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Code-free machine learning: AutoML with AutoGluon, Amazon SageMaker, and AWS Lambda

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One of AWS’s goals is to put machine learning (ML) in the hands of every developer. With the open-source AutoML library AutoGluon, deployed using Amazon SageMaker and AWS Lambda, we can take this a step further, putting ML in the hands of anyone who wants to make predictions based on data—no prior programming or data science expertise required.

AutoGluon automates ML for real-world applications involving image, text, and tabular datasets. AutoGluon trains multiple ML models to predict a particular feature value (the target value) based on the values of other features for a given observation. During training, the models learn by comparing their predicted target values to the actual target values available in the training data, using appropriate algorithms to improve their predictions accordingly. When training is complete, the resulting models can predict the target feature values for observations they have never seen before, even if you don’t know their actual target values.

AutoGluon automatically applies a variety of techniques to train models on data with a single high-level API call—you don’t need to build models manually. Based on a user-configurable evaluation metric, AutoGluon automatically selects the highest-performing combination, or ensemble, of models. For more information about how AutoGluon works, see Machine learning with AutoGluon, an open source AutoML library.

To get started with AutoGluon, see the AutoGluon GitHub repo. For more information about trying out sophisticated AutoML solutions in your applications, see the AutoGluon website. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models efficiently. AWS Lambda lets you run code without provisioning or managing servers, can be triggered automatically by other AWS services like Amazon Simple Storage Service (Amazon S3), and allows you to build a variety of real-time data processing systems.

With AutoGluon, you can achieve state-of-the-art predictive performance on new observations with as few as three lines of Python code. In this post, we achieve the same results with zero lines of code—making AutoML accessible to non-developers—by using AWS services to deploy a pipeline that trains ML models and makes predictions on tabular data using AutoGluon. After deploying the pipeline in your AWS account, all you need to do to get state-of-the-art predictions on your data is upload it to an S3 bucket with a provided AutoGluon package.

The code-free ML pipeline

The pipeline starts with an S3 bucket, which is where you upload the training data that AutoGluon uses to build your models, the testing data you want to make predictions on, and a pre-made package containing a script that sets up AutoGluon. After you upload the data to Amazon S3, a Lambda function kicks off an Amazon SageMaker model training job that runs the pre-made AutoGluon script on the training data. When the training job is finished, AutoGluon’s best-performing model makes predictions on the testing data, and these predictions are saved back to the same S3 bucket. The following diagram illustrates this architecture.

Deploying the pipeline with AWS CloudFormation

You can deploy this pipeline automatically in an AWS account using a pre-made AWS CloudFormation template. To get started, complete the following steps:

  1. Choose the AWS Region in which you’d like to deploy the template. If you’d like to deploy it in another region, please download the template from GitHub and upload it to CloudFormation yourself.
    Northern Virginia
    Oregon
    Ireland
    Sydney
  2. Sign in to the AWS Management Console.
  3. For Stack name, enter a name for your stack (for example, code-free-automl-stack).
  4. For BucketName, enter a unique name for your S3 bucket (for example, code-free-automl-yournamehere).
  5. For TrainingInstanceType, enter your compute instance.

This parameter controls the instance type Amazon SageMaker model training jobs use to run AutoGluon on your data. AutoGluon is optimized for the m5 instance type, and 50 hours of Amazon SageMaker training time with the m5.xlarge instance type are included as part of the AWS Free Tier. We recommend starting there and adjusting the instance type up or down based on how long your initial job takes and how quickly you need the results.

  1. Select the IAM creation acknowledgement checkbox and choose Create stack.
  2. Continue with the AWS CloudFormation wizard until you arrive at the Stacks page.

It takes a moment for AWS CloudFormation to create all the pipeline’s resources. When you see the CREATE_COMPLETE status (you may need to refresh the page), the pipeline is ready for use.

  1. To see all the components shown in the architecture, choose the Resources tab.
  2. To navigate to the S3 bucket, choose the corresponding link.

Before you can use the pipeline, you have to upload the pre-made AutoGluon package to your new S3 bucket.

