Amazon Web Services (AWS) provides a number of ML/AI services to ease the lifecycle from development to deployment. It supports use cases across various industries such as healthcare, retail, and manufacturing. The services are built for various domains of AI such as Computer Vision, Natural Language Processing, and Machine Learning.
ML/AI services by AWS
Amazon Web Services (AWS) provides a number of ML/AI services to ease the lifecycle from development to deployment. It supports use cases across various industries such as healthcare, retail, and manufacturing. The services are built for various domains of AI such as Computer Vision, Natural Language Processing, and Machine Learning. The accuracy of solutions and the advantage that these services can be applied to different frameworks, operating systems, and deployment platforms is the driving force for gaining popularity and trust among leading multinational clients.
This service provides functionalities to build, train and deploy machine learning models. Various tools, workflows, and infrastructure are available to build the end-to-end ML lifecycle.
Benefits – Easily deal with large amounts of data, accelerated ML development with reduced training time, streamlines the MLOps processes to handle models at scale.
Supported frameworks/programming languages – Jupyter, TensorFlow, PyTorch, mxnet, Hugging Face, scikit-learn, python, R.
This is a service that provides tools to create high-quality data for your machine learning models. It accepts raw input in the form of either image, text file, or video and generates labels on it. There are two main services that perform data labeling – Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth.
Amazon SageMaker Ground Truth Plus creates and manages all the data labeling workflows on itself. It reduces the cost of data labeling by 40%.
Amazon SageMaker Ground Truth allows you to build and manage your data labeling workflows manually. One can choose human annotators from Amazon Mechanical Turk or from one’s own workforce.
This service automatically optimizes the machine learning models for inference on cloud or edge devices. To suit a particular deployment platform, developers usually spend a lot of time optimizing the model so Amazon SageMaker Neo can come to your rescue. It also claims to improve your model performance by 25 times.
Working of Amazon SageMaker Neo:
Best features of Amazon SageMaker Neo:
⦁ It optimizes the model by speeding up the inference without affecting or reducing the accuracy of the model.
⦁ It supports the popular machine learning frameworks for model development as well as supports the popular target platforms for model deployment.
⦁ It provides a runtime environment that acquires significantly less storage and memory as compared to the framework.
Machine Learning solutions often require the expertise of humans in addition to machines to give the best outputs. Human feedback is necessary for the systems containing sensitive data, and those which have a requirement for re-training or updating the model based on the user requirements. That’s where Amazon A2I works. Amazon A2I eases the process of building workflows for human review. It gives the benefit of incorporating human feedback into the existing Machine Learning pipeline without hassles. Its most common use cases are in healthcare and financial services where one has to deal with sensitive documents with extreme care as the consequences of errors can be huge.
Working of Amazon A2I:
This service is mainly for assisting the image and video analysis in machine learning. It provides pretrained models and customizable capabilities which can be used for several applications without building the code and infrastructure from scratch. Some of the features that this service provides are content moderation, face compare and search, face detection and analysis, text detection, celebrity recognition, and video segment detection.
This service is built for identifying the defects in manufactured products such as missing product components or a compromise in quality control. It automates the process of inspection.
AWS Panorama is a service built for utilizing computer vision services on the edge. It integrates computer vision with IP cameras on the premises. It allows the processing of predictions on the local device with very low latency and great accuracy.
Automatically extracts information such as tables, text, data values and handwriting from the documents. It provides smart functionalities beyond what OCR provides with the use of machine learning.
This service uses Natural Language Processing (NLP) to derive smart insights and analysis from the documents.
This service allows hassle-free creation of conversational assistants – chatbots and virtual agents which can understand the content and its context in multiple languages.
It automatically converts the speech to text. It has many use cases to identify important information such as in video calls, meetings, and voice messages.
This service converts text to speech. It contains a myriad of natural sounding humanlike voices in multiple languages which can be used to build speech-enabled applications.
In this blog, we looked at the several services in ML and AI domains which are offered by AWS. These services heavily use Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision to cater to various applications of the industry. AWS ML/AI services are trusted by some of the renowned multinational companies across the world. Consult the best AI development company in India, to avail these services.