** AWS & AI Research Specialist-Principal Applied AI Product Engineer [Product-Owner] & Enterprise Architect @PepsiCo | IIM-A | Community Member @Landing.AI | AI Research Specialist [Portfolio] | Author | Quantum AI | Mojo | Next JS | 7+ Years of Experience in Fortune 50 Product Companies | **
** Global Top AI Community Member @Landing.AI @MLOPS Community, @Pandas AI, @Full Stack Deep Learning, @humaneai @H2o.ai Generative AI, @Modular & @Cohere AI @hugging Face Research Papers Group @Papers with Code @DAIR.AI ** ** Completed 90+ Online Technical Paid Courses from Udemy & Coursera as I believe in Continuous Learning and Growth Mindset **
** AWS AI Research Scientist **
| AWS Quantum AI CATEGORY | AWS SERVICES SUMMARIES | RESOURCE LINKS |
|---|---|---|
| 1. Quantum technologies - Amazon Bracket | Amazon Braket is a fully managed quantum computing service that helps researchers and developers get started with the technology to accelerate research and discovery. Amazon Braket provides a development environment for you to explore and build quantum algorithms, test them on quantum circuit simulators, and run them on different quantum hardware technologies.Quantum computing has the potential to solve computational problems that are beyond the reach of classical computers by harnessing the laws of quantum mechanics to process information in new ways. This approach to computing could transform areas such as chemical engineering, material science, drug discovery, financial portfolio optimization, and machine learning. But defining those problems and programming quantum computers to solve them requires new skills, which are difficult to acquire without easy access to quantum computing hardware.Amazon Braket overcomes these challenges so you can explore quantum computing. With Amazon Braket, you can design and build your own quantum algorithms from scratch or choose from a set of pre-built algorithms. Once you have built your algorithm, Amazon Braket provides a choice of simulators to test, troubleshoot and run your algorithms. When you are ready, you can run your algorithm on your choice of different quantum computers, and gate-based computers from Rigetti and IonQ. With Amazon Braket, you can now evaluate the potential of quantum computing for your organization, and build expertise. | Amazon Docs |
| 2. Robotics - AWS RoboMaker | AWS RoboMaker is a service that makes it easy to develop, test, and deploy intelligent robotics applications at scale. AWS RoboMaker extends the most widely used open-source robotics software framework, Robot Operating System (ROS), with connectivity to cloud services. This includes AWS machine learning services, monitoring services, and analytics services that enable a robot to stream data, navigate, communicate, comprehend, and learn. AWS RoboMaker provides a robotics development environment for application development, a robotics simulation service to accelerate application testing, and a robotics fleet management service for remote application deployment, update, and management.Robots are machines that sense, compute, and take action. Robots need instructions to accomplish tasks, and these instructions come in the form of applications that developers code to determine how the robot will behave. Receiving and processing sensor data, controlling actuators for movement, and performing a specific task are all functions that are typically automated by these intelligent robotics applications. Intelligent robots are being increasingly used in warehouses to distribute inventory, in homes to carry out tedious housework, and in retail stores to provide customer service. Robotics applications use machine learning in order to perform more complex tasks like recognizing an object or face, having a conversation with a person, following a spoken command, or navigating autonomously.Until now, developing, testing, and deploying intelligent robotics applications was difficult and time consuming. Building intelligent robotics functionality using machine learning is complex and requires specialized skills. Setting up a development environment can take each developer days and building a realistic simulation system to test an application can take months due to the underlying infrastructure needed. Once an application has been developed and tested, a developer needs to build a deployment system to deploy the application into the robot and later update the application while the robot is in use.AWS RoboMaker provides you with the tools to make building intelligent robotics applications more accessible, a fully managed simulation service for quick and easy testing, and a deployment service for lifecycle management. AWS RoboMaker removes the heavy lifting from each step of robotics development so you can focus on creating innovative robotics applications. | Amazon Docs |
