervised ML, semi-supervised ML is also available. In order for the engine to run, test data is the fuel. Structured data can include images and text, while unstructured data can include stock trading activity and software usage patterns.
The test data will eventually be processed into a prediction or transformed into structured training data. Feature generation is the process of augmenting test data in a way that the model can understand it. A “feature” is a relationship in the data that a machine learning model can learn (e.g. the relationship between the shape of a car in aerial imagery and its classification as a “car”). In unstructured data, feature generation is achieved by hand-labeling test data (e.g. drawing bounding boxes on an image), whereas in structured data, it is accomplished by feature engineering.
The term validation simply refers to the process of verifying the accuracy of either human or model-generated features. Validation is simply a process in which a trained reviewer approves or rejects/edits each model prediction or human annotation. Data for training a model is structured, validated, and can be used in a variety of ways. During model training, supervised machine learning models are taught how to make inferences from the training data that are as close as possible to the judgments represented in the training data. In this case, the model predictions refer to the judgments that the model makes when presented with test data in order to make predictions about it. Application refers to a software application in this case. The use of machine learning systems is typically intended to support an application that assists users in making a decision of some sort and, in some cases, automates part or all of that decision. The field of Natural Language Processing (NLPP) deals with the interpretation of text. The field of Computer Vision (CV) focuses on the interpretation of images.
If you are interested in learning how to design machine learning systems, you should read case studies to gain an understanding of how real teams deal with different deployment requirements. Airbnb, Lyft, Uber, Netflix, and many other companies publish excellent tech blogs where they share their experiences using machine learning to improve products and/or processes. In case you are interested in a company, you should visit their tech blogs to learn more about what they have been working on. This could come up in your interview!
Basically, when you feed the Training Data to the Machine Learning Algorithm, the algorithm will produce a mathematical model and based on that mathematical model, the machine will make a prediction and make a decision without any explicit programming. Additionally, the more the machine is exposed to training data, the more experience it will gain, which results in a more efficient result.
Perhaps you are in need of a template by this point. I would also like to do so. It has nevertheless become apparent to me that there is no perfect design document template. Each ML system will have its own optimal structure and sections. In addition, my experience has made me cautious about the use and provision of templates. By following templates blindly, authors are forced to play a fill-in-the-blank game. Consequently, they don’t pay attention to aspects unique to their application or system, instead focusing on filling out sections.
However, some templates present redundant information that your audience already knows (e.g., organization/team tech stack) – this wastes document space and your readers’ time. In order to construct a good design document, it is helpful to write with the intention of thinking deeply and to receive feedback. Provide enough detail so that others will be able to provide meaningful feedback before you implement the system. A good design document is usually the result of doing this. In addition, please refer to the pointers above for guidance. In any case, if you still require a template that guides your thinking, here is a minimalist template designed to be as lean as possible.
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