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In today's fast-paced technological landscape, staying ahead of the competition requires seamless integration, rapid deployment, and efficient management of development and machine learning operations. Ankercloud presents DevOps and MLOps Pipeline Solutions – a game-changing approach to streamlining your workflow, reducing bottlenecks, and achieving peak operational efficiency. In this article, we explore how Ankercloud's innovative pipeline solutions can revolutionize your development workflow.

Benefits of Ankerclouds DevOps and MLOps Pipeline Solutions

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Enhanced Efficiency and Speed:

Ankercloud’s DevOps and MLOps Pipeline solutions are designed to accelerate your development lifecycle. With streamlined automation and continuous integration/continuous deployment (CI/CD) pipelines, your team can reduce manual interventions, leading to faster code deployment and quicker time-to-market for your applications and models.

Seamless Collaboration:

Collaboration is at the heart of successful development. Our Solution provides a collaborative platform where developers, testers, and data scientists can work together seamlessly. Shared pipelines, version control, and integration with popular project management tools foster a culture of teamwork, resulting in efficient problem-solving and improved software quality.

Reliable Testing and Quality Assurance:

We ensure that every code change and model update goes through rigorous testing and quality assurance processes. Automated testing environments, code reviews, and integration with testing frameworks guarantee that your software remains stable, secure, and bug-free.

Scalability and Flexibility:

As your projects grow, so do your infrastructure needs. Our solutions scale with your requirements, allowing you to handle larger workloads without compromising on performance. Whether you're managing a small application or a complex machine learning pipeline, Ankercloud solutions adapt to your needs.

Advanced Monitoring and Insights:

Gain deep insights into your development and deployment processes with comprehensive monitoring and analytics tools. We provide real-time performance metrics, logs, and reports, enabling you to make data-driven decisions to optimize your pipelines for maximum efficiency.

Secure and Compliant Practices:

Security and compliance are paramount in today's data-driven landscape. Ankercloud incorporates robust security features into its solutions, including access controls, encryption, and compliance with industry standards. Focus on your development while we take care of safeguarding your data and applications.

Simplified MLOps:

For teams working on machine learning projects, Ankerclouds' MLOps Pipeline solutions offer specialized features. From data preprocessing and model training to deployment and monitoring, We streamlines the end-to-end machine learning workflow, ensuring reproducibility and efficiency in your AI projects.

Conclusion: 

Ankercloud's DevOps and MLOps Pipeline solutions offer a transformative approach to modern development practices. By automating key processes, fostering collaboration, and optimizing deployment, our pipelines empower your teams to focus on innovation and deliver high-quality software and machine learning solutions faster than ever before. Embrace the future of development with Ankercloud's cutting-edge pipeline solutions. Contact us today to learn how we can elevate your development workflow.

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Are you tired of sifting through endless data, struggling to make sense of it all? Say goodbye to the complexities of data analysis and welcome the future of business intelligence with open arms. Ankercloud presents BI in a BOX - the all-in-one solution designed to empower your business decisions and supercharge your growth.

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What is Ankercloud's BI in a BOX Solution?

Ankercloud's BI in a BOX Solution is a comprehensive Business Intelligence (BI) package designed to streamline your data analysis processes. It provides a complete end-to-end solution for collecting, processing, visualizing, and extracting valuable insights from your data,.  It's your one-stop-shop for data driven decision,  all within a user-friendly and customizable interface.

‍Why Choose BI in a BOX Solution?

🔍 Unlock Insights, Drive Success:

 In today's fast-paced business landscape, making informed decisions is the key to staying ahead. With Ankercloud's BI in a BOX Solution, you gain a bird's-eye view of your data, allowing you to uncover valuable insights that drive growth and innovation. No more guesswork – just data-driven excellence!

📊 All-in-One Analytics: 

BI in a BOX is your one-stop-shop for comprehensive analytics. From sales trends and customer behavior to operational efficiency and financial performance, our solution empowers you with the tools to explore, visualize, and understand your data like never before. Make smarter choices, faster.

🌐 Seamless Integration:

Say goodbye to data silos! Our BI in a BOX Solution seamlessly integrates with your existing systems, ensuring that your data is always up-to-date and accurate. Whether it's CRM, ERP, or other software, we bring your data together for a holistic view of your business operations.