| 3. Satellite - AWS Ground Station | AWS Ground Station is a fully managed service that lets you control satellite communications, downlink and process satellite data, and scale your satellite operations quickly, easily and cost-effectively without having to worry about building or managing your own ground station infrastructure. Satellites are used for a wide variety of use cases, including weather forecasting, surface imaging, communications, and video broadcasts. Ground stations are at the core of global satellite networks, which are facilities that provide communications between the ground and the satellites by using antennas to receive data and control systems to send radio signals to command and control the satellite. Today, you must either build your own ground stations and antennas, or obtain long-term leases with ground station providers, often in multiple countries to provide enough opportunities to contact the satellites as they orbit the globe. Once all this data is downloaded, you need servers, storage, and networking in close proximity to the antennas to process, store, and transport the data from the satellites.AWS Ground Station eliminates these problems by delivering a global ground station as a service. We provide direct access to AWS services and the AWS Global Infrastructure including our low-latency global fiber network right where your data is downloaded into our AWS Ground Station. This enables you to easily control satellite communications, quickly ingest and process your satellite data, and rapidly integrate that data with your applications and other services running in the AWS Cloud. For example, you can use Amazon S3 to store the downloaded data, Amazon Kinesis Data Streams for managing data ingestion from satellites, SageMaker for building custom machine learning applications that apply to your data sets, and Amazon EC2 to command and download data from satellites. AWS Ground Station can help you save up to 80% on the cost of your ground station operations by allowing you to pay only for the actual antenna time used, and relying on our global footprint of ground stations to download data when and where you need it, instead of building and operating your own global ground station infrastructure. There are no long-term commitments, and you gain the ability to rapidly scale your satellite communications on-demand when your business needs it. | Amazon Docs |
| 4. **AWS Architecture Center ** | **AWS Well-Architected Framework ** | Amazon Docs |
| 5. **AWS Whitepapers & Guide ** | **AWS Whitepapers & Guide ** | Amazon Docs |
| 6. **Cloud Foundations ** | **Cloud Foundations ** | Amazon Docs |
| AWS AI CATEGORY | AWS SERVICES SUMMARIES | RESOURCE LINKS |
|---|---|---|
| 1. Machine Learning (ML) and Artificial Intelligence | Machine Learning (ML) and Artificial Intelligence (AI). | Amazon Docs |
| 2. **AWS Cloud Complete Bootcamp Course. ** | AWS Cloud Complete Bootcamp Course. | Amazon Docs |
| 3. **Generative AI for every business. ** | Generative AI for every business. | Amazon Docs |
| 4. **Amazon Web Services AI Services. ** | Amazon Web Services AI Services. | Amazon Docs |
| 5. **Amazon-Q-Generative AI Assistant. ** | Amazon-Q-Generative AI Assistant. | Amazon Docs |
| 6. **Amazon Augmented AI. ** | Amazon Augmented AI (Amazon A2I) is a ML service which makes it easy to build the workflows required for human review. Amazon A2I brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers, whether it runs on AWS or not. | Amazon Docs |
| 7. **Amazon Bedrock. ** | Amazon Bedrock is a fully managed service that makes foundational models (FMs) from Amazon and leading AI startups available through an API. With the Amazon Bedrock serverless experience, you can quickly get started, experiment with FMs, privately customize them with your own data, and seamlessly integrate and deploy FMs into your AWS applications.You can choose from a variety of foundation models, including Amazon Titan, Claude 2 from Anthropic, Command and Embed from Cohere, Jurassic-2 from AI21 Studio, and Stable Diffusion from Stability AI. | Amazon Docs |
| 8. **Amazon CodeGuru. ** | Amazon CodeGuru is a developer tool that provides intelligent recommendations to improve code quality and identify an application’s most expensive lines of code. Integrate CodeGuru into your existing software development workflow to automate code reviews during application development and continuously monitor application's performance in production and provide recommendations and visual clues on how to improve code quality, application performance, and reduce overall cost. | Amazon Docs |
| 9. **Amazon CodeWhisperer. ** | Designed to improve developer productivity, Amazon CodeWhisperer provides ML–powered code recommendations to accelerate development of C#, Java, JavaScript, Python, and TypeScript applications. The service integrates with multiple integrated development environments (IDEs), including JetBrains (IntelliJ IDEA, PyCharm, WebStorm, and Rider), Visual Studio Code, AWS Cloud9, and the AWS Lambda console, and helps developers write code faster by generating entire functions and logical blocks of code—often consisting of more than 10–15 lines of code. | Amazon Docs |
| 10. ** Amazon Comprehend. ** | Amazon Comprehend uses ML and natural language processing (NLP) to help you uncover the insights and relationships in your unstructured data. The service identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; analyzes text using tokenization and parts of speech; and automatically organizes a collection of text files by topic. You can also use AutoML capabilities in Amazon Comprehend to build a custom set of entities or text classification models that are tailored uniquely to your organization’s needs.For extracting complex medical information from unstructured text, you can use Amazon Comprehend Medical. The service can identify medical information, such as medical conditions, medications, dosages, strengths, and frequencies from a variety of sources like doctor’s notes, clinical trial reports, and patient health records. Amazon Comprehend Medical also identifies the relationship among the extracted medication and test, treatment and procedure information for easier analysis. For example, the service identifies a particular dosage, strength, and frequency related to a specific medication from unstructured clinical notes. | Amazon Docs |