  1. Create a folder called source in that bucket.
  2. Upload the sourcedir.tar.gz package there; keep the default object settings.

Your pipeline is now ready for use!

Preparing the training data

To prepare your training data, go back to the root of the bucket (where you see the source folder) and make a new directory called data; this is where you upload your data.

Gather the data you want your models to learn from (the training data). The pipeline is designed to make predictions for tabular data, the most common form of data in real-world applications. Think of it like a spreadsheet; each column represents the measurement of some variable (feature value), and each row represents an individual data point (observation).

For each observation, your training dataset must include columns for explanatory features and the target column containing the feature value you want your models to predict.

Store the training data in a CSV file called <Name>_train.csv, where <Name> can be replaced with anything.

Make sure that the header name of the desired target column (the value of the very first row of the column) is set to target so AutoGluon recognizes it. See the following screenshot of an example dataset.

Preparing the test data

You also need to provide the testing data you want to make predictions for. If this dataset already contains values for the target column, you can compare these actual values to your model’s predictions to evaluate the quality of the model.

Store the testing dataset in another CSV file called <Name>_test.csv, replacing <Name> with the same string you chose for the corresponding training data.

Make sure that the column names match those of <Name>_train.csv, including naming the target column target (if present).

Upload the <Name>_train.csv and <Name>_test.csv files to the data folder you made earlier in your S3 bucket.

The code-free ML pipeline kicks off automatically when the upload is finished.

Training the model

When the training and testing dataset files are uploaded to Amazon S3, AWS logs the occurrence of an event and automatically triggers the Lambda function. This function launches the Amazon SageMaker training job that uses AutoGluon to train an ensemble of ML models. You can view the job’s status on the Amazon SageMaker console, in the Training jobs section (see the following screenshot).

Performing inference

When the training job is complete, the best-performing model or weighted combination of models (as determined by AutoGluon) is used to compute predictions for the target feature value of each observation in the testing dataset. These predictions are automatically stored in a new directory within a results directory in your S3 bucket, with the filename <Name>_test_predictions.csv.

AutoGluon produces other useful output files, such as <Name>_leaderboard.csv (a ranking of each individual model trained by AutoGluon and its predictive performance) and <Name>_model_performance.txt (an extended list of metrics corresponding to the best-performing model). All these files are available for download to your local machine from the Amazon S3 console (see the following screenshot).

Extensions

The trained model artifact from AutoGluon’s best-performing model is also saved in the output folder (see the following screenshot).

You can extend this solution by deploying that trained model as an Amazon SageMaker inference endpoint to make predictions on new data in real time or by running an Amazon SageMaker batch transform job to make predictions on additional testing data files. For more information, see Work with Existing Model Data and Training Jobs.

You can also reuse this automated pipeline with custom model code by replacing the AutoGluon sourcedir.tar.gz package we prepared for you in the source folder. If you unzip that package and look at the Python script inside, you can see that it simply runs AutoGluon on your data. You can adjust some of the parameters defined there to better match your use case. That script and all the other resources used to set up this pipeline are freely available in this GitHub repository.

Cleaning up

The pipeline doesn’t cost you anything more to leave up in your account because it only uses fully managed compute resources on demand. However, if you want to clean it up, simply delete all the files in your S3 bucket and delete the launched CloudFormation stack. Make sure to delete the files first; AWS CloudFormation doesn’t automatically delete an S3 bucket with files inside.

To delete the files from your S3 bucket, on the Amazon S3 console, select the files and choose Delete from the Actions drop-down menu.

To delete the CloudFormation stack, on the AWS CloudFormation console, choose the stack and choose Delete.

In the confirmation window, choose Delete stack.

Conclusion

In this post, we demonstrated how to train ML models and make predictions without writing a single line of code—thanks to AutoGluon, Amazon SageMaker, and AWS Lambda. You can use this code-free pipeline to leverage the power of ML without any prior programming or data science expertise.