📈 Customizable to Your Needs:

Every business is unique, and so are your analytical needs. Ankercloud's solution is highly customizable, allowing you to tailor your analytics dashboards to match your specific industry, goals, and KPIs. No cookie-cutter solutions here – just insights that matter.

🔒 Security You Can Trust:

At Ankercloud, security is paramount. BI in a BOX  employs top-tier encryption and data protection measures, ensuring that your sensitive business information is kept safe and confidential. Focus on your data exploration; we'll take care of the security.

🎯 Competitive Edge:

Stay ahead of your competition by leveraging the power of data. With  BI in a BOX, you gain the insights needed to identify emerging trends, capitalize on new opportunities, and fine-tune your strategies for maximum impact. It's not just about being in the game – it's about leading it.

📢 Elevate Your Business with Ankercloud

Don't let valuable insights remain hidden in your data. With Ankercloud's BI in a BOX Solution, you'll elevate your business game and uncover opportunities you never knew existed. Embrace the future of business intelligence – get started with BI in a BOX now!

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The AWS Migration Acceleration Program (MAP) is a structured framework designed to facilitate seamless migration of workloads to the AWS Cloud. The program is divided into three essential phases:

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1. Migration Readiness Assessment:

 In this initial phase, organizations evaluate their existing IT landscape to determine which workloads are suitable candidates for migration. This involves analyzing dependencies, performance requirements, and potential challenges. By assessing their readiness, organizations gain a clear understanding of the scope and complexity of the migration project.

‍2. Migration Planning and Mobilize Your Resources:

Once the assessment is complete, organizations move on to the planning phase. This involves creating a comprehensive migration strategy that outlines the approach for each workload. Key considerations include selecting the appropriate migration methods (rehost, re-platform, refactor), estimating costs, and defining a timeline. Mobilizing resources involves assembling the necessary teams, tools, and technologies to execute the migration plan effectively.

3. Migrate and Modernize Your Workloads: 

The final phase is the execution of the migration plan. Workloads are migrated to the AWS Cloud based on the selected strategies. This phase also provides an opportunity to modernize and optimize workloads, taking advantage of AWS services and best practices to enhance performance, scalability, and cost efficiency. The focus is on ensuring that the migrated workloads fully leverage the benefits of the AWS Cloud environment.

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Throughout these phases, the AWS Migration Acceleration Program offers guidance, tools, and resources to streamline the migration process and enable organizations to achieve their cloud adoption goals efficiently and effectively. As technology and offerings continue to evolve, the program remains adaptable to deliver successful outcomes for businesses embracing the AWS Cloud.

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DevSecOps, which stands for Development, Security, and Operations, is an approach that integrates security practices into the software development process. It emphasizes the collaboration and cooperation between development teams, security teams, and operations teams, with the goal of integrating security measures throughout the entire software development lifecycle.

There are several reasons why organizations adopt DevSecOps:

1. Early Detection of Vulnerabilities: By incorporating security practices into the development process from the beginning, DevSecOps enables early detection and remediation of vulnerabilities. Security measures such as code analysis, vulnerability scanning, and penetration testing can be performed during development, reducing the chances of security issues going unnoticed until later stages.
2. Rapid and Continuous Delivery: DevSecOps promotes the use of automation, continuous integration, and continuous delivery (CI/CD) pipelines. This approach enables faster and more frequent software releases while maintaining security standards. Security checks and tests can be automated and integrated into the CI/CD pipeline, ensuring that security is not compromised during the fast-paced development and deployment cycles.
3. Collaboration and Shared Responsibility: DevSecOps emphasizes collaboration between development, security, and operations teams. It encourages breaking down silos and fostering a shared responsibility for security across different teams. This collaboration ensures that security considerations are not an afterthought but an integral part of the development process, leading to more secure and resilient software.
4. Compliance and Regulatory Requirements: Many industries and organizations have stringent compliance and regulatory requirements concerning data protection and security. DevSecOps helps address these requirements by integrating security controls and practices into the development process. By automating security checks and documentation, organizations can demonstrate compliance more efficiently.
5. Agile and Adaptive Security: DevSecOps aligns with the agile development methodology, allowing security practices to be implemented in an iterative and adaptive manner. Security measures can be continuously evaluated, improved, and adjusted based on changing threats and vulnerabilities. This enables organizations to respond more effectively to emerging security challenges.
6. Enhanced Risk Management: Incorporating security into the development process allows organizations to identify and manage security risks more effectively. By addressing security concerns early on, the overall risk profile of the software can be reduced. DevSecOps provides visibility into potential risks and facilitates risk mitigation strategies.