| 11. **Amazon DevOps Guru. ** | Amazon DevOps Guru is an ML-powered service that makes it easy to improve an application’s operational performance and availability. Amazon DevOps Guru detects behaviors that deviate from normal operating patterns so you can identify operational issues long before they impact your customers. | Amazon Docs |
| 12. **Amazon Forecast. ** | Amazon Forecast is a fully managed service that uses ML to deliver highly accurate forecasts. | Amazon Docs |
| 13. **Amazon Fraud Detector. ** | Amazon Fraud Detector is a fully managed service that uses ML and more than 20 years of fraud detection expertise from Amazon, to identify potentially fraudulent activity so customers can catch more online fraud faster. Amazon Fraud Detector automates the time consuming and expensive steps to build, train, and deploy an ML model for fraud detection, making it easier for customers to leverage the technology. Amazon Fraud Detector customizes each model it creates to a customer’s own dataset, making the accuracy of models higher than current one-size fits all ML solutions. And, because you pay only for what you use, you avoid large upfront expenses.. | Amazon Docs |
| 14. **Amazon Comprehend Medical. ** | Over the past decade, AWS has witnessed a digital transformation in health, with organizations capturing huge volumes of patient information every day. But this data is often unstructured and the process to extract this information is labor-intensive and error-prone. Amazon Comprehend Medical is a HIPAA-eligible natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text, such as prescriptions, procedures, or diagnoses. Amazon Comprehend Medical can help you extract information from unstructured medical text accurately and quickly with medical ontologies like ICD-10-CM, RxNorm, and SNOMED CT and in turn accelerate insurance claim processing, improve population health, and accelerate pharmacovigilance. | Amazon Docs |
| 15. **Amazon Kendra. ** | Amazon Kendra is an intelligent search service powered by ML. Amazon Kendra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they are looking for, even when it’s scattered across multiple locations and content repositories within your organization.. | Amazon Docs |
| 16. ** Amazon Lex. ** | Amazon Lex is a fully managed artificial intelligence (AI) service to design, build, test, and deploy conversational interfaces into any application using voice and text. Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions, and create new categories of products. | Amazon Docs |
| 17. ** Amazon Lookout for Equipment. ** | analyzes the data from the sensors on your equipment (such as pressure in a generator, flow rate of a compressor, revolutions per minute of fans), to automatically train an ML model based on just your data, for your equipment – with no ML expertise required. | Amazon Docs |
| 18. ** Amazon Lookout for Metrics. ** | Amazon Lookout for Metrics uses ML to automatically detect and diagnose anomalies (outliers from the norm) in business and operational data, such as a sudden dip in sales revenue or customer acquisition rates. In a couple of clicks, you can connect Amazon Lookout for Metrics to popular data stores such as Amazon S3, Amazon Redshift, and Amazon Relational Database Service (Amazon RDS), as well as third-party Software as a Service (SaaS) applications, such as Salesforce, Servicenow, Zendesk, and Marketo, and start monitoring metrics that are important to your business. Amazon Lookout for Metrics automatically inspects and prepares the data from these sources to detect anomalies with greater speed and accuracy than traditional methods used for anomaly detection. You can also provide feedback on detected anomalies to tune the results and improve accuracy over time. Amazon Lookout for Metrics makes it easy to diagnose detected anomalies by grouping together anomalies that are related to the same event and sending an alert that includes a summary of the potential root cause. It also ranks anomalies in order of severity so that you can prioritize your attention to what matters the most to your business. | Amazon Docs |
| 19. ** Amazon Lookout for Vision. ** | Amazon Lookout for Vision is an ML service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. For example, Amazon Lookout for Vision can be used to identify missing components in products, damage to vehicles or structures, irregularities in production lines, miniscule defects in silicon wafers, and other similar problems. Amazon Lookout for Vision uses ML to see and understand images from any camera as a person would, but with an even higher degree of accuracy and at a much larger scale. Amazon Lookout for Vision allows customers to eliminate the need for costly and inconsistent manual inspection, while improving quality control, defect and damage assessment, and compliance. In minutes, you can begin using Amazon Lookout for Vision to automate inspection of images and objects – with no ML expertise required.. | Amazon Docs |