If you’re interested in getting more guidance on how you can best use ML in your organization’s products and processes, you can work with the Amazon ML Solutions Lab. The Amazon ML Solutions Lab pairs your team with Amazon ML experts to prepare data, build and train models, and put models into production. It combines hands-on educational workshops with brainstorming sessions and advisory professional services to help you work backward from business challenges, and go step-by-step through the process of developing ML-based solutions. At the end of the program, you can take what you have learned through the process and use it elsewhere in your organization to apply ML to business opportunities.


About the Authors

Abhi Sharma is a deep learning architect on the Amazon ML Solutions Lab team, where he helps AWS customers in a variety of industries leverage machine learning to solve business problems. He is an avid reader, frequent traveler, and driving enthusiast.

Ryan Brand is a Data Scientist in the Amazon Machine Learning Solutions Lab. He has specific experience in applying machine learning to problems in healthcare and the life sciences, and in his free time he enjoys reading history and science fiction.

Tatsuya Arai Ph.D. is a biomedical engineer turned deep learning data scientist on the Amazon Machine Learning Solutions Lab team. He believes in the true democratization of AI and that the power of AI shouldn’t be exclusive to computer scientists or mathematicians.

Source: https://aws.amazon.com/blogs/machine-learning/code-free-machine-learning-automl-with-autogluon-amazon-sagemaker-and-aws-lambda/

Artificial Intelligence

AI Offers Powerful Insights into Video Content Production

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AI, language processing, and deep learning are three major technologies that affect streaming services. They are already used to create content and the picture looks beautiful and detailed. If we exploit Artificial Intelligence in live streaming, this would change everything. Modern technology allows us to create automated actions, trigger real-time graphics, analyze audio, monitor social media sentiment and auto-sharing.

AI Leads to New Opportunities for Video Content Development

Some developers saw AI technology as an opportunity and created useful tools that help in video production. Let’s talk about 4 software variants that could be used for video production.

NotMP3

To process multimedia you should have a useful tool that will allow getting them without payment. Nowadays the best content provider is Youtube. That is why we decided to mention NotMP3 software, which uses cutting-edge AI technology. This sort will allow you to fulfill this task for free.

This is free software which is downloadable from the site. This is a simple but very powerful tool that can help you to get audio or video file of any quality you like. You can also get files from Soundcloud and transfer them to various formats.

The main advantage of NotMP3 software is that it is completely free and does not require creating an account. All you have to do is to install and use it.  

Transcriptive AI

The first program that actually uses AI technology is Transcriptive AI. This is a universal tool that is exploited to transcribe footage. It will make that footage searchable. You can use this software to make caption and paper edits.

The creator allows getting a free trial copy but if you want to continue using it, you will have to buy the product. This soft will be useful if you want to find some pieces of audio that have been mentioned in the video. You will also have accurate transcripts spending as little time as possible. The most powerful component is searchability – you can look for clips, sequences, and markers. This machine learning tool works like Google and you just mention the keywords.

Pixop

This is not a tool but a cloud service that exploits intuitive interface and AI technologies for video enhancement. The main advantage of this service is that it is very easy to use. You will not need any plugins and other tools. No subscription and fees are required. This service has been created to encourage creators to update their digital archives as easily as it is possible.

Users value Pixop because it provides a place where you can make video enhancement as fast as possible. This is a flexible and scalable could service which has been created to tackle every requirement. And the most important feature is the fact that it is powered by machine learning – just let AI do the work! Just sign up and use the online tool.

Imagen

This is a media management tool that is used for keeping media safe, secure, and easy to find. This program will help you to organize and unlock the full potential of your video files. You just open a video file and start editing it. This software uses a simple but effective technology to process a picture. Choose a frame, pause it and crop out the needed fragment. Then this picture could be saved on your device and used to create media products. You can add annotations for any image.

This tool could be ideal for content makers that are willing to organize and see the true value of their content. This service lets you focus on the important features of the picture. It is very good for business in content-making industry.

AI is Invaluable for Creating New Video Content

So, what is the best AI solution for content creation? At first, you will need a tool that would be able to download the high-quality video as fast as possible. That is why you will definitely need NotMP3 downloader and converter.

After that try Transcriptive AI program because this is professional software. It has lots of useful features. But it is not free.

