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Multi-account model deployment with Amazon SageMaker Pipelines




Amazon SageMaker Pipelines is the first purpose-built CI/CD service for machine learning (ML). It helps you build, automate, manage, and scale end-to-end ML workflows and apply DevOps best practices of CI/CD to ML (also known as MLOps).

Creating multiple accounts to organize all the resources of your organization is a good DevOps practice. A multi-account strategy is important not only to improve governance but also to increase security and control of the resources that support your organization’s business. This strategy allows many different teams inside your organization, to experiment, innovate, and integrate faster, while keeping the production environment safe and available for your customers.

Pipelines makes it easy to apply the same strategy to deploying ML models. Imagine a use case in which you have three different AWS accounts, one for each environment: data science, staging, and production. The data scientist has the freedom to run experiments and train and optimize different models any time in their own account. When a model is good enough to be deployed in production, the data scientist just needs to flip the model approval status to Approved. After that, an automated process deploys the model on the staging account. Here you can automate testing of the model with unit tests or integration tests or test the model manually. After a manual or automated approval, the model is deployed to the production account, which is a more tightly controlled environment used to serve inferences on real-world data. With Pipelines, you can implement a ready-to-use multi-account environment.

In this post, you learn how to use Pipelines to implement your own multi-account ML pipeline. First, you learn how to configure your environment and prepare it to use a predefined template as a SageMaker project for training and deploying a model in two different accounts: staging and production. Then, you see in detail how this custom template was created and how to create and customize templates for your own SageMaker projects.

Preparing the environment

In this section, you configure three different AWS accounts and use SageMaker Studio to create a project that integrates a CI/CD pipeline with the ML pipeline created by a data scientist. The following diagram shows the reference architecture of the environment that is created by the SageMaker custom project and how AWS Organizations integrates the different accounts.

The following diagram shows the reference architecture of the environment that is created by the SageMaker custom project and how AWS Organizations integrates the different accounts.

The diagram contains three different accounts, managed by Organizations. Also, three different user roles (which may be the same person) operate this environment:

  • ML engineer – Responsible for provisioning the SageMaker Studio project that creates the CI/CD pipeline, model registry, and other resources
  • Data scientist – Responsible for creating the ML pipeline that ends with a trained model registered to the model group (also referred to as model package group)
  • Approver – Responsible for testing the model deployed to the staging account and approving the production deployment

It’s possible to run a similar solution without Organizations, if you prefer (although not recommended). But you need to prepare the permissions and the trust relationship between your accounts manually and modify the template to remove the Organizations dependency. Also, if you’re an enterprise with multiple AWS accounts and teams, it’s highly recommended that you use AWS Control Tower for provisioning the accounts and Organizations. AWS Control Tower provides the easiest way to set up and govern a new and secure multi-account AWS environment. For this post, we only discuss implementing the solution with Organizations.

But before you move on, you need to complete the following steps, which are detailed in the next sections:

  1. Create an AWS account to be used by the data scientists (data science account).
  2. Create and configure a SageMaker Studio domain in the data science account.
  3. Create two additional accounts for production and staging.
  4. Create an organizational structure using Organizations, then invite and integrate the additional accounts.
  5. Configure the permissions required to run the pipelines and deploy models on external accounts.
  6. Import the SageMaker project template for deploying models in multiple accounts and make it available for SageMaker Studio.

Configuring SageMaker Studio in your account

Pipelines provides built-in support for MLOps templates to make it easy for you to use CI/CD for your ML projects. These MLOps templates are defined as Amazon CloudFormation templates and published via AWS Service Catalog. These are made available to data scientists via SageMaker Studio, an IDE for ML. To configure Studio in your account, complete the following steps:

  1. Prepare your SageMaker Studio domain.
  2. Enable SageMaker project templates and SageMaker JumpStart for this account and Studio users.