| 20. ** Amazon Amazon Monitron. ** | **Amazon Monitron is an end-to-end system that uses ML to detect abnormal behavior in industrial machinery, enabling you to implement predictive maintenance and reduce unplanned downtime. | |
| Installing sensors and the necessary infrastructure for data connectivity, storage, analytics, and alerting are foundational elements for enabling predictive maintenance. However, to make it work, companies have historically needed skilled technicians and data scientists to piece together a complex solution from scratch. This included identifying and procuring the right type of sensors for their use cases and connecting them together with an IoT gateway (a device that aggregates and transmits data). As a result, few companies have been able to successfully implement predictive maintenance.Amazon Monitron includes sensors to capture vibration and temperature data from equipment, a gateway device to securely transfer data to AWS, the Amazon Monitron service that analyzes the data for abnormal machine patterns using ML, and a companion mobile app to set up the devices and receive reports on operating behavior and alerts to potential failures in your machinery. You can start monitoring equipment health in minutes without any development work or ML experience required, and enable predictive maintenance with the same technology used to monitor equipment in Amazon Fulfillment Centers.** | Amazon Docs | |
| 21. ** Amazon Personalize. ** | **Amazon Personalize is an ML service that makes it easy for developers to create individualized recommendations for customers using their applications. | |
| ML is increasingly used to improve customer engagement by powering personalized product and content recommendations, tailored search results, and targeted marketing promotions. However, developing the ML capabilities necessary to produce these sophisticated recommendation systems has been beyond the reach of most organizations today due to the complexity of developing ML functionality. Amazon Personalize allows developers with no prior ML experience to easily build sophisticated personalization capabilities into their applications, using ML technology perfected from years of use on Amazon.com. | ||
| With Amazon Personalize, you provide an activity stream from your application – page views, signups, purchases, and so forth – as well as an inventory of the items you want to recommend, such as articles, products, videos, or music. You can also choose to provide Amazon Personalize with additional demographic information from your users such as age, or geographic location. Amazon Personalize processes and examines the data, identifies what is meaningful, selects the right algorithms, and trains and optimizes a personalization model that is customized for your data.Amazon Personalize offers optimized recommenders for retail and media and entertainment that make it faster and easier to deliver high-performing personalized user experiences. Amazon Personalize also offers intelligent user segmentation so you can run more effective prospecting campaigns through your marketing channels. With our two new recipes, you can automatically segment your users based on their interest in different product categories, brands, and more.All data analyzed by Amazon Personalize is kept private and secure, and only used for your customized recommendations. You can start serving your personalized predictions via a simple API call from inside the virtual private cloud that the service maintains. You pay only for what you use, and there are no minimum fees and no upfront commitments. | ||
| Amazon Personalize is like having your own Amazon.com ML personalization team at your disposal, 24 hours a day.** | Amazon Docs | |
| 22. ** Amazon Polly. ** | **Amazon Polly is a service that turns text into lifelike speech. Amazon Polly lets you create applications that talk, enabling you to build entirely new categories of speech-enabled products. Amazon Polly is an Amazon artificial intelligence (AI) service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice. Amazon Polly includes a wide selection of lifelike voices spread across dozens of languages, so you can select the ideal voice and build speech-enabled applications that work in many different countries. | |
| Amazon Polly delivers the consistently fast response times required to support real-time, interactive dialog. You can cache and save Amazon Polly speech audio to replay offline or redistribute. And Amazon Polly is easy to use. You simply send the text you want converted into speech to the Amazon Polly API, and Amazon Polly immediately returns the audio stream to your application so your application can play it directly or store it in a standard audio file format, such as MP3.In addition to Standard TTS voices, Amazon Polly offers Neural Text-to-Speech (NTTS) voices that deliver advanced improvements in speech quality through a new machine learning approach. Polly’s Neural TTS technology also supports a Newscaster speaking style that is tailored to news narration use cases. Finally, Amazon Polly Brand Voice can create a custom voice for your organization. This is a custom engagement where you will work with the Amazon Polly team to build an NTTS voice for the exclusive use of your organization.With Amazon Polly, you pay only for the number of characters you convert to speech, and you can save and replay Amazon Polly generated speech. The Amazon Polly low cost per character converted, and lack of restrictions on storage and reuse of voice output, make it a cost-effective way to enable Text-to-Speech everywhere.** | Amazon Docs | |