If you are looking for the fastest way, try Pixop cloud service – it has all the needed functions. You can use Imagen as a free offline variant which will help you to create the best content.

The post AI Offers Powerful Insights into Video Content Production appeared first on SmartData Collective.

Checkout PrimeXBT
Source: https://www.smartdatacollective.com/ai-offers-insights-into-video-content-production/

Continue Reading

Artificial Intelligence

AI Offers Powerful Insights into Video Content Production

Avatar

Published

on

AI, language processing, and deep learning are three major technologies that affect streaming services. They are already used to create content and the picture looks beautiful and detailed. If we exploit Artificial Intelligence in live streaming, this would change everything. Modern technology allows us to create automated actions, trigger real-time graphics, analyze audio, monitor social media sentiment and auto-sharing.

AI Leads to New Opportunities for Video Content Development

Some developers saw AI technology as an opportunity and created useful tools that help in video production. Let’s talk about 4 software variants that could be used for video production.

NotMP3

To process multimedia you should have a useful tool that will allow getting them without payment. Nowadays the best content provider is Youtube. That is why we decided to mention NotMP3 software, which uses cutting-edge AI technology. This sort will allow you to fulfill this task for free.

This is free software which is downloadable from the site. This is a simple but very powerful tool that can help you to get audio or video file of any quality you like. You can also get files from Soundcloud and transfer them to various formats.

The main advantage of NotMP3 software is that it is completely free and does not require creating an account. All you have to do is to install and use it.  

Transcriptive AI

The first program that actually uses AI technology is Transcriptive AI. This is a universal tool that is exploited to transcribe footage. It will make that footage searchable. You can use this software to make caption and paper edits.

The creator allows getting a free trial copy but if you want to continue using it, you will have to buy the product. This soft will be useful if you want to find some pieces of audio that have been mentioned in the video. You will also have accurate transcripts spending as little time as possible. The most powerful component is searchability – you can look for clips, sequences, and markers. This machine learning tool works like Google and you just mention the keywords.

Pixop

This is not a tool but a cloud service that exploits intuitive interface and AI technologies for video enhancement. The main advantage of this service is that it is very easy to use. You will not need any plugins and other tools. No subscription and fees are required. This service has been created to encourage creators to update their digital archives as easily as it is possible.

Users value Pixop because it provides a place where you can make video enhancement as fast as possible. This is a flexible and scalable could service which has been created to tackle every requirement. And the most important feature is the fact that it is powered by machine learning – just let AI do the work! Just sign up and use the online tool.

Imagen

This is a media management tool that is used for keeping media safe, secure, and easy to find. This program will help you to organize and unlock the full potential of your video files. You just open a video file and start editing it. This software uses a simple but effective technology to process a picture. Choose a frame, pause it and crop out the needed fragment. Then this picture could be saved on your device and used to create media products. You can add annotations for any image.

This tool could be ideal for content makers that are willing to organize and see the true value of their content. This service lets you focus on the important features of the picture. It is very good for business in content-making industry.

AI is Invaluable for Creating New Video Content

So, what is the best AI solution for content creation? At first, you will need a tool that would be able to download the high-quality video as fast as possible. That is why you will definitely need NotMP3 downloader and converter.

After that try Transcriptive AI program because this is professional software. It has lots of useful features. But it is not free.

If you are looking for the fastest way, try Pixop cloud service – it has all the needed functions. You can use Imagen as a free offline variant which will help you to create the best content.

The post AI Offers Powerful Insights into Video Content Production appeared first on SmartData Collective.

Checkout PrimeXBT
Source: https://www.smartdatacollective.com/ai-offers-insights-into-video-content-production/

Continue Reading

AI

How AI is Transforming Cybersecurity in 2021?

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Artificial Intelligence (AI) is one of the main weapons by which companies or medium-sized corporations can combat numerous cyber threats successfully.

According to Warren Buffet, “Cyber-attack is the biggest threat to mankind, even more of a bigger threat than the nuclear weapon.” Therefore, organizations should consider applying the concepts of AI within their workplaces if they want to prosper in the future without compromising their digital anonymity.

