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The integration of cloud solutions into the healthcare sector marks a transformative leap, delivering profound benefits that not only streamline operations but also enhance patient care.

Structured and comprehensive approach to migrating to the cloud

The program offers methodologies, best practices, tools, and resources to support organizations through each stage of migration, from assessment and planning to execution and optimization.

Developed from scratch

Cloud computing allows healthcare organizations to quickly scale their IT resources up or down based on changing demands.

Match specific business goals

Cloud solutions offer advanced analytics capabilities, enabling healthcare providers to analyze vast datasets to gain insights.

Extract new insights from your historical data

Cloud solutions offer robust security features that comply with healthcare regulations like HIPAA.

Extract new insights from your historical data

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We excel through partnerships with industry giants like AWS, GCP, and Azure, offering innovative solutions backed by leading cloud technologies.

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Kubeflow, AWS, Cloud

Kubeflow on AWS

August 9, 2023
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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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Aws Cloud Migration, Cloud, AWS, Cloud Services, Cloud Computing

Introducing ACE — our Accelerated Cloud Exploration program!

August 8, 2023
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Do you have too much data to handle and analyze?
Are your IT budgets maxed out and you are unsure if Cloud is a good alternative?
Are you uncertain if Cloud aligns with your security requirements and can align with business processes?

When it comes to migrating to the cloud there are many different scenarios and challenges our customers need to assess and tackle. One of the above questions can be the trigger moment to consider migrating to or modernizing within the cloud. But what does migration imply?

When we talk about migration it could be the traditional case of a full IT migration from on-prem or one cloud provider to the other, but it can also mean bringing a large workload — like a whole Machine Learning application — into an existing infrastructure on the cloud. We also talk about a migration case when a customer is planning to add a new component to existing infrastructure or is modernizing and reshaping their cloud infrastructure.

Since there are so many possible reasons to consider choosing Cloud and every requirement and use case is unique, we have developed a new program — the Accelerated Cloud Exploration (ACE) — to help our customers assess their status quo and get full visibility on relevant stakeholders, timelines, a detailed analysis of Cost of Ownership (TCO) along with a Testbed/Sandbox when considering migrating to the cloud.

What is it?

ACE contains the components of the AWS MAP Assess phase and combines them with the substantial migration expertise and experience of Ankercloud as well as the speed and agility that we can provide through the strength of our global team.

How does it work?

The program runs in a 4–6-week time frame in which we conduct several workshops, deep dive sessions and prepare testbeds/Sandboxes together with our customers, and create a detailed report which provides you with all aspects of cloud adoption for your needs.

What is Included?

· Migration Readiness Assessment — The first workshop focuses on examining the scope and targets of a potential migration as well as shedding light on the current platform setup, governance, and security requirements by analyzing our customers’ readiness/adoption factors.

· Discovery Workshop — Once we have the business, product, and organizational alignment, we move our focus to the current technology inventory like the existing application stack and databases to then start mapping the right services and infrastructure on AWS.

· Migration Patterns and Architectures — After the Discovery Workshops, we built an exact AWS architecture that would suit your needs. We create the exact architectural diagrams, configurations and systems that enable them to adopt new cloud services or replace existing infrastructure with AWS.

· Total Cost of Ownership (TCO) Analysis –Using this architecture and understanding of your utilization, we develop an investment plan and ROI analysis for the next