| 23. ** Amazon Rekognition. ** | Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no ML expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. Amazon Rekognition also provides highly accurate facial analysis and facial search capabilities that you can use to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases.With Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs. For example, you can build a model to classify specific machine parts on your assembly line or to detect unhealthy plants. Amazon Rekognition Custom Labels takes care of the heavy lifting of model development for you, so no ML experience is required. You simply need to supply images of objects or scenes you want to identify, and the service handles the rest. | Amazon Docs |
| 24. ** Amazon SageMaker Autopilot. ** | Amazon SageMaker Autopilot automatically builds, trains, and tunes the best ML models based on your data, while allowing you to maintain full control and visibility. With SageMaker Autopilot, you simply provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification). SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality. | Amazon Docs |
| 25. ** Amazon SageMaker Canvas. ** | Amazon SageMaker Canvas expands access to ML by providing business analysts with a visual point-and-click interface that allows them to generate accurate ML predictions on their own — without requiring any ML experience or having to write a single line of code. | Amazon Docs |
| 26. ** Amazon SageMaker Clarify. ** | Amazon SageMaker Clarify provides machine learning developers with greater visibility into their training data and models so they can identify and limit bias and explain predictions. Amazon SageMaker Clarify detects potential bias during data preparation, after model training, and in your deployed model by examining attributes you specify. SageMaker Clarify also includes feature importance graphs that help you explain model predictions and produces reports which can be used to support internal presentations or to identify issues with your model that you can take steps to correct. | Amazon Docs |
| 27. ** Amazon SageMaker Data Labeling. ** | Amazon SageMaker provides data labeling offerings to identify raw data, such as images, text files, and videos, and add informative labels to create high-quality training datasets for your ML models. | Amazon Docs |
| 28. ** Amazon SageMaker Data Wrangler. ** | WAmazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for ML from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. | Amazon Docs |
| 29. ** Amazon SageMaker Edge. ** | Amazon SageMaker Edge enables machine learning on edge devices by optimizing, securing, and deploying models to the edge, and then monitoring these models on your fleet of devices, such as smart cameras, robots, and other smart-electronics, to reduce ongoing operational costs. SageMaker Edge Compiler optimizes the trained model to be executable on an edge device. SageMaker Edge includes an over-the-air (OTA) deployment mechanism that helps you deploy models on the fleet independent of the application or device firmware. SageMaker Edge Agent allows you to run multiple models on the same device. The Agent collects prediction data based on the logic that you control, such as intervals, and uploads it to the cloud so that you can periodically retrain your models over time. | Amazon Docs |
| 30. ** # Amazon SageMaker Feature Store. ** | Amazon SageMaker Feature Store is a purpose-built repository where you can store and access features so it’s much easier to name, organize, and reuse them across teams. SageMaker Feature Store provides a unified store for features during training and real-time inference without the need to write additional code or create manual processes to keep features consistent. SageMaker Feature Store keeps track of the metadata of stored features (such as feature name or version number) so that you can query the features for the right attributes in batches or in real time using Amazon Athena, an interactive query service. SageMaker Feature Store also keeps features updated, because as new data is generated during inference, the single repository is updated so new features are always available for models to use during training and inference. | Amazon Docs |
| 31. ** Amazon SageMaker geospatial capabilities. ** | Amazon SageMaker geospatial capabilities make it easier for data scientists and machine learning (ML) engineers to build, train, and deploy ML models faster using geospatial data. You have access to data (open-source and third-party), processing, and visualization tools to make it more efficient to prepare geospatial data for ML. You can increase your productivity by using purpose-built algorithms and pre-trained ML models to speed up model building and training, and use built-in visualization tools to explore prediction outputs on an interactive map and then collaborate across teams on insights and results. | Amazon Docs |