If you have an existing domain, you can simply edit the settings for the domain or individual users to enable this option. Enabling this option creates two different AWS Identity and Account Management (IAM) roles in your AWS account:

  • AmazonSageMakerServiceCatalogProductsLaunchRole – Used by SageMaker to run the project templates and create the required infrastructure resources
  • AmazonSageMakerServiceCatalogProductsUseRole – Used by the CI/CD pipeline to run a job and deploy the models on the target accounts

If you created your SageMaker Studio domain before re:Invent 2020, it’s recommended that you refresh your environment by saving all the work in progress. On the File menu, choose Shutdown, and confirm your choice.

  1. Create and prepare two other AWS accounts for staging and production, if you don’t have them yet.

Configuring Organizations

You need to add the data science account and the two additional accounts to a structure in Organizations. Organizations helps you to centrally manage and govern your environment as you grow and scale your AWS resources. It’s free and benefits your governance strategy.

Each account must be added to a different organizational unit (OU).

  1. On the Organizations console, create a structure of OUs like the following:
  • Root
    • multi-account-deployment (OU)
      • 111111111111 (data science account—SageMaker Studio)
      • production (OU)
        • 222222222222 (AWS account)
      • staging (OU)
        • 333333333333 (AWS account)

After configuring the organization, each account owner receives an invite. The owners need to accept the invites, otherwise the accounts aren’t included in the organization.

  1. Now you need to enable trusted access with AWS organizations (“Enable all features” and “Enable trusted access in the StackSets”).

This process allows your data science account to provision resources in the target accounts. If you don’t do that, the deployment process fails. Also, this feature set is the preferred way to work with Organizations, and it includes consolidating billing features.

  1. Next, on the Organizations console, choose Organize accounts.
  2. Choose staging.
  3. Note down the OU ID.
  4. Repeat this process for the production OU.

Repeat this process for the production OU.

Configuring the permissions

You need to create a SageMaker execution role in each additional account. These roles are assumed by AmazonSageMakerServiceCatalogProductsUseRole in the data science account to deploy the endpoints in the target accounts and test them.

  1. Sign in to the AWS Management Console with the staging account.
  2. Run the following CloudFormation template.

This template creates a new SageMaker role for you.

  1. Provide the following parameters:
    1. SageMakerRoleSuffix – A short string (maximum 10 lowercase with no spaces or alpha-numeric characters) that is added to the role name after the following prefix: sagemaker-role-. The final role name is sagemaker-role-<<sagemaker_role_suffix>>.
    2. PipelineExecutionRoleArn – The ARN of the role from the data science account that assumes the SageMaker role you’re creating. To find the ARN, sign in to the console with the data science account. On the IAM console, choose Roles and search for AmazonSageMakerServiceCatalogProductsUseRole. Choose this role and copy the ARN (arn:aws:iam::<<data_science_acccount_id>>:role/service-role/AmazonSageMakerServiceCatalogProductsUseRole).
  2. After creating this role in the staging account, repeat this process for the production account.

In the data science account, you now configure the policy of the Amazon Simple Storage Service (Amazon S3) bucket used to store the trained model. For this post, we use the default SageMaker bucket of the current Region. It has the following name format: sagemaker-<<region>>-<<aws_account_id>>.

  1. On the Amazon S3 console, search for this bucket, providing the Region you’re using and the ID of the data science account.

If you don’t find it, create a new bucket following this name format.

  1. On the Permissions tab, add the following policy:
    { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "AWS": [ "arn:aws:iam::<<staging_account_id>>:root", "arn:aws:iam::<<production_account_id>>:root" ] }, "Action": [ "s3:GetObject", "s3:ListBucket" ], "Resource": [ "arn:aws:s3:::sagemaker-<<region>>-<<aws_account_id>>", "arn:aws:s3:::sagemaker-<<region>>-<<aws_account_id>>/*" ] } ]

  1. Save your settings.

The target accounts now have permission to read the trained model during deployment.

The next step is to add new permissions to the roles AmazonSageMakerServiceCatalogProductsUseRole and AmazonSageMakerServiceCatalogProductsLaunchRole.