Benefits of DevSecOps

DevSecOps, a combination of “Development,” “Security,” and “Operations,” is an approach that integrates security practices into the software development and deployment process. It emphasizes collaboration, automation, and continuous monitoring to ensure security measures are incorporated from the earliest stages of development. The benefits of DevSecOps include:

a. Early identification and mitigation of security vulnerabilities

b. Faster and more efficient software development

c. Improved collaboration and communication

d. Enhanced security awareness and culture

e. Automated security testing and monitoring

f. Continuous compliance and auditing

g. Rapid incident response and recovery

h. Cost-effectiveness

Types of Security Techniques

In DevSecOps (Development, Security, and Operations), security techniques are integrated into the entire software development lifecycle to ensure the continuous delivery of secure and reliable software. Here are some common security techniques used in DevSecOps:

i. Static Application Security Testing (SAST): SAST involves analyzing the application’s source code or binary without executing it. It helps identify security vulnerabilities, such as insecure coding practices, potential backdoors, or known vulnerabilities in third-party libraries.

ii. Dynamic Application Security Testing (DAST): DAST involves testing an application in a running state to identify vulnerabilities. It simulates attacks on the application to find security weaknesses, such as injection flaws, cross-site scripting (XSS), or improper access controls.

iii. Interactive Application Security Testing (IAST): IAST combines elements of both SAST and DAST. It instruments the application during runtime to monitor its behavior and identify vulnerabilities. It provides more accurate results by analyzing code execution paths.

iv. Security Code Reviews: Manual code reviews are performed by security experts to identify security flaws that might be missed by automated tools. This technique involves a thorough examination of the codebase, looking for vulnerabilities or insecure coding practices.

v. Security Testing Automation: Automation tools can be used to perform various security tests, including vulnerability scanning, penetration testing, and security assessment. These tools help identify common vulnerabilities efficiently and enable continuous security testing.

vi. Container Security: When using containerization technologies like Docker or Kubernetes, container security techniques are essential. This includes scanning container images for vulnerabilities, enforcing secure container configurations, and monitoring container runtime behavior.

vii. Infrastructure as Code (IaC) Security: DevSecOps also focuses on securing the infrastructure by applying security practices to infrastructure-as-code (IaC) templates. This involves implementing secure configurations, scanning IaC templates for security vulnerabilities, and performing automated security checks during infrastructure deployment.

Advantages of DevSecOps:

1. Recognize Bugs and Vulnerabilities Early.

2. Use open source with assurance.

3. Reduce resource management costs.

4. Educate developers about security.

5. Minimize Legal Liability and Risk

Conclusion:

DevSecOps promotes a proactive approach to security by integrating it into the software development lifecycle. It enables early detection of vulnerabilities, facilitates faster and more frequent releases, fosters collaboration and shared responsibility, ensures compliance with regulations, supports agile and adaptive security practices, and enhances overall risk management.

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Amazon Quicksight powers data-driven organizations with unified business intelligence (Bi) at hyperscale. With the Quicksight, all users can meet varying analytic needs from the same source of truth through modern interactive dashboards, paginated reports, embedded analytics and natural language queries.

Benefits of AWS QuickSight :-

Build faster : Speed up the development by using one authoring experience to build modern dashboards and reports. Developers can quickly integrate rich analytics and ML-powered natural language query capabilities into applications with one-step, public embedding and rich APIs.

Low cost : Pay for what you use with the quicksight usage-based pricing. No need to buy thousands of end-user licenses for large scale Bi/embedded analytics deployments. With no servers or software to install or manage. Also can lower costs by removing upfront costs and complex capacity planning.