Continue reading this post to know what is AI and how it is transforming cybersecurity for all the right reasons.  

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is a modern branch of computer science. It helps create smart devices that can perform various tasks depending on the situation using the core concepts of human intelligence.

Thanks to Artificial Intelligence (AI), you can now deposit cheques online from your homes hassle-free. Furthermore, AI allows you to decipher handwriting, making online cheque processing a reality these days.

As far as the importance of AI in cybersecurity is concerned, it enables cybersecurity professionals in detecting and resolving numerous security risks residing in corporate networks of different organizations proactively.

How is AI transforming cybersecurity in 2021?

As previously discussed, AI is providing numerous solutions to cybersecurity experts worldwide. Besides, Artificial Intelligence (AI) will replace the need for human beings in cybersecurity by the end of 2030.

In short, AI will help organizations or businesses significantly improve the performance of their cybersecurity departments in the future without relying on human expertise.

As expected, various organizations are already relying on AI concepts when they want to detect and eliminate potential cyber risks within their networks timely.

Moreover, 80% of telecom companies are benefiting from AI notions to protect themselves against dangerous cybersecurity issues like hacking, data theft, identity theft, and others accordingly.

The best thing about implementing AI concepts is that companies can reduce their cost by 12% in terms of threats and breaches detection. In addition to this, they can follow use cases of AI to improve their performance cybersecurity-wise.

This is one of the major reasons why organizations or businesses are investing huge amounts of money in Artificial Intelligence (AI) globally.

Considering the significance of AI in cybersecurity, we can expect that the role of AI when it comes to enhancing cybersecurity of organizations or businesses will increase with the passage of time.

Is everything rosy with Artificial Intelligence (AI)?

There is no denying that AI offers various advantages to its users, be it companies, medium-sized enterprises and small businesses. However, the worst thing about AI is that it is easily accessible to everyone, including cyber terrorists, which is not good from a cybersecurity point of view.

Therefore, hackers can use the said concepts of AI to accomplish their malicious objectives. Unfortunately, they can access AI models and use them to bypass prevailing cybersecurity practices of organizations or businesses easily.

As a result, they can gain control of corporate IT networks and misuse official data including customers’ personal information and sensitive business communications to execute their notorious plans.

In this scenario, companies should use cybersecurity tools such as VPNs. They can try a VPN service that offers a free trial if they do not want to spend hundreds of bucks initially to see if it helps them safeguard their digital assets from hackers and other cyber goons. Once they are satisfied with its performance, they can consider using its premium packages as per their preferences.

Apart from this, there is no harm in availing different online protection tools like antivirus software, email encryption software, password managers, firewalls, KPIs, etc. Once organizations or companies start using them, they can effectively protect their official devices and business data from the prying eyes of notorious elements over the web.

Lastly, they should provide basic cybersecurity training to improve their employee’s personal privacy either remote or office-based comprehensively.

As a result, they will start updating their official devices regularly. Moreover, they can secure their devices from phishing attacks as they will not click on a suspicious link provided in emails sent from unknown people.

They can also start using password managers like LastPass and others that will enable them to protect their crucial login credentials trouble-free.

Wrapping Things Up

Artificial Intelligence (AI) does have all the right ingredients to transform the future of cybersecurity to the next level. However, companies or businesses should be smart enough to apply AI notions within their workplaces with caution.

Besides, they should not stop following certain cyber hygiene practices at any cost, like installing antivirus software on official devices, data backup, password managers, email encryption tools, etc.

This way, they can protect themselves from the consequences of hacking, data theft, malware, phishing and other cyber risks in 2021 and beyond.

The post How AI is Transforming Cybersecurity in 2021? appeared first on SmartData Collective.

Checkout PrimeXBT
Source: https://www.smartdatacollective.com/how-ai-is-transforming-cybersecurity/

Continue Reading

AI

How AI is Transforming Cybersecurity in 2021?

Avatar

Published

on

Artificial Intelligence (AI) is one of the main weapons by which companies or medium-sized corporations can combat numerous cyber threats successfully.

