According to IDC, the Artificial Intelligence market will attain a gigantic 37% compound annual growth by 2022. Owing to its popularity, several tools and software have emerged in the market to make AI adaptation easier. However, one tool that clearly stands out in all respects is Amazon Sagemaker. In this blog, we take an in-depth look at what it is, why use it, and how to go about its usage.
What is Amazon Sagemaker?
Amazon SageMaker is a fully managed AWS solution that empowers data scientists and developers to quickly build, train, and deploy machine learning models. It is in the form of an integrated development environment for machine learning, the Amazon SageMaker Studio, which acts as a base to build upon a collection of other AWS SageMaker tools.
You can build and train ML models from scratch or purchase pre-built algorithms that suit your project requirements. Similar tools are available for debugging models or adding manual review processes atop model predictions.
Image via Amazon
Why Should You Use It?
The complexity of the machine learning project in any enterprise increases with the expansion of scale. This is because machine learning projects comprise of three key stages - build, train and deploy - each of which can continuously loop back into each other as the project progresses. And as the amount of data being dealt with increases, so does the complexity. And if you are planning to build a ML model that truly works, your training data sets will tend to be on the larger side.
Typically, different skill sets are required at different stages of a machine learning project. Data scientists are involved in researching and formulating the machine learning model, while developers are the ones taking the model and transforming it into a useful, scalable product or web-service API. But not every enterprise can put together a skilled team like that, or achieve the necessary coordination between data scientists and developers to roll out workable ML models at scale.
This is exactly where Amazon Sagemaker steps in. As a fully managed machine learning platform, SageMaker abstracts the software skills, enabling data engineers to build and train the machine learning models they want with an intuitive and easy-to-use set of tools. While they play to the core strengths of working with the data and crafting the ML models, the heavy lifting needed for developing these into a ready-to-roll web-service API is handled by Amazon Sagemaker.
Amazon SageMaker packs all the components used for machine learning in a single shell, allowing data scientist to deliver end-to-end ML projects, with reduced effort and at lower cost.
How It Works?
With a 3-step model of Build-Train-Deploy, Amazon SageMaker simplifies and streamlines your machine learning modeling. Let’s take a quick look at how it works.
Amazon SageMaker offers you a completely integrated development environment for machine learning that lets you improve your productivity. With the help of its one-click Jupyter notebooks, you can build and collaborate with lightning speed. Sagemaker also offers you a one-click sharing facility for these notebooks. The entire coding structure is captured automatically, which allows you to collaborate with others without any hurdle.
Apart from this, the Amazon SageMaker Autopilot is the first automated machine learning capability of this industry. It allows you to have complete control as well as visibility into your respective machine learning models. The traditional approaches of automated machine learning do not allow you to peek in the data or logic used to create that model. However, the Amazon Sagemaker Autopilot is capable of integrating with Sagemaker Studio and provides you complete visibility into the raw data and information used in the creation.
One of the highlights of Amazon SageMaker is its Ground Truth feature that helps you in building as well as managing precise training datasets without facing any hurdle. The Ground Truth provides you complete access to the labelers via Amazon Mechanical Trunk along with pre-built workflows as well as interfaces for common labeling tasks. The Amazon Sagemaker comes with the support of various deep learning frameworks including PyTorch, TensorFlow, Apache MXNet, Chainer, Gluon, Keras, Scikit-learn, and Deep-Graph library.
Leveraging Amazon Sagemaker, Srijan built a video analytics solutions that can scrape video feed data to log asset performance.
Using AWS Lambda, Amazon SageMaker and Amazon S3, Srijan developed a video analytics solution for the client. The solution utilized a machine learning model to scrape video feed data and log asset performance over a given period of time and assigned location.
As a result, it helped in:
Using Amazon SageMaker Experiments, you can easily organize, track, and evaluate every iteration to machine learning models. Training a machine learning model packs various iterations to measure and isolate the impact of changing algorithm versions, model parameters, and changing datasets. The Sagemaker Experiments help you in managing these iterations via capturing the configurations, parameters, and results automatically, and storing them as ‘experiments’.
SageMaker comes with a debugger functionality that is capable of analyzing, debugging, and fixing all the problems in your machine learning model. Debugger makes the training process entirely transparent by capturing real-time metrics during the process. The Sagemaker Debugger also comes with a facility of generating warnings as well as remediation advice if any common problems are detected during the training process.
Apart from this, AWS Tensorflow optimization offers you a scaling facility of up to 90% with the help of its gigantic 256 GPUs. Using this, you can experience precise, and sophisticated training models in very little time. Furthermore, the Amazon Sagemaker comes with a Managed Spot Training that helps reduce training costs up to 90%.
Amazon SageMaker offers you a one-click deployment facility so that you can easily generate predictions for batch or real-time data. You can easily deploy your model on auto-scaling Amazon machine learning instances across various availability zones for improved redundancy. You just need to specify the desired maximum and minimum numbers, and the type of instance, and then leave the rest to Amazon Sagemaker.
The major problem that can affect the accuracy of your entire operation is the difference between data used to generate predictions and the data used to train models. The SageMaker Model Monitor can help you in getting out of this puzzle by detecting and remediating concept drift. The Sagemaker Model Monitor detects the concept drift in all of your deployed models automatically and then provides alerts to identify the main source of the problem.
The Amazon Sagemaker also packs Augmented AI facility, with the help of which, you can easily allow human reviewers to step in if the model is unable to make high confidence precise predictions. Moreover, the Amazon Elastic Inference is capable of minimizing your machine learning inference costs by 75%. Lastly, Amazon also allows you to integrate Sagemaker with Kubernetes, by which you can easily automate the deployment, scale, and management of your applications.
So there you have it, a look at how Amazon Sagemaker can help build, train and deploy machine learning models to suit your project requirements.
Srijan is an advanced AWS Consulting Partner, and can help you utilize AWS solutions at your enterprise. To know more, drop us a line outlining your business requirements and our expert AWS team will get in touch.