AWS Machine Learning tool Series — B1

AWS Services:

  1. Chatbots and virtual assistants from AWS help streamline self-service operations and lower operational expenses.
  2. AWS collects data from siloed and unstructured sources across your organization to boost company productivity and customer happiness.
  3. With web pages personalized to individual users, AWS can help you increase consumer engagement and conversion rates.
  4. Customers may extract text and data from nearly any document, such as loan applications and medical paperwork, using AWS without the need for manual labor.

Tool 01: Machine Learning SageMaker:

SageMaker is a fully managed platform that allows developers and data scientists to construct, train, and deploy machine learning models at any scale rapidly and with ease. It removes all of the roadblocks that developers have when using machine learning. Most developers find machine learning to be more difficult than it should be since the process of creating and training models, and deploying them into production, is both complicated and slow.

Manual Machine Learning step by step:

  1. To determine which components of your data set are necessary, you must first gather and prepare your training data.
  2. After that, you must decide which algorithm and framework to employ.
  3. After you’ve decided on a strategy, you’ll need to train the model to make predictions, which will take a lot of time and resources.
  4. The model must be optimized to produce the best possible predictions and then integrated and deployed on scalable infrastructure.
  5. All of this necessitates a high level of specialized knowledge, access to enormous amounts of computing and storage, and a significant amount of time to test and optimize each step.
  6. Finally, it’s not surprising that most developers find the whole process prohibitively expensive.

Tool 02: SageMaker Ground Truth:

SageMaker Ground Truth enables you to quickly create highly accurate machine learning training datasets. SageMaker Ground Truth gives public and private human labelers simple access to typical labeling activities and includes built-in workflows and interfaces. Additionally, automatic labeling, which works by teaching Ground Truth using data labeled by people so that the service learns to label data independently, can reduce your labeling costs by up to 70%.


  1. Amazon SageMaker Ground Truth decreases the time and effort required to produce training datasets. Machine learning in ground truth automatically categorizes data, resulting in cost savings.
  2. The labeling model will automatically assign labels to the raw data if it has high confidence in its results based on what it has learned so far. If the labeling model’s accuracy is low, it passes the data to humans to label.

Tool 03: Amazon Comprehend:

In your unstructured data, there lies a gold mine of opportunity. Customer emails, service requests, product reviews, social media, and even ad copy provide information on customer sentiment that can benefit your company. The question is, how do you get your hands on it? Machine learning, it turns out, is particularly adept at finding individual objects of interest within large swaths of text and can understand the sentiment concealed within language on an almost infinite scale. AWS provides an answer to this query in the form of Amazon Comprehend.


  1. The service recognizes the text’s language extracts key phrases, places, people, companies, or events.
  2. Moreover, it determines if the material is positive or negative, analyses text using tokenization and parts of speech, and organizes a collection of text files by topic automatically.
  3. You can also leverage Amazon Comprehend’s AutoML features to create a bespoke collection of entities or text categorization models that are tailored to your company’s needs.



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Hiba Rajput

Hiba Rajput

I’m a technical content writer with over 2+ years of experience. My love for writing has compelled me to write on diverse topics, especially on Tech. Follow me!