BI for everyone : Deliver insights to all your users when, where and how they need them. Users can explore modern & interactive dashboards, get insights within their applications, obtain scheduled formatted reports with reports and make decisions with ML insights.

Scalable : Quicksight is serverless, it automatically scale to tens of thousands of users without the need to set up, configure or manage your own servers. The Quicksight in-memory calculation engine, SPICE provides consistently fast response times for end users, removing the need to scale databases for high workloads.

Lets Start With QuickSight :-

Create a manifest file for uploading the dataset :

· Initially we have uploaded the data & manifest file into AWS S3.

· To create a manifest file you can refer the following.

· We have created a manifest file named manifest.json.

· Navigate to Quicksight in the AWS console.

· Select the Datasets from the left side of the console.

· Click on New dataset to upload a new dataset.

· Now you will see the multiple datasources from where you can upload the data into quicksight.

· So we are using S3 for the dataset.

· Name the Dataset and upload the manifest file directly from the local by selecting Upload options or from S3 using URL.

· Click on connect to finish the upload.

· Here you will get the uploaded dataset which was named Latest.

Create a new analysis :

· Click on the Analyses to start the analysis and dashboards.

· Click on the New analysis button to start the analysis.

· Here you will be in the analysis, where you can do analysis, make dashboards and visuals.

Publish the dashboard and make it publically available :

· First save the analysis.

· Click on the Save icon & name the dashboard, Click on SAVE.

· Now click on the Share icon and select Publish dashboard option as shown below

· Name the dashboard & click on Publish dashboard button.

· Now Click on the share icon and select the Share dashboard option as shown below.

· You will see the option Anyone on the internet (public), just enable it.

· Copy the link by simply clicking on the Copy link.

· Paste the copied link in the browser or share it to anyone to see your dashboards.

Introduction

Kubernetes monitoring is crucial for ensuring the optimal performance, availability, and reliability of your containerized applications running in a Kubernetes cluster. With the complexity and scale of Kubernetes deployments, effective monitoring becomes essential for identifying and resolving issues quickly. Datadog, a popular monitoring platform, provides comprehensive Kubernetes monitoring capabilities. It offers real-time visibility into the health and performance of your cluster, including metrics, logs, and traces. With Datadog, you can gain insights into resource utilization, application performance, and container orchestration. This enables proactive troubleshooting, efficient resource allocation, and effective capacity planning, ensuring the smooth operation of your Kubernetes environment and facilitating application scalability and stability.

1. Prerequisite.

. Install Datadog Agent on EKS

• Install Datadog Cluster Agent

• Configure permissions and secrets

a. Creating ClusterRole, ClusterRoleBinding, and ServiceAccount for allowing permission to cluster agent and datadog agent to collect metrices.

• Creating Kubernetes Secret to provide your Datadog API key

• Deploy the datadog-cluster-agent and datadog-agent on EKS using yaml files. (datadog-cluster-agent.yaml, datadog-agent.yaml.)

Install Datadog Agent on EKS:-

The Datadog Agent is free software that enables you to observe and manage your complete infrastructure in one location by gathering metrics, distributed traces, and logs from each of your nodes and reporting them.

The Agent automatically gathers and provides resource measurements (such as CPU, memory, and network traffic) from your nodes, regardless of the underlying infrastructure platform, in addition to gathering telemetry data from Kubernetes, Docker, and other infrastructure technologies.

Install Datadog Cluster Agent:-

By acting as a proxy between the API server and the node-based Agents, the Datadog Cluster Agent reduces the load on the Kubernetes API server for collecting cluster-level data. It also adds security by lowering the permissions required for the node-based Agents, and it allows Kubernetes workloads to be automatically scaled using any metric that Datadog collects.

Configure permissions and secrets:-

The following manifests can be deployed to create the permissions that the node-based Agent and Cluster Agent will need to function in your Kubernetes cluster if it implements role-based access control. The following manifests provide two sets of permissions: one for the node-based Agent and one for the cluster agent. The cluster agent has rights specifically for gathering cluster-level metrics and Kubernetes events via the Kubernetes API. For each type of Agent, deploying these two manifests will result in the creation of a ClusterRole, ClusterRoleBinding, and ServiceAccount.