  1. In the data science account, on the IAM console, choose Roles.
  2. Find the AmazonSageMakerServiceCatalogProductsUseRole role and choose it.
  3. Add a new policy and enter the following JSON code.
  4. Save your changes.
  5. Now, find the AmazonSageMakerServiceCatalogProductsLaunchRole role, choose it and add a new policy with the following content:
    { "Version": "2012-10-17", "Statement": [ { "Sid": "VisualEditor0", "Effect": "Allow", "Action": "s3:GetObject", "Resource": "arn:aws:s3:::aws-ml-blog/artifacts/sagemaker-pipeline-blog-resources/*" } ]

  1. Save your changes.

That’s it! Your environment is almost ready. You only need one more step and you can start training and deploying models in different accounts.

Importing the custom SageMaker Studio project template

In this step, you import your custom project template.

  1. Sign in to the console with the data science account.
  2. On the AWS Service Catalog console, under Administration, choose Portfolios.
  3. Choose Create a new portfolio.
  4. Name the portfolio SageMaker Organization Templates.
  5. Download the following template to your computer.
  6. Choose the new portfolio.
  7. Choose Upload a new product.
  8. For Product name¸ enter Multi Account Deployment.
  9. For Description, enter Multi account deployment project.
  10. For Owner, enter your name.
  11. Under Version details, for Method, choose Use a template file.
  12. Choose Upload a template.
  13. Upload the template you downloaded.
  14. For Version title, choose 1.0.

The remaining parameters are optional.

  1. Choose Review.
  2. Review your settings and choose Create product.
  3. Choose Refresh to list the new product.
  4. Choose the product you just created.
  5. On the Tags tab, add the following tag to the product:
    1. Keysagemaker:studio-visibility
    2. ValueTrue

Back in the portfolio details, you see something similar to the following screenshot (with different IDs).

Back in the portfolio details, you see something similar to the following screenshot (with different IDs).

  1. On the Constraints tab, choose Create constraint.
  2. For Product, choose Multi Account Deployment (the product you just created).
  3. For Constraint type, choose Launch.
  4. Under Launch Constraint, for Method, choose Select IAM role.
  5. Choose AmazonSageMakerServiceCatalogProductsLaunchRole.
  6. Choose Create.
  7. On the Groups, roles, and users tab, choose Add groups, roles, users.
  8. On the Roles tab, select the role you used when configuring your SageMaker Studio domain.
  9. Choose Add access.

If you don’t remember which role you selected, in your data science account, go to the SageMaker console and choose Amazon SageMaker Studio. In the Studio Summary section, locate the attribute Execution role. Search for the name of this role in the previous step.

You’re done! Now it’s time to create a project using this template.

Creating your project

In the previous sections, you prepared the multi-account environment. The next step is to create a project using your new template.

  1. Sign in to the console with the data science account.
  2. On the SageMaker console, open SageMaker Studio with your user.
  3. Choose the Components and registries
  4. On the drop-down menu, choose Projects.
  5. Choose Create project.

Choose Create project.

On the Create project page, SageMaker templates is chosen by default. This option lists the built-in templates. However, you want to use the template you prepared for the multi-account deployment.

  1. Choose Organization templates.
  2. Choose Multi Account Deployment.
  3. Choose Select project template.

If you can’t see the template, make sure you completed all the steps correctly in the previous section.

If you can’t see the template, make sure you completed all the steps correctly in the previous section.

  1. In the Project details section, for Name, enter iris-multi-01.

The project name must have 15 characters or fewer.

  1. In the Project template parameters, use the names of the roles you created in each target account (staging and production) and provide the following properties:
    1. SageMakerExecutionRoleStagingName
    2. SageMakerExecutionRoleProdName
  2. Retrieve the OU IDs you created earlier for the staging and production OUs and provide the following properties:
    1. OrganizationalUnitStagingId
    2. OrganizationalUnitProdId
  3. Choose Create project.

Choose Create project.

Provisioning all the resources takes a few minutes, after which the project is listed in the Projects section. When you choose the project, a tab opens with the project’s metadata. The Model groups tab chows a model group with the same name as your project. It was also created during the project provisioning.

Provisioning all the resources takes a few minutes, after which the project is listed in the Projects section.

The environment is now ready for the data scientist to start training the model.

Training a model

Now that your project is ready, it’s time to train a model.

