my ppt preentation.pptx
- 1. AN INTERNSHIP UNDER THE GUIDANCE OF Sri. K. Jagadeeswara Rao (Assistant Professor) N.KESAV KRISHNA VAMSI (20131A0375) --------------------------------------------------------------------------- GAYATRI VIDYA PARISHAD COLLEGE OF ENGINEERING (AUTONOMOUS)
- 2. AWS CLOUD FOUNDATION AND MACHINE LEARNING
- 3. CERTIFICATE:
- 4. The AI-ML is defined as an application of artificial intelligence where available information is used through algorithms to process or assist the processing of statistical data. While Machine Learning involves concepts of automation, it requires human guidance. In this course they have introduced about cloud foundations and gave brief explanation about AWS cloud foundations. I have learnt basics of cloud foundations and which web server is better to choose and after learning of this cloud foundations they have given introduction and basic definitions of artificial intelligence and machine learning, this course explained briefly about artificial intelligence and machine learning. Cloud Foundation is a multi-disciplinary team of enterprise architects, developers, and operators, network and security engineers, system and database administrators. AI which stands for artificial intelligence refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect. AI manifests in a number of forms. As yet, self-aware AIs are purely the stuff of science fiction. Artificial intelligence, machine learning and deep learning give organizations a way to extract value out of the troves of data they collect, delivering business insights, automating tasks and advancing system capabilities. ABSTRACT
- 5. INTRODUCTION TO CLOUD COMPUTING Cloud computing is the delivery of computing services- including servers, databases, storage, networking, software, analytics, and intelligence-over the Internet(“the cloud’’)to offer faster innovation , flexible resources and economies of scale • CLOUD SERVICES : There are three main cloud service models. • Infrastructure as a service (IaaS): IaaS is known as Hardware as Service (HaaS). It is a computing infrastructure managed over the internet. • Platform as a service (PaaS): PaaS cloud computing platform is created for the programmer to develop, test, run, and manage the applications. • Software as a service (SaaS): SaaS is also known as “on-demand software”. It is a software in which the applications are hosted by a cloud service provider. Users can access these applications with the help of internet connection and web browser.
- 6. WHY CLOUD FOR AI–ML? Cloud provides the large-scale data stores and compute resources (GPUs) that can ingest, process, and store high-velocity real-time streaming data, as well as high-volume batch data, needed for AI-ML. Quick access to large data stores and compute resources. Public clouds also provide cheap data storage. You can leverage true databases or storage systems as the input of the data into the machine learning-enabled applications. WHAT IS AWS? AWS is a subsidiary of amazon that provides on demand cloud computing platforms. It is designed to allow application providers, ISVs, and vendors to quickly and securely host your applications – whether an existing application or a new SaaS-based application.
- 7. AMAZON WEB SERVICES AWS is designed to allow application providers, ISVs, and vendors to quickly and securely host your applications – whether an existing application or a new SaaS-based application. You can use the AWS Management Console or well-documented web services APIs to access AWS's application hosting platform It is a free account management service that enables you to consolidate multiple AWS accounts into an organization that you create and centrally manage. AWS Organizations include consolidated billing and account management capabilities that help you to better meet the budgetary, security, and compliance needs of your business The accessing aws organizations are provided by aws : 1.AWS Management Console 2.AWS Command Line Interface (AWS CLI) tools 3.Software development kits (SDKs) 4.HTTPS Query application programming interfaces (API)
- 8. SUMMARY The first module is based on different types of cloud computing models and also focuses on the main aws services, core services. the second module explored the fundamentals of aws pricing, reviewed total cost of owner concepts. the third module describes the differences between aws regions, availability zones and identifies the aws services. the fourth module summarizes shared responsibility model and identifies responsibility of customer. fifth module describes virtual networking in the cloud with amazon vpc and label a network diagram. the sixth module provides an overview of different aws compute services in the cloud and demonstrates why to use amazon ec2. the seventh module identifies different types of storage, functionality in amazon s3,ebs,efs and differences between them. the eighth module explains amazon relational database service and identifies functionality in amazon rds.
- 9. INTRODUCTION TO MACHINE LEARNING Machine learning (ML)- It is a branch of artificial intelligence (AI) that enables computers to “self- learn” from training data and improve over time ,without being explicitly programmed. Machine learning algorithms are able to detect patterns in data and learn from them ,in order to make their own predictions.
- 10. STEPS IN MACHINE LEARNING For any machine learning it is utmost importance to collect reliable data so that your ML model can find correct patterns. After you have your data put together all the data you have and randomizing it .Clean the data to remove unwanted data visualize the data to understand how it structured. Choose a model which is relevant to the task at hand .Training is the most important step in machine learning it pass the prepared data to your ML model to find patterns and predictions. After training check to see how its performing . Once the model is evaluated its accuracy can be performed by tuning the parameters . In the end use the model on unseen data to make predictions.