2. Tasks To Do.

The Github repository URL is below. In that repository, you can find the configuration file. You must run that file on the EKS Cluster so that the node-based Agent and Cluster Agent may conduct tasks.

https://github.com/frankisinfotech/Datadog-monitoring

kubectl create -f https://raw.githubusercontent.com/DataDog/datadog-agent/master/Dockerfiles/manifests/cluster-agent/cluster-agent-rbac.yaml

kubectl create -f https://raw.githubusercontent.com/DataDog/datad og-agent/master/Dockerfiles/manifests/cluster-agent/rbac.yaml

Create a Kubernetes secret next so you may give the Agent your Datadog API key without include it in your deployment manifests.

kubectl create secret generic datadog-secret — from-literal api-key=”<YOUR_API_KEY>”

In order to provide secure Agent-to-Agent communication between the Cluster Agent and the node-based Agents, construct a secret token as follows:

Create a 32-bit long string using the link below, then use it in the command below.

echo -n <32 String long password> | base64

Use the resulting token to create a Kubernetes secret that both flavors of Agent will use to authenticate with each other:

Use the token in below command which is generated from above command.

Kubectl create secret generic datadog-auth-token –from literal=token=<TOKEN_FROM_PREVIOUS_STEP>

Deploy the Cluster Agent:-

You’re prepared to deploy the Cluster Agent now that you’ve created Kubernetes secrets using your Datadog API key and an authentication token. Copy the manifest file from the aforementioned Gitub repo link to your local computer and save it there as datadog-cluster-agent.yaml:

After copy that file to local you have to run that file on cluster so it will deploy that cluster agent on node, so use below command to deploy agent.

kubectl apply -f datadog-cluster-agent.yaml

For the Cluster Agent, the manifest establishes a Kubernetes deployment and service. The Service offers a consistent endpoint within the cluster so that node-based Agents can communicate with the Cluster Agent, wherever it may be running, while the Deployment ensures that a single Cluster Agent is always running somewhere in the cluster. It should be noted that rather than being saved in plaintext in the manifest itself, the Datadog API key and authentication token are obtained through Kubernetes secrets.

check the status of cluster agent using below command:

kubectl get pods -l app=datadog-cluster-agent

Deploy the node-based Agent:

The node-based Datadog Agent is easy to install to your cluster once the required permissions and secrets have been generated. DD_CLUSTER_AGENT_ENABLED (set to true) and DD_CLUSTER_AGENT_AUTH_TOKEN (set using Kubernetes secrets, much like in the Cluster Agent manifest) are two additional environment variables that are set in the manifest that follows the normal Kubernetes Agent manifest. Save the following manifest as datadog-agent.yaml and copy it to a local file.

Similar to the previous cluster agent, you must copy the datadog-agent file to local storage before deploying the datadog agent on the cluster. Once the node-based Agent is deployed as a DaemonSet, use the following command to make sure that one instance of the Agent is running on each node in the cluster.

kubectl apply -f -f datadog-agent.yaml

To verify that the node-based Datadog Agent is running on your cluster, run the following command:

kubectl get daemonset datadog-agent

After this all the configuration you will be able to see the resources and metrices in the datadog console.

Dive into the metrics

The resource measurements and events from your cluster should be streaming into Datadog after the Datadog Agent has been successfully deployed. The built-in Kubernetes dashboard allows you to view the data you’ve already started gathering.

You might remember from earlier in this series that an optional cluster add-on called kube-state-metrics offers specific cluster-level metrics, more specifically the counts of Kubernetes objects like the count of desired, available, and unavailable pods. If you notice that this information is missing from the dashboard, it indicates that the kube-state-metrics service has not yet been installed. You only need to deploy kube-state-metrics to your cluster to start collecting these statistics in addition to the lower-level resource metrics that the Agent already gathers.

Deploy kube-state-metrics

You may rapidly deploy the add-on and its related resources by using a set of manifests from the official kube-state-metrics project, as was discussed in Part 3 of this series. Run the following commands to get the manifests and apply them to your cluster:

git clone https://github.com/kubernetes/kube-state-metrics.git

cd kube-state-metrics

kubectl apply -f examples/standard

Below are the screenshots from datadog console.