  1. Download the example notebook to use for this walkthrough.
  2. Choose the Folder icon to change the work area to file management.
  3. Choose the Create folder
  4. Enter a name for the folder.
  5. Choose the folder name.
  6. Choose the Upload file
  7. Choose the Jupyter notebook you downloaded and upload it to the new directory.
  8. Choose the notebook to open a new tab.

Choose the notebook to open a new tab.

You’re prompted to choose a kernel.

  1. Choose Python3 (Data Science).
  2. Choose Select.

Choose Select.

  1. In the second cell of the notebook, replace the project_name variable with the name you gave your project (for this post, iris-multi-01).

You can now run the Jupyter notebook. This notebook creates a very simple pipeline with only two steps: train and register model. It uses the iris dataset and the XGBoost built-in container as the algorithm.

  1. Run the whole notebook.

The process takes some time after you run the cell containing the following code:

start_response = pipeline.start(parameters={ "TrainingInstanceCount": "1"

This starts the training job, which takes approximately 3 minutes to complete. After the training is finished, the next cell of the Jupyter notebook gets the latest version of the model in the model registry and marks it as Approved. Alternatively, you can approve a model from the SageMaker Studio UI. On the Model groups tab, choose the model group and desired version. Choose Update status and Approve before saving.

Choose Update status and Approve before saving

This is the end of the data scientist’s job but the beginning of running the CI/CD pipeline.

Amazon EventBridge monitors the model registry. The listener starts a new deployment job with the provisioned AWS CodePipeline workflow (created with you launched the SageMaker Studio project).

  1. On the CodePipeline console, choose the pipeline starting with the prefix sagemaker-, followed by the name of your project.

On the CodePipeline console, choose the pipeline starting with the prefix sagemaker-, followed by the name of your project.

Shortly after you approve your model, the deployment pipeline starts running. Wait for the pipeline to reach the state DeployStaging. That stage can take approximately 10 minutes to complete. After deploying the first endpoint in the staging account, the pipeline is tested, and then moves to the next step, ApproveDeployment. In this step, it waits for manual approval.

  1. Choose Review.
  2. Enter an approval reason in the text box.
  3. Choose Approve.

The model is now deployed in the production account.

You can also monitor the pipeline on the AWS CloudFormation console, to see the stacks and stack sets the pipeline creates to deploy endpoints in the target accounts. To see the deployed endpoints for each account, sign in to the SageMaker console as either the staging account or production account and choose Endpoints on the navigation pane.

Cleaning up

To clean up all the resources you provisioned in this example, complete the following steps:

  1. Sign in to the console with your main account.
  2. On the AWS CloudFormation console, click on StackSets and delete the following items (endpoints):
    1. Prod sagemaker-<<sagemaker-project-name>>-<<project-id>>-deploy-prod
    2. Stagingsagemaker-<<sagemaker-project-name>>-<<project-id>>-deploy-staging
  3. In your laptop or workstation terminal, use the AWS Command Line Interface (AWS CLI) and enter the following code to delete your project:
    aws sagemaker delete-project --project-name iris-multi-01

Make sure you’re using the latest version of the AWS CLI.

Building and customizing a template for your own SageMaker project

SageMaker projects and SageMaker MLOps project templates are powerful features that you can use to automatically create and configure the whole infrastructure required to train, optimize, evaluate, and deploy ML models. A SageMaker project is an AWS Service Catalog provisioned product that enables you to easily create an end-to-end ML solution. For more information, see the AWS Service Catalog Administrator Guide.

A product is a CloudFormation template managed by AWS Service Catalog. For more information about templates and their requirements, see AWS CloudFormation template formats.

ML engineers can design multiple environments and express all the details of this setup as a CloudFormation template, using the concept of infrastructure as code (IaC). You can also integrate these different environments and tasks using a CI/CD pipeline. SageMaker projects provide an easy, secure, and straightforward way of wrapping the infrastructure complexity around in the format of a simple project, which can be launched many times by the other ML engineers and data scientists.

The following diagram illustrates the main steps you need to complete in order to create and publish your custom SageMaker project template.

The following diagram illustrates the main steps you need to complete in order to create and publish your custom SageMaker project template.