- 11. SUMMARY AI which stands for artificial intelligence refers to systems or machines that mimic human intelligence to perform tasks and ml refers to giving the machine the ability to mimic the behavior of the humans. the introduction module consists of the basic terminology and tools available to data scientists and the next module is all about amazon sagemaker and how it is used in building a model and to increase the effectiveness of models’ s performance. the third module focuses on the problems solved by using amazon forecast and working with time series data and usage of amazon forecast to make a prediction. The next module describes aws managing ML services for image and video analytics and use of amazon recognition to perform facial detection. and using amazon sagemaker we can prepare custom dataset. the last module is about how nlp cases are managed by using Amazon ML Services .
- 12. APPLICATIONS OF ARTIFICIAL INTELLIGENCE 1.Web Design 2.Voice Search Optimization 3.E-commerce 4.E-mail Marketing Campaigns 5.Content Curation 6.Content Generation 7.Predictive Analysis 8.AI Powered Chatbotse 9.Online Advertising 10.Google Maps
- 13. GOOGLE MAPS Google maps- the app which we use every time we go out Despite of the usual traffic you are on the fastest route Everyone who is using the google maps is contributing in making the apps more accurate When app is opened it is constantly sending information back to google. Faster route selection
- 14. FORE CASTING • Machine Learning forecasting is a process that uses algorithms to learn data and make predictions about future events. • Forecast is fully managed service that uses machine learning algorithms to deliver highly accurate time-series forecasts. • Forecast provides state-of-the-art algorithms to predict future time-series data based on historical data , requires no machine learning experience. • Time-series forecasting is useful in multiple fields , including retail ,finance, logistics, and healthcare.
- 15. Problem Statement: Face detection using amazon rekognition Domains used: Amazon rekognition, Amazon S3, Amazon ES2, Amazon Lambda Amazon Rekognition : •Amazon Rekognition is a service that makes it easy to add image and video analysis to our application using deep learning technology that requires no mastering in machine learning. •With Amazon Rekognition, we can easily identify text, objects, scenes, and activities in images and videos. •It provides facial analysis and facial search capabilities with high accuracy. We can easily detect and compare faces , user verification, people counting, and human safety use cases. •It can identify the objects and scenes in images that are exact to your business needs. CASE STUDY
- 16. KEY FEATURES OF AMAZON REKOGNITION 1)LABELS 2) CUSTOM LABELS 3) CONTENT MODERATION 4) TEXT DETECTION 5) FACE ANALYSIS AND DETECTION 6) FACE VERIFICATION AND SEARCH
- 17. APPLICATION Amazon rekognition provides two API sets you can use Amazon rekognition image for analysing images and Amazon rekognition video for analysing the videos both the APIS analyse images and videos to provide insights you can use in your applications for example you could use Amazon rekognition image to enhance the customer experience for a photo management application when a customer uploads a photo your application can use Amazon rekognition image to detect real world objects or faces in the image after your application stores the information returned from Amazon rekognition image the user could then query their photo collection for photos with a specific object or a face deeper querying is also possible so for example the user could query for faces that are smiling or query for faces that are for certain age you can use Amazon rekognition video to track the path of the people in a stored video alternatively you can use Amazon rekognition video to search a streaming video for persons whose facial descriptions match diffusion descriptions already stored by Amazon rekognition. The Amazon recognition API makes deep learning image analysis easy to use. For example, rekognizing celebrities returns information for up to 100 celebrities detected in an image. This includes the information about where celebrity faces are detected on the image and where to get further information about the celebrity so this was the overall working of Amazon rekognition.
- 18. THE STEPS INVILVED IN APPLICATION ARE:
- 19. CONCLUSION: In conclusion, cloud computing is recently new technological development that has the potential to have a great impact on the world. It has many benefits that it provides to businesses, is that it reduces operating cost by spending less on maintenance and software upgrades and focus more on the businesses itself. Earlier, We know that Machine Learning is a technique of training machines to perform the activities that a human brain can do, with a bit faster and better than an average human-being. Internship has introduced us to Amazon Sage Maker, Amazon Forecast and Amazon Rekognition through which we came to know how to increase our model performance and to create our own custom datasets, how to handle time series data. These modules described how model explain ability relates to AI/ML solutions, giving customers insight to explain ability requirements when initiating AI/ML use cases. Using AWS, four pillars were presented to assess model explain ability options to bridge knowledge gaps and requirements for simple to complex algorithms