| 32. ** Amazon SageMaker JumpStart. ** | SageMaker JumpStart provides pretrained, open-source models for a wide range of problem types to help you get started with machine learning. You can incrementally train and tune these models before deployment. JumpStart also provides solution templates that set up infrastructure for common use cases, and executable example notebooks for machine learning with SageMaker.* | Amazon Docs |
| 33. ** Amazon SageMaker Model Building. ** | Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case. In Amazon SageMaker you can pick different algorithms, including over 15 that are built-in and optimized for SageMaker, and use over 150 pre-built models from popular model zoos available with a few clicks. SageMaker also offers a variety of model building tools, including Amazon SageMaker Studio Notebooks and RStudio, where you can run ML models on a small scale to see results and view reports on their performance so you can come up with high-quality working prototypes. | Amazon Docs |
| 34. ** Amazon SageMaker Model Training. ** | AAmazon SageMaker reduces the time and cost to train and tune ML models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, you can use the Amazon SageMaker distributed training libraries for better performance or use third-party libraries such as DeepSpeed, Horovod, or Megatron. | Amazon Docs |
| 35. ** Amazon SageMaker Model Deployment. ** | Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case. It provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. | Amazon Docs |
| 36. ** Amazon SageMaker Pipelines. ** | AAmazon SageMaker reduces the time and cost to train and tune ML models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, you can use the Amazon SageMaker distributed training libraries for better performance or use third-party libraries such as DeepSpeed, Horovod, or Megatron. | Amazon Docs |
| 37. ** Amazon SageMaker Studio Lab. ** | Amazon SageMaker Studio Lab is a free ML development environment that provides the compute, storage (up to 15GB), and security—all at no cost—for anyone to learn and experiment with ML. All you need to get started is a valid email address—you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later. | Amazon Docs |
| 38. ** Apache MXNet on AWS. ** | With Hugging Face on Amazon SageMaker, you can deploy and fine-tune pre-trained models from Hugging Face, an open-source provider of natural language processing (NLP) models known as Transformers, reducing the time it takes to set up and use these NLP models from weeks to minutes. NLP refers to ML algorithms that help computers understand human language. They help with translation, intelligent search, text analysis, and more. However, NLP models can be large and complex (sometimes consisting of hundreds of millions of model parameters), and training and optimizing them requires time, resources, and skill. AWS collaborated with Hugging Face to create Hugging Face AWS Deep Learning Containers (DLCs), which provide data scientists and ML developers a fully managed experience for building, training, and deploying state-of-the-art NLP models on Amazon SageMaker. | Amazon Docs |
| 39. ** Apache MXNet on AWS. ** | Apache MXNet is a fast and scalable training and inference framework with an easy-to-use, concise API for ML. MXNet includes the Gluon interface that allows developers of all skill levels to get started with deep learning on the cloud, on edge devices, and on mobile apps. In just a few lines of Gluon code, you can build linear regression, convolutional networks and recurrent LSTMs for object detection, speech recognition, recommendation, and personalization. You can get started with MxNet onAWS with a fully-managed experience using Amazon SageMaker, a platform to build, train, and deploy ML models at scale. Or, you can use the AWS Deep Learning AMIs to build custom environments and workflows with MxNet as well as other frameworks including TensorFlow, PyTorch, Chainer, Keras, Caffe, Caffe2, and Microsoft Cognitive Toolkit. | Amazon Docs |
| 40. ** AWS Deep Learning AMIs. ** | AThe AWS Deep Learning AMI provide ML practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at any scale. You can quickly launch Amazon EC2 instances pre-installed with popular deep learning frameworks and interfaces such as TensorFlow, PyTorch, Apache MXNet, Chainer, Gluon, Horovod, and Keras to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques. Whether you need Amazon EC2 GPU or CPU instances, there is no additional charge for the Deep Learning AMIs – you only pay for the AWS resources needed to store and run your applications.. | Amazon Docs |
| 41. ** Hugging Face on AWS. ** | With Hugging Face on Amazon SageMaker, you can deploy and fine-tune pre-trained models from Hugging Face, an open-source provider of natural language processing (NLP) models known as Transformers, reducing the time it takes to set up and use these NLP models from weeks to minutes. NLP refers to ML algorithms that help computers understand human language. They help with translation, intelligent search, text analysis, and more. However, NLP models can be large and complex (sometimes consisting of hundreds of millions of model parameters), and training and optimizing them requires time, resources, and skill. AWS collaborated with Hugging Face to create Hugging Face AWS Deep Learning Containers (DLCs), which provide data scientists and ML developers a fully managed experience for building, training, and deploying state-of-the-art NLP models on Amazon SageMaker. | Amazon Docs |