We described these steps in more detail in the sections Importing the custom SageMaker Studio Project template and Creating your project.

As an ML engineer, you can design and create a new CloudFormation template for the project, prepare an AWS Service Catalog portfolio, and add a new product to it.

Both data scientists and ML engineers can use SageMaker Studio to create a new project with the custom template. SageMaker invokes AWS Service Catalog and starts provisioning the infrastructure described in the CloudFormation template.

As a data scientist, you can now start training the model. After you register it in the model registry, the CI/CD pipeline runs automatically and deploys the model on the target accounts.

If you look at the CloudFormation template from this post in a text editor, you can see that it implements the architecture we outline in this post.

The following code is a snippet of the template:

Description: Toolchain template which provides the resources needed to represent infrastructure as code. This template specifically creates a CI/CD pipeline to deploy a given inference image and pretrained Model to two stages in CD -- staging and production.
Parameters: SageMakerProjectName: Type: String SageMakerProjectId: Type: String
<<other parameters>>
Resources: MlOpsArtifactsBucket: Type: AWS::S3::Bucket DeletionPolicy: Retain Properties: BucketName: …
… ModelDeployCodeCommitRepository: Type: AWS::CodeCommit::Repository Properties: RepositoryName: … RepositoryDescription: … Code: S3: Bucket: … Key: …
… ModelDeployBuildProject: Type: AWS::CodeBuild::Project
… ModelDeployPipeline: Type: AWS::CodePipeline::Pipeline

The template has two key sections: Parameters (input parameters of the template) and Resources. SageMaker project templates require that you add two input parameters to your template: SageMakerProjectName and SageMakerProjectId. These parameters are used internally by SageMaker Studio. You can add other parameters if needed.

In the Resources section of the snippet, you can see that it creates the following:

  • A new S3 bucket used by the CI/CD pipeline to store the intermediary artifacts passed from one stage to another.
  • An AWS CodeCommit repository to store the artifacts used during the deployment and testing stages.
  • An AWS CodeBuild project to get the artifacts, and validate and configure them for the project. In the multi-account template, this project also creates a new model registry, used by the CI/CD pipeline to deploy new models.
  • A CodePipeline workflow that orchestrates all the steps of the CI/CD pipelines.

Each time you register a new model to the model registry or push a new artifact to the CodeCommit repo, this CodePipeline workflow starts. These events are captured by an EventBridge rule, provisioned by the same template. The CI/CD pipeline contains the following stages:

  • Source – Reads the artifacts from the CodeCommit repository and shares with the other steps.
  • Build – Runs the CodeBuild project to do the following:
    • Verify if a model registry is already created, and create one if needed.
    • Prepare a new CloudFormation template that is used by the next two deployment stages.
  • DeployStaging – Contains the following components:
    • DeployResourcesStaging – Gets the CloudFormation template prepared in the Build step and deploys a new stack. This stack deploys a new SageMaker endpoint in the target account.
    • TestStaging – Invokes a second CodeBuild project that runs a custom Python script that tests the deployed endpoint.
    • ApproveDeployment – A manual approval step. If approved, it moves to the next stage to deploy an endpoint in production, or ends the workflow if not approved.
  • DeployProd – Similar to DeployStaging, it uses the same CloudFormation template but with different input parameters. It deploys a new SageMaker endpoint in the production account. 

You can start a new training process and register your model to the model registry associated with the SageMaker project. Use the Jupyter notebook provided in this post and customize your own ML pipeline to prepare your dataset and train, optimize, and test your models before deploying them. For more information about these features, see Automate MLOps with SageMaker Projects. For more Pipelines examples, see the GitHub repo.

Conclusions and next steps

In this post, you saw how to prepare your own environment to train and deploy ML models in multiple AWS accounts by using SageMaker Pipelines.

With SageMaker projects, the governance and security of your environment can be significantly improved if you start managing your ML projects as a library of SageMaker project templates.

As a next step, try to modify the SageMaker project template and customize it to address your organization’s needs. Add as many steps as you want and keep in mind that you can capture the CI/CD events and notify users or call other services to build comprehensive solutions.

