ALL Machine Learning Services in AWS

Working Backward With AWS ML Service

Disney, Capital One, Digital Global, Epiq, Autodesk, and BuildFax have something in common. It’s not about their business or marketing policies but about improving their business by incorporating machine learning (ML) models.

ML and DL (Deep Learning) have an impact on all possible industries. There is not a single corner of business not touched by these technologies. It makes the technology more available to people, so they concentrate and explore historical data to innovate things. From forecasting, building product designs, help in transcribing and transcription, AWS ML services are helping the industries to the server world.

Firstly, ML requires a secured and huge data center to store the data. AWS stores historical data that’s necessary for the models to continue learning and performing.

Secondly, it keeps you connected with the system 24hrs, even when you are disconnected, Don’t get me wrong! ML models require time to analyze the data. It ideally requires 2–10 hr to train itself, depending on the algorithms, models, and files that are queued. AWS is the best choice since the self-learning model works on AWS cloud infrastructure.

Finally, AWS provides multiple services for your ML requirements, there are more than 15 ML services, and some services don’t require coding or training. We’ll see the most used Amazon ML solutions that drive innovation in the business model.

  1. SageMaker:

Build for a data scientist, its fully-managed ML platform assists in preparing, building, training, and deploying ML models. It quickly brings the board set of capabilities of ML into a single unified visual user interface. By allowing you to collect and prepare your training data, the comprehensive integrated system detects essential elements of your training data set. SageMaker makes it easy to select the algorithms and frameworks required to train the model. Since ML modeling needs the expertise to access an enormous amount of data to compute and store, it’s a nightmare for data scientists as they integrate their model into their application and run full scale.

SageMaker removes the complexity of each step by introducing modules that are independent but used in conjunction with each other.

2. SageMaker Ground Truth:

Reduce your time and effort for labeling the objects in the video with SageMaker ground truth. Labeling objects to multiple frames of videos is impossible unless you work with SageMaker Ground truth. The accurately trained custom or built-in data labeling workflows identifies and labels your objects in a given image or video. Generally, applied in Computer Vision (CV) applications, it includes 3D point clouds, removal of distortion, 3D cuboid snapping for videos as additional features.

3. Amazon Comprehend:

ML is here to make things complex! Don’t worry if you are not aware of programming languages. Amazon Comprehend is an NLP (Natural Language Processing) used for text like email, support tickets, social media, or reviews that assist in business growth. It uncovers the relationship between the text, phrases, speech, etc. Similarly, Amazon Comprehend Medical works to extract the medical phrases like medical conditions, medications, dosages treatment, and patient health.

Amazon Comprehend Medical

4. Amazon Lex:

What to build an Alexa? Amazon Lex is what you need. It builds a conversational interface using voice and text. It converts speech to natural text using automatic speech recognition same deep learning technology used in Alexa. You can have new categories of products similar to Alexa.

5. Amazon Polly:

A cost-effective way of converting text to speech, Amazon Polly turns text into lifelike speech. Create speech-enabled applications that sound like humans. With options of 47 lifelike voices and 24 languages, it is already in work in various countries. Caching, saving, and run on exact time, make it user-friendly and cost-effective.

6. Amazon Rekognition:

What to search an object in an image, Amazon Rekognition is the one that could scan several images to label them. Doesn’t it sound familiar to SageMaker Ground Truth, but here is the difference, Amazon Recognition works only on images to build powerful visual search.

7. Amazon Translate:

The day is not far when all would take a translator to understand and respond to foreign languages. Amazon translates neural machine translate. The high-quality localized content works efficiently to translate large volumes of text.

8. Amazon Transcribe:

Generating subtitles to videos or generating medical transcriptions given by doctors is now easy with Amazon Transcribe. The APIs analyze your speech of video and returns the text format speech. Commonly used in many applications the Amazon transcribe is used to transcribe the customer service calls, or integrated into education to covert the speech of audio into text formats.

9. Amazon Elastic Inference:

As a Data scientist, you worry about GPU instances and compute cost. The inference is a process used to make predictions that drives 90% of computational cost. Knowing the right amount of GPU instances to train your model is necessary to reduce computing costs. Each model requires different amounts of GPU, memory resources, and GPU. There is always a problem with attaching the right amount of GPU-powered inference. Amazon Elastic helps you to reduce the cost of inference by 75%, and also supports the models of TensorFlow, Apache MXNet, and ONNX.

10. Amazon Forecast:

From decays, the business strategy relies on the analysis of time-series data. Forecasting business or sales uses the spreadsheets with the formulas attached. Working on the spreadsheet isn’t bad, but it’s troublesome to retrieve the last updates on a corrupted file, moreover, it’s impossible to find the relationship between the additional parameters in time series data.

Amazon Forecast combines the ML models and your additional parameters, making an impact on your decisions. A fully managed service does not require you to train models or attach servers. Direct use of service provides you with analysis and allows you to pay for what is used.

Software Engineer @ Big 4 | public speaker | voracious learner | Zealous Open-Source Advocate.