| 42. ** PyTorch on AW. ** | PyTorch is an open-source deep learning framework that makes it easy to develop machine learning models and deploy them to production. Using TorchServe, PyTorch's model serving library built and maintained by AWS in partnership with Facebook, PyTorch developers can quickly and easily deploy models to production. PyTorch also provides dynamic computation graphs and libraries for distributed training, which are tuned for high performance on AWS. You can get started with PyTorch on AWS using Amazon SageMaker, a fully managed ML service that makes it easy and cost-effective to build, train, and deploy PyTorch models at scale. If you prefer to manage the infrastructure yourself, you can use the AWS Deep Learning AMIs or the AWS Deep Learning Containers, which come built from source and optimized for performance with the latest version of PyTorch to quickly deploy custom machine learning environments. | Amazon Docs |
| 43. ** TensorFlow on AWS. ** | TensorFlow is one of many deep learning frameworks available to researchers and developers to enhance their applications with machine learning. AWS provides broad support for TensorFlow, enabling customers to develop and serve their own models across computer vision, natural language processing, speech translation, and more. You can get started with TensorFlow on AWS using Amazon SageMaker, a fully managed ML service that makes it easy and cost-effective to build, train, and deploy TensorFlow models at scale. If you prefer to manage the infrastructure yourself, you can use the AWS Deep Learning AMIs or the AWS Deep Learning Containers, which come built from source and optimized for performance with the latest version of TensorFlow to quickly deploy custom ML environments. | Amazon Docs |
| 44. ** Amazon Textract. ** | Amazon Textract is a service that automatically extracts text and data from scanned documents. Amazon Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables.Today, many companies manually extract data from scanned documents such as PDFs, images, tables, and forms, or through simple OCR software that requires manual configuration (which often must be updated when the form changes). To overcome these manual and expensive processes, Amazon Textract uses ML to read and process any type of document, accurately extracting text, handwriting, tables, and other data with no manual effort. Amazon Textract provides you with the flexibility to specify the data you need to extract from documents using queries. You can specify the information you need in the form of natural language questions (such as “What is the customer name”). You do not need to know the data structure in the document (table, form, implied field, nested data) or worry about variations across document versions and formats. Amazon Textract Queries are pre-trained on a large variety of documents including paystubs, bank statements, W-2s, loan application forms, mortgage notes, claims documents, and insurance cards.With Amazon Textract, you can quickly automate document processing and act on the information extracted, whether you’re automating loans processing or extracting information from invoices and receipts. Amazon Textract can extract the data in minutes instead of hours or days. Additionally, you can add human reviews with Amazon Augmented AI to provide oversight of your models and check sensitive data. | Amazon Docs |
| 45. ** Amazon Transcribe. ** | **Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for customers to automatically convert speech to text. The service can transcribe audio files stored in common formats, like WAV and MP3, with time stamps for every word so that you can easily locate the audio in the original source by searching for the text. You can also send a live audio stream to Amazon Transcribe and receive a stream of transcripts in real time. Amazon Transcribe is designed to handle a wide range of speech and acoustic characteristics, including variations in volume, pitch, and speaking rate. The quality and content of the audio signal (including but not limited to factors such as background noise, overlapping speakers, accented speech, or switches between languages within a single audio file) may affect the accuracy of service output. Customers can choose to use Amazon Transcribe for a variety of business applications, including transcription of voice-based customer service calls, generation of subtitles on audio/video content, and conduct (text based) content analysis on audio/video content.Two very important services derived from Amazon Transcribe include Amazon Transcribe Medical and Amazon Transcribe Call Analytics.Amazon Transcribe Medical uses advanced ML models to accurately transcribe medical speech into text. Amazon Transcribe Medical can generate text transcripts that can be used to support a variety of use cases, spanning clinical documentation workflow and drug safety monitoring (pharmacovigilance) to subtitling for telemedicine and even contact center analytics in the healthcare and life sciences domains.Amazon Transcribe Call Analytics is an AI-powered API that provides rich call transcripts and actionable conversation insights that you can add into their call applications to improve customer experience and agent productivity. It combines powerful speech-to-text and custom natural language processing (NLP) models that are trained specifically to understand customer care and outbound sales calls. As a part of AWS Contact Center Intelligence (CCI) solutions, this API is contact center agnostic and makes it easy for customers and ISVs to add call analytics capabilities into their applications. | |