About the Author

Samir Araújo is an AI/ML Solutions Architect at AWS. He helps customers creating AI/ML solutions solve their business challenges using the AWS platform. He has been working on several AI/ML projects related to computer vision, natural language processing, forecasting, ML at the edge, and more. He likes playing with hardware and automation projects in his free time, and he has a particular interest for robotics.



Digital ID Verification Service IDnow Acquires identity Trust Management AG, a Global Provider of ID Software from Germany




IDnow, a provider of identity verification-as-a-service solutions, will be acquiring identity Trust Management, a global provider of digital and offline ID verification software from Germany.

IDnow confirmed that it would continue to maintain identity Trust Management’s Düsseldorf location and will retain its employees as well.

The acquisition of Identity Trust Management should help IDnow with further expanding into new verticals while offering its services to a larger and potentially more diverse client base in Germany and other areas.

The combined product portfolio will aim to provide comprehensive ID verification methods, ranging from automated to human-assisted and from being purely online to point-of-sale. All these ID verification methods will be accessible through the IDnow platform.

Identity Trust Management has established its operations in Germany’s identity industry during the past 10 years, with a solid reputation and portfolio of clients focused on telecommunications and insurance services.

Andreas Bodczek, CEO at IDnow, stated:

“Identity Trust Management AG has built an impressive company both in terms of product portfolio and client relationships. We have known the leadership team for years and have established a partnership rooted in deep loyalty and mutual understanding. We are excited to welcome identity Trust Management AG’s talented team to the IDnow family and look forward to combining the strengths of both companies to create a unified, market-leading brand.”

Uwe Stelzig, CEO at identity Trust Management AG, remarked:

“This combination unites the power of IDnow’s innovative technology with identity Trust Management AG’s diverse set of capabilities to create a differentiated identity verification platform. Together, we will be well-positioned to achieve our joint vision of providing clients with a unique, one-stop solution for identity verification.”

This is reportedly IDnow’s second acquisition in just the past 6 months following that of Wirecard Communication Services in September of last year.

As covered in December 2020, the European Investment Bank (EIB) had decided to provide €15 million of growth funding to Germany-based identity verification platform, IDnow. Founded in 2014, IDnow covers a wide range of use cases both in regulated sectors in Europe and for completely new digital business models worldwide.

The platform allows the identity flow to be adapted to different regional, legal, and business requirements on a per-use case basis.

As explained by the IDnow team:

“IDnow uses Artificial Intelligence to check all security features on ID documents and can therefore reliably identify forged documents. Potentially, the identities of more than 7 billion customers from 193 different countries can be verified in real-time. In addition to safety, the focus is also on an uncomplicated application for the customer. Achieving five out of five stars on the Trustpilot customer rating portal, IDnow technology is particularly user-friendly.”

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China five-year plan aims for supremacy in AI, quantum computing




China’s tech industry has been hit hard by US trade battles and the economic uncertainties of the pandemic, but it’s eager to bounce back in the relatively near future. According to the Wall Street Journal, the country used its annual party meeting to outline a five-year plan for advancing technology that aids “national security and overall development.” It will create labs, foster educational programs and otherwise boost research in fields like AI, biotech, semiconductors and quantum computing.

The Chinese government added that it would increase spending on basic research (that is, studies of potential breakthroughs) by 10.6 percent in 2021, and would create a 10-year research strategy.

China has a number of technological advantages, such as its 5G availability and the sheer volume of AI research it produces. This is one of the few countries where completely driverless taxis are serving real customers. In that light, the country is really cementing some of its strong points.

However, this may also be a matter of survival. US trade restrictions have hobbled companies like Huawei and ZTE, in part due to a lack of cutting-edge chip manufacturing. The US also leads in overall research, and the Biden administration is boosting spending on advancements for 5G, AI and electric cars. As experienced as China is in some areas, it risks slipping behind if it doesn’t counter the latest American efforts.

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How Machine Learning is Being Applied to Software Development




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When Elon Musk proposed the idea of autonomous vehicles, everyone assumed it to be a hypothetical dream and never took it seriously. However, the same vehicles are now on the roads, being one of the top-selling cars in the United States.

