Machine learning is the act of adapting various computer systems to keep learning from experiences, and hence become better performers. This helps industries fine-tune their processes, be it manufacturing, fault detection, image and video processing, and much more. Amazon SageMaker is a machine learning program used to build, train, and deploy these models, for any use case. Built from scratch for every type of data scientist and developer in mind, most skill levels can use the platform.
Amazon SageMaker acts as a large set of tools and features that make the machine learning process accessible for everyone. Below we will go over some of the Amazon SageMaker modules that allow for better machine learning.
Amazon SageMaker Autopilot
SageMaker Autopilot is all about automation. General machine learning is still a manual process with algorithm testing and other minute details to find the best model. People have learned to use AutoML which curbs the effort and expertise by automating most of it. This does, however, lower the visibility of the impact of your features for model prediction, making it a little troublesome. You can’t exactly see the changes it made and how it made them, which is a disadvantage.
With SageMaker Autopilot, all you have to do is feed it a tabular data set, and it will handle the rest. It will explore the data to find different solutions, then picking the best model while giving you full visibility. You just have to deploy the model that is presented to you or make minor adjustments to it just before. This ensures that you still have some agency in producing the best model possible while it. Easily create cloud applications with this feature.
Amazon SageMaker Ground Truth
Ground Truth is a service for the labeling of large data sets. Labels are useful when you want highly accurate training data sets for machine learning. Label data easily as it acts as a console with its own interface with assistive features. A data labeling workflow is formed with full support for video, image, and text. Organize how your business goes about machine learning with this innovative tool.
Amazon SageMaker Data Wrangler
For the preparation of data, there is no better utility than Data Wrangler. Data Wrangler greatly increases the simplification of the process and follows all data preparation steps. These include data selection, cleaning, exploration, and visualization from just a singular console interface. Data importation from any source is also a simple process, asking for just a single click for confirmation. The hundreds of built-in data transformations let you transform and normalize without ever interacting with code. Lastly, it comes with many different visual templates, so you can see changes made at a glance to make sure.
Amazon SageMaker Model Monitor
Model Monitor analyzes existing models and checks if their predictions are up to par or not. Model drift, which is a model getting less and less accurate over time, can also be tracked in real-time. Analyze your model’s quality using dependent and independent variables, these correspond to their outputs and inputs respectively.
Amazon SageMaker Clarify
Use Clarify to avoid biases getting in the way of training machine learning models. These biases can take root in the algorithm or data sets used to train the model. Perhaps you have focused your data set on a particular age group, or any similar reasons like this.
You will receive generated graphs regarding predictions and issues that you can then take corrective actions on.
Amazon SageMaker Pipelines
SageMaker Pipelines is the first and only continuous delivery and continuous integration service specifically for machine learning. With this, you will be able to create and then manage your workflows. Usually, it takes months to organize every step of the machine learning process, based on coding and other activities.
SageMaker Pipelines lets you automate a number of these steps like data loading, transformation, and much more. The end result is you can create many models in any given week and manage large amounts of data.
AWS and Machine Learning
With AWS, you have more agency in how you handle your machine learning models. Even though it is automated, you still get the opportunity to fine-tune and perfect your model before deployment. Data is cleaned and prepared throughout the entire process, with most steps not requiring any coding.
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