| The easiest way to get started with Amazon Transcribe is to submit a job using the console to transcribe an audio file. You can also call the service directly from the AWS Command Line Interface, or use one of the supported SDKs of your choice to integrate with your applications.** | Amazon Docs | |
| 46. ** Amazon Translate ** | Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Neural machine translation is a form of language translation automation that uses deep learning models to deliver more accurate and more natural sounding translation than traditional statistical and rule-based translation algorithms. Amazon Translate allows you to localize content such as websites and applications for your diverse users, easily translate large volumes of text for analysis, and efficiently enable cross-lingual communication between users. | Amazon Docs |
| 47. ** AWS DeepComposer. ** | AWS DeepComposer is the world’s first musical keyboard powered by ML to enable developers of all skill levels to learn Generative AI while creating original music outputs. DeepComposer consists of a USB keyboard that connects to the developer’s computer, and the DeepComposer service, accessed through the AWS Management Console. DeepComposer includes tutorials, sample code, and training data that can be used to start building generative models. | Amazon Docs |
| 48. ** AWS DeepLens. ** | AWS DeepLens helps put deep learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills. | Amazon Docs |
| 49. ** AWS HealthLake. ** | AWS HealthLake is a HIPAA-eligible service offering healthcare companies a complete view of individual and patient population health data using FHIR (Fast Healthcare Interoperable Resources) API based transactions to securely store and transform their data into a queryable format at petabyte scale, and further analyze this data using machine learning (ML) models.* | Amazon Docs |
| 50. ** AWS HealthScribe. ** | AWS HealthScribe (in preview) is a HIPAA-eligible service that allows healthcare software vendors to automatically generate clinical notes by analyzing patient-clinician conversations. AWS HealthScribe combines speech recognition with generative AI to reduce the burden of clinical documentation by transcribing conversations and quickly producing clinical notes. Conversations are segmented to identify the speaker roles for patients and clinicians, extract medical terms, and generate preliminary clinical notes. To protect sensitive patient data, security and privacy are built-in to ensure that the input audio and the output text are not retained in AWS HealthScribe. | Amazon Docs |
| 51. ** AWS Panorama. ** | AWS Panorama is a collection of ML devices and software development kit (SDK) that brings computer vision (CV) to on-premises internet protocol (IP) cameras. With AWS Panorama, you can automate tasks that have traditionally required human inspection to improve visibility into potential issues.Computer vision can automate visual inspection for tasks such as tracking assets to optimize supply chain operations, monitoring traffic lanes to optimize traffic management, or detecting anomalies to evaluate manufacturing quality. In environments with limited network bandwidth however, or for companies with data governance rules that require on-premises processing and storage of video, computer vision in the cloud can be difficult or impossible to implement. AWS Panorama is an ML service that allows organizations to bring computer vision to on-premises cameras to make predictions locally with high accuracy and low latency.The AWS Panorama Appliance is a hardware device that adds computer vision to your existing IP cameras and analyzes the video feeds of multiple cameras from a single management interface. It generates predictions at the edge in milliseconds, meaning you can be notified about potential issues such as when damaged products are detected on a fast-moving production line, or when a vehicle has strayed into a dangerous off-limits zone in a warehouse. And, third-party manufacturers are building new AWS Panorama-enabled cameras and devices to provide even more form factors for your unique use cases. With AWS Panorama you can use ML models from AWS to build your own computer vision applications, or work with a partner from the AWS Partner Network to build CV applications quickly.. | Amazon Docs |
| 52. ** AWS DeepLens. ** | AWS DeepLens helps put deep learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills. | Amazon Docs |
| 53. ** AWS DeepLens. ** | AWS DeepLens helps put deep learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills. | Amazon Docs |
| 54. ** AWS DeepLens. ** | AWS DeepLens helps put deep learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills. | Amazon Docs |
| 55. ** AWS DeepLens. ** | AWS DeepLens helps put deep learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills. | Amazon Docs |
| 56. ** AWS DeepLens. ** | AWS DeepLens helps put deep learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills. | Amazon Docs |