The applications of artificial intelligence and machine learning are visible in all areas, from Google Photos in your smartphone to Amazon’s Alexa at your home, and software development is no exception. AI has already changed the way iOS and Android app developers work.

Machine learning can enhance the way a traditional software development cycle works. It allows a computer to learn and improve from the experiences without the need for programming. The sole purpose of AI and ML is to allow computers to learn automatically.

Moreover, being a software developer, you might need to specify minute details to let your computer know what it has to do. Developing software integrated with machine learning can help you make a significant difference in your developing experience.

Machine Intelligence is the last invention that humanity will ever need to make!

When it comes to how machine learning and AI help developers, only the sky’s the limit. Taking it even broader, AI has always transformed every industry it has ever entered. Here’s a quick rundown of stats that convey the same:

As the figures stated, artificial intelligence and machine learning are surely transforming the world, and the development industry is no exception. Let’s have a look at how it can help you write flawless code, deploy, and rectify bugs.

AI and ML in Development – How Does This Benefit Software Developers?

Whether you’re a person working as an android app developer or someone who writes codes for a living, you might have wondered what AI has in it for you. Here’s how developers can harness the capabilities of machine learning and AI:

1. Controlled Deployment of Code

AI and machine learning technologies help in enhancing the efficiency of code deployment activities required in development. In the development spectrum, the deployment mechanisms include a development phase where you need to upgrade your programs and applications to a newer version.

However, if you fail to execute the process properly, you need to face several risks including corruption of the software or application. With the help of AI, you can easily prevent such vulnerabilities and upgrade your code with ease.

2. Bugs and Error Identification

With the advancements in Artificial intelligence, the coding experience is getting even better and improved. It allows developers to easily spot bugs in their code and fix them instantly. They don’t have to read their code, again and again, to find potential flaws in their code anymore.

Several machine learning algorithms can automatically test your software and suggest changes.

AI-powered testing tools are certainly saving a plethora of time to developers and help them deliver their projects faster.

3. Secure Data Storage

With the ever-growing transfer of data from numerous networks, cybersecurity experts often find it complex and overwhelming to monitor every activity going on in the network. Due to this, there might be a threat or breach that may go away unnoticed, without producing any alerts.

However, with the capabilities of artificial intelligence, you can avoid issues such as delayed warnings and get notified about bugs in your code as soon as possible. These tools gradually lessen the time it takes a company to get notified about a breach.

4. Strategic Decision Making and Prototyping 

It’s a habit for a developer to go through a hefty and endless list of what needs to be included in a project or code they’re making. However, technological solutions driven by machine learning and AI are capable of analyzing and evaluating the performance of existing applications.æ

With the help of this technology, both business leaders and engineers can work on a solution that cuts down the risk and maximizes the impact. By using natural language and visual interfaces, technical domain experts can develop technologies faster.

5. Skill Enhancement

To keep evolving with the upcoming technology, you need to evolve with the advancement in technology. For the freshers and young developers, AI-based tools help them to collaborate on various software programs and share insights with fellow team members and seniors to learn more about the programming language and software.

Parting Words

While machine learning and AI simplify numerous tasks and activities related to software development, it doesn’t mean that testers and developers are going to lose their jobs. A hired android app developer will still write codes in a faster, better, and more efficient environment, supported by AI and machine learning.


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Future of Mobile Apps: Here’s Everything that’s Worth the Wait




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@devansh-khetrapalDevansh Khetrapal

Devansh writes all about tech. He mainly talks about AI, Machine Learning and Software Development.

This year has been really rough on everyone and I guess we’ve seen enough of that already, but what we’ve also seen during this period are some amazing technological inventions. With phones, however, it’s kinda gotten boring. 

Every year the mobile users are excited because of the new Snapdragon processors and other bleeding-edge specs that these devices are pumping so they can insanely outperform the previous generation smartphones, but are the mobile apps in these phones evolving as congruently?

From the most interactive social media and messaging apps like Facebook, Instagram, WhatsApp, etc, it seems like there isn’t anything beyond that. So what’s next? Well, that’s exactly what we’re going to talk about.