Amazon Managed Prometheus is a fully managed backend to ingest, query metrics, store, and visualizes data using Grafana. It is highly scalable, has fast, and secure access to data, and has a unified way of monitoring all containerized applications like AWS EKS.
Amazon Managed Grafana we can be able to create Grafana dashboards and visualizations to analyze your metrics, and logs, and trace our applications. Here would be able to perform native Prometheus Query Language (PromQL) to query the metrics to analyze the data of our Kubernetes cluster.

CREATING AWS PROMETHEUS AND GRAFANA STEPS

Step 1: Create an EKS cluster with a node group

Step 2: Create a workspace in the AWS Prometheus

Mark down the Workspace ID and Endpoint-query URL this will require later.
Step 3: Setting up the Prometheus server in our Kubernetes.

Prometheus server helps to collect all the cluster metrics which is inside our EKS cluster then it will transfer to AMP.

3.1) Execute the following helm commands to add charts

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What is Kubeflow?

The Kubeflow project aims to simplify, portability, and scalability of machine learning (ML) workflow deployments on Kubernetes. Our objective is to make it simple to deploy best-of-breed open-source ML systems to a variety of infrastructures, not to replicate other services. Run Kubeflow wherever Kubernetes is installed and configured.

Need of Kubeflow?

The need for Kubeflow arises from the challenges of building, deploying, and managing machine learning workflows at scale. By providing a scalable, portable, reproducible, collaborative, and automated platform, Kubeflow enables organizations to accelerate their machine learning initiatives and improve their business outcomes.

Here are some of the main reasons why Kubeflow is needed:

Scalability: Machine learning workflows can be resource-intensive and require scaling up or down based on the size of the data and complexity of the model. Kubeflow allows you to scale your machine learning workflows based on your needs by leveraging the scalability and flexibility of Kubernetes.

Portability: Machine learning models often need to be deployed across multiple environments, such as development, staging, and production. Kubeflow provides a portable and consistent way to build, deploy, and manage machine learning workflows across different environments.

Reproducibility: Reproducibility is a critical aspect of machine learning, as it allows you to reproduce results and debug issues. Kubeflow provides a way to reproduce machine learning workflows by using containerization and version control.

Collaboration: Machine learning workflows often involve collaboration among multiple teams, including data scientists, developers, and DevOps engineers. Kubeflow provides a collaborative platform where teams can work together to build and deploy machine learning workflows.

Automation: Machine learning workflows involve multiple steps, including data preprocessing, model training, and model deployment. Kubeflow provides a way to automate these steps by defining pipelines that can be executed automatically or manually.

Architecture Diagram:

What does Kubeflow do?

Kubeflow provides a range of tools and frameworks to support the entire ML workflow, from data preparation to model training to deployment and monitoring. Here are some of the key components of Kubeflow:

Jupyter Notebooks: Kubeflow includes a Jupyter Notebook server that allows users to run Python code interactively and visualize data in real-time.

TensorFlow: Kubeflow includes TensorFlow, a popular open-source ML library, which can be used to train and deploy ML models.

TensorFlow Extended (TFX): TFX is an end-to-end ML platform for building and deploying production ML pipelines. Kubeflow integrates with TFX to provide a streamlined way to manage ML pipelines.

Katib: Kubeflow includes Katib, a framework for hyperparameter tuning and automated machine learning (AutoML).

Kubeflow Pipelines: Kubeflow Pipelines is a tool for building and deploying ML pipelines. It allows users to define complex workflows that can be run on a Kubernetes cluster.

What is Amazon SageMaker?

Amazon SageMaker is a fully-managed machine learning service that enables data scientists and developers to build, train, and deploy machine learning models at scale. Kubeflow, on the other hand, is an open-source machine learning platform that provides a framework for running machine learning workflows on Kubernetes.

Using Amazon SageMaker with Kubeflow can help streamline the machine learning workflow by providing a unified platform for model development, training, and deployment. Here are the key steps to using Amazon SageMaker with Kubeflow:

Set up a Kubeflow cluster on Amazon EKS or other Kubernetes platforms.