According to Warren Buffet, “Cyber-attack is the biggest threat to mankind, even more of a bigger threat than the nuclear weapon.” Therefore, organizations should consider applying the concepts of AI within their workplaces if they want to prosper in the future without compromising their digital anonymity.

Continue reading this post to know what is AI and how it is transforming cybersecurity for all the right reasons.  

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is a modern branch of computer science. It helps create smart devices that can perform various tasks depending on the situation using the core concepts of human intelligence.

Thanks to Artificial Intelligence (AI), you can now deposit cheques online from your homes hassle-free. Furthermore, AI allows you to decipher handwriting, making online cheque processing a reality these days.

As far as the importance of AI in cybersecurity is concerned, it enables cybersecurity professionals in detecting and resolving numerous security risks residing in corporate networks of different organizations proactively.

How is AI transforming cybersecurity in 2021?

As previously discussed, AI is providing numerous solutions to cybersecurity experts worldwide. Besides, Artificial Intelligence (AI) will replace the need for human beings in cybersecurity by the end of 2030.

In short, AI will help organizations or businesses significantly improve the performance of their cybersecurity departments in the future without relying on human expertise.

As expected, various organizations are already relying on AI concepts when they want to detect and eliminate potential cyber risks within their networks timely.

Moreover, 80% of telecom companies are benefiting from AI notions to protect themselves against dangerous cybersecurity issues like hacking, data theft, identity theft, and others accordingly.

The best thing about implementing AI concepts is that companies can reduce their cost by 12% in terms of threats and breaches detection. In addition to this, they can follow use cases of AI to improve their performance cybersecurity-wise.

This is one of the major reasons why organizations or businesses are investing huge amounts of money in Artificial Intelligence (AI) globally.

Considering the significance of AI in cybersecurity, we can expect that the role of AI when it comes to enhancing cybersecurity of organizations or businesses will increase with the passage of time.

Is everything rosy with Artificial Intelligence (AI)?

There is no denying that AI offers various advantages to its users, be it companies, medium-sized enterprises and small businesses. However, the worst thing about AI is that it is easily accessible to everyone, including cyber terrorists, which is not good from a cybersecurity point of view.

Therefore, hackers can use the said concepts of AI to accomplish their malicious objectives. Unfortunately, they can access AI models and use them to bypass prevailing cybersecurity practices of organizations or businesses easily.

As a result, they can gain control of corporate IT networks and misuse official data including customers’ personal information and sensitive business communications to execute their notorious plans.

In this scenario, companies should use cybersecurity tools such as VPNs. They can try a VPN service that offers a free trial if they do not want to spend hundreds of bucks initially to see if it helps them safeguard their digital assets from hackers and other cyber goons. Once they are satisfied with its performance, they can consider using its premium packages as per their preferences.

Apart from this, there is no harm in availing different online protection tools like antivirus software, email encryption software, password managers, firewalls, KPIs, etc. Once organizations or companies start using them, they can effectively protect their official devices and business data from the prying eyes of notorious elements over the web.

Lastly, they should provide basic cybersecurity training to improve their employee’s personal privacy either remote or office-based comprehensively.

As a result, they will start updating their official devices regularly. Moreover, they can secure their devices from phishing attacks as they will not click on a suspicious link provided in emails sent from unknown people.

They can also start using password managers like LastPass and others that will enable them to protect their crucial login credentials trouble-free.

Wrapping Things Up

Artificial Intelligence (AI) does have all the right ingredients to transform the future of cybersecurity to the next level. However, companies or businesses should be smart enough to apply AI notions within their workplaces with caution.

Besides, they should not stop following certain cyber hygiene practices at any cost, like installing antivirus software on official devices, data backup, password managers, email encryption tools, etc.

This way, they can protect themselves from the consequences of hacking, data theft, malware, phishing and other cyber risks in 2021 and beyond.

The post How AI is Transforming Cybersecurity in 2021? appeared first on SmartData Collective.

Checkout PrimeXBT
Source: https://www.smartdatacollective.com/how-ai-is-transforming-cybersecurity/

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