Introduction To Amazon SageMaker Built-in Algorithms

Amazon SageMaker equips with built-in algorithms to benefit data scientists and machine learning practitioners who get initiated on training and deploying machine learning models rapidly.

In this blog, we are going to cover each Amazon SageMaker Built-in algorithm in detail.

1. What Is Amazon SageMaker and How It Works?
2. Built-in Algorithms in Amazon SageMaker
   1. Supervised Learning
   2. Unsupervised Learning
   3. Textual Analysis
   4. Image Processing
3. Why Amazon SageMaker?
4. General Amazon SageMaker FAQs

What Is Amazon SageMaker And How It Works?

• Amazon SageMaker is a totally managed ML service. With SageMaker, data scientists can speedy and without difficulty build and train ML models, and then immediately set up them right into manufacturing equipped hosted environment.
• It provides an incorporated Jupyter authoring notebook instance for simple access to your records sources for exploration and analysis so you don’t need to control servers.
• It additionally presents common machine learning algorithms that may be optimized to run effectively against extremely big data in an exceedingly distributed environment.
• In SageMaker, first, we preprocess data during a Jupyter notebook on our notebook instance. We use our notebook to fetch our dataset, explore it, and prepared it for model training.
• To train a model, we’d like one in all the algorithms that SageMaker provides. we will install our version independently with SageMaker web hosting services, and decoupling it from our application code.

Built-in Algorithms In Amazon SageMaker

17 built-in algorithms in Amazon SageMaker. These built-in algorithms are divided into 4 sections.

1. Supervised Learning
2. Unsupervised Learning
3. Textual Analysis
4. Image Processing

1) Supervised Learning

• In supervised learning, the training data we feed to the machine learning algorithm includes the required solutions, called labels.
• Amazon SageMaker offers numerous built-in general-purpose algorithms that will be used for both classification or regression problems.
• Linear Learner Algorithm: learns a linear feature for regression or a linear threshold function for classification. It is accustomed to Predict a numeric/continuous value. The data input format is Tabular.
• Factorization Machines Algorithm: It is an updated version of a linear model that is constructed to economically grab communication between features within high-dimensional sparse datasets. It is used to predict if an item belongs to a category. The data input format is Tabular.
• XGBoost Algorithm: implementation of the gradient-boosted trees algorithm that mixes an ensemble of estimates from a fixed of easier and weaker models. It is used to predict if an item belongs to a category. The data input format is Tabular.
• K-Nearest Neighbors (k-NN) Algorithm: a non-parametric approach that creates use of k adjacent labeled points to assign a label to a new data point for classification or a forecast target value from the average of the k adjacent points for regression.
• Object2Vec Algorithm: It is a highly customizable multi-purpose machine learning algorithm made for feature engineering.
• DeepAR Forecasting Algorithm: It is a type of supervised learning algorithm for forecasting 1-D time series using RNN.

2) Unsupervised Learning

• In unsupervised learning, the training data is unlabeled.
• Amazon SageMaker equips with several built-in algorithms that may be used for a range of unsupervised learning projects like clustering, pattern recognition, dimension reduction, and anomaly detection.
• Principal Component Analysis (PCA) Algorithm: It lowers the dimensionality within a dataset by projecting data points onto the primary few principal components. The aim is to keep as much information or variation as possible.
• K-Means Algorithm: It is used to finds discrete groupings within the dataset, where members of a group are as identical as possible to one another and as non-identical as possible from members of other groups.
• IP Insights: It is used to capture associations b/w IPv4 addresses and various entities, such as account numbers or user IDs. It learns the usage patterns for IPv4 addresses.
• Random Cut Forest (RCF) Algorithm: It identifies anomalous data points within a data set that different from otherwise well-structured or patterned data.

3) Textual Analysis

• Amazon SageMaker equips with many algorithms that are tailored to the study of textual documents used in NLP, summarization or document classification, classification or topic modeling, and translation or language transcription.
• BlazingText algorithm: It is a highly advanced application of the Word2vec and text classification algorithms that simply scale to large datasets. It is helpful for many downstream NLP tasks.
• Sequence-to-Sequence Algorithm: It is a supervised algorithm generally used for neural machine translation.
• Latent Dirichlet Allocation (LDA) Algorithm: an algorithm used for determining topics in a set of documents. It is an unsupervised algorithm, which means that it doesn’t require an example dataset with answers during training.
• Neural Topic Model (NTM) Algorithm: It is also a type of unsupervised technique used for determining topics in a set of documents, using a NN approach.

4) Image Processing

• Amazon SageMaker also equips with many image processing algorithms that are designed for object detection, image classification, and computer vision.
• Image Classification Algorithm: It is a supervised algorithm using example data with answers. This algorithm used to classify images.
• Semantic Segmentation Algorithm: It’s used to developing computer vision applications using a pixel-level, fine-grained approach.
• Object Detection Algorithm: It is used to classifies and detect objects in images using a single DNN (deep neural network). It is a supervised learning algorithm that identifies all instances of objects within the image scene after taking images as input.

Why Amazon SageMaker?

• Amazon SageMaker is made on Amazon’s two decades of experience developing real-world ML applications, including product recommendations, intelligent shopping, personalization, robotics, and voice-assisted devices.
• It increases team productivity 10 times.
• It reduces 90% cost with managed spot training.
• 75% lower inference costs.
• 70% reduction in data labeling costs.
• 198 new capabilities added since launch.
• 22 compliance programs (FedRAMP, ISO, PCI, HIPAA, SOC 1/2/3,  and more).
• It supports the leading machine learning frameworks
  • TensorFlow
  • PyTorch
  • Mxnet

General Amazon SageMaker FAQs

Q: How Amazon SageMaker charged you?

Answer: You pay for storage, ML compute, and data processing resources you use for hosting the notebook, performing predictions, training the model, and logging the outputs. You only pay for what you use.

Q: What if you have your own notebook, training, or hosting environment?

Answer: Amazon SageMaker provides a full end-to-end workflow, but you’ll be able to still use your current tools with SageMaker. You’ll easily transfer the output of every stage in and out of SageMaker as your business requirements dictate.

Q: Is R supported with Amazon SageMaker?

Answer: Yes, R is supported with Amazon SageMaker.

Related References

• AWS Certified Machine Learning Specialty: All You Need To Know
• AWS Database Services – Amazon RDS, Aurora, DynamoDB, ElastiCache
• Multi-Account Management Using AWS Organizations
• AWS Certified Solutions Architect: Roles & Responsibilities
• Amazon Kinesis Overview, Features And Benefits
• AWS Route 53 Introduction
• Create And Connect To Amazon AWS Windows EC2 Instance
• AWS Certified Machine Learning Specialty: All You Need To Know

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