● Install the Amazon SageMaker operator in your Kubeflow cluster. The operator provides a custom resource definition (CRD) that allows you to create and manage SageMaker resources within your Kubeflow environment.

● Use the SageMaker CRD to create SageMaker resources such as training jobs, model endpoints, and batch transform jobs within your Kubeflow cluster.

● Run your machine learning workflow using Kubeflow pipelines, which can orchestrate SageMaker training jobs and other components of the workflow.

● Monitor and manage your machine learning workflow using Kubeflow’s web-based UI or command-line tools.

● By integrating Amazon SageMaker with Kubeflow, you can take advantage of SageMaker’s powerful features for model training and deployment, while also benefiting from Kubeflow’s flexible and scalable machine learning platform.

Amazon SageMaker Components for Kubeflow Pipelines:

Component 1: Hyperparameter tuning job

The first component runs an Amazon SageMaker hyperparameter tuning job to optimize the following hyperparameters:

· learning-rate — [0.0001, 0.1] log scale

· optimizer — [sgd, adam]

· batch-size– [32, 128, 256]

· model-type — [resnet, custom model]

Component 2: Selecting the best hyperparameters

During the hyperparameter search in the previous step, models are only trained for 10 epochs to determine well-performing hyperparameters. In the second step, the best hyperparameters are taken and the epochs are updated to 80 to give the best hyperparameters an opportunity to deliver higher accuracy in the next step.

Component 3: Training job with the best hyperparameters

The third component runs an Amazon SageMaker training job using the best hyperparameters and for higher epochs.

Component 4: Creating a model for deployment

The fourth component creates an Amazon SageMaker model artifact.

Component 5: Deploying the inference endpoint

The final component deploys a model with Amazon SageMaker deployment.

Conclusion:

Kubeflow is an open-source platform that provides a range of tools and frameworks to make it easier to run ML workloads on Kubernetes. With Kubeflow, you can easily build and deploy ML models at scale, while also benefiting from the scalability, flexibility, and reproducibility of Kubernetes.

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Amazon CloudWatch can load all the metrics in your account (both AWS resource metrics and application metrics that you provide) for search, graphing, and alarms. Metric data is kept for 15 months, enabling you to view both up-to-the-minute data and historical data.

The CloudWatch monitoring provide some basic monitoring which can be configures in some clicks, while if you want to monitor custom metrics such as the disk and memory utilization of your EC2 machine you should have to follow these steps.

Steps to configure CloudWatch Metrics on Linux Machine:

1. Go to AWS Console-> Go To IAM -> Go to Role-> create Role-> Attach CloudWatchAgentServerPolicy -> click next-> Give Role Name-> click create role

2. Attach created role to the EC2 instance on which you want to do configuration of CloudWatch Metrics.

Go to EC2-> Go to Security-> Go to Modify IAM Role-> Select the Role Name-> click on update IAM Role

3. SSH into Your EC2 instance and apply following Commands

i. wget https://s3.amazonaws.com/amazoncloudwatch-agent/ubuntu/amd64/latest/amazon-cloudwatch-agent.deb

The command will download CloudWatch Agent on your EC2 machine.

iI. sudo dpkg -i -E ./amazon-cloudwatch-agent.deb

The Command will unzip the installed package

iiI. sudo /opt/aws/amazon-cloudwatch-agent/bin/amazon-cloudwatch-agent-config-wizard

The Command start installation of CloudWatch Agent

iv. sudo /opt/aws/amazon-cloudwatch-agent/bin/amazon-cloudwatch-agent-ctl -a fetch-config -m ec2 -c file:/opt/aws/amazon-cloudwatch-agent/bin/config.json -s

The Command will run the AWS CloudWatch Agent

v. sudo /opt/aws/amazon-cloudwatch-agent/bin/amazon-cloudwatch-agent-ctl -a status

The Command Will start the AWS CloudWatch Configuration as per selected Settings.

4. Now monitor the instance from CloudWatch Console.

5. Go to the cloudwatch dashboard->click on all Metrics-> click on cwagent-> click on InstanceId

6. Select Metrics which utilization you want to check

7. This is how you can check the memory, disk and CPU utilization of your EC2 instance.

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