Here’s the Future of Mobile Apps

When we say future of mobile apps, we don’t completely mean that these technologies aren’t already here. In fact, several of these are being incorporated right now. It’s just that these are in their primitive stages of development.

Here they are:

IoT (Internet of Things)

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It’s projected that by 2023, the global spending on IoT technology will be $1.1 trillion. Through Machine Learning and integrated Artificial Intelligence (AI), it has the potential to not just enable billions of devices simultaneously but also leverage the huge volumes of actionable data that can automate diverse business processes.

What does this entail for the future of mobile apps? Well, get ready to be able to control your car, thermostats, and kitchen appliances through your mobile devices. The IoT is being presently used in Manufacturing, Transportation, Healthcare, Energy, and many other industries.

Artificial Intelligence

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AI will single handedly change the future of mobile app design.

Mobile apps are coded to operate within the constraints of certain parameters, the implications of which have to be predefined. Simply put, if you’re browsing for a homestay on Airbnb, the results you see are based on predetermined parameters like your location, your size, and amenity requirements.

Those predetermined parameters, with the assistance of AI, can evolve to a point where you’ll be able to get results based on your preferences that it learned along the way, such as the kind of accommodation you usually prefer, the kind of facilities you need, and may even suggest you buy a place because your favourite restaurant is nearby. 

Augmented Reality (AR) / Virtual Reality (VR)

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AR and VR are attracting a high amount of investments and are forecasted to reach $72.8 billion by 2024. We can already see their success in the gaming and entertainment industry with Pokemon Go, Sky Siege, Google Cardboard, iOnRoad, and Samsung Gear VR.

Brands like Jaguar Land Rover and BMW have already started using VR to conduct design and engineering evaluation sessions to finalize their visual design before they spend any money on manufacturing the parts physically.

Gradually, you’ll be able to make more immersive simulations that can revolutionize any form of architecture involved in it.

Cross-Platform Development

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The future of mobile apps will definitely make native app development obsolete. Currently, React Native offers exceptional flexibility while developing Android and iOS apps. This will save tons of time since you won’t have to develop 2 separate apps.

More importantly, cross-platform app development will eliminate the downside of having to compromise on certain nuanced features. All of this will gradually make the app development process a lot cheaper, simpler, and time-saving.


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Imagine if you could download an entire Netflix series in about 10 seconds. That’s how great the potential of 5G is. Theoretically, it has the potential to reach speeds of 10 Gigabits per second and not just high speeds, but low latency. Even in its infancy, we can witness 5-6 Gigabits per second on our smartphones in the US.

Speaking of the future of mobile apps, well, fast internet would mean faster download and upload speeds, which changes everything from Augmented and Virtual Reality, IoT, supply chain, transportation, smart cities, because everything can happen in real-time because of the latency of merely 2 – 20 milliseconds.


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Blockchain is a term being thrown around a lot lately. Well, it’s a technology that allows data to be stored globally on thousands of servers. Now because it’s decentralized, completely transparent, and immutable, it becomes difficult for one user to gain control over the network.

This means that it’s almost impossible for anyone to hack into blockchain and make changes. The future of app development depends highly on blockchain technology because of its ability to deliver highly secure mobile apps.

Wearable Devices

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You see wearables, or “smartwatches”, being popularly used as fitness bands these days. They’re smart in the sense that they’re able to tell you your heart rate, blood oxygen, count steps, are able to notify you in case of irregular heart rhythms. And of course, it does tell time.

The tech, when combined with IoT, opens up so many doors. Be it checking appointments, making calls, sending messages, getting reminders, it’s just scratching the surface. This tech has a huge potential to evolve and can eventually eliminate the need to use a smartphone. 

Wrapping Up

It’s pretty assuring that the future of mobile apps is ridiculously exciting. We can only imagine how the user experience is going to unfold. 

Be it data visualization with the help of VR and AR, or maximization of convenience with the help of wearables, they’re all going to bring about a massive change in the mobile app development trends. Hopefully, we’ve helped you scratch that itch of curiosity and you got to learn about how our interaction with the world is about to change.

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Devansh writes all about tech. He mainly talks about AI, Machine Learning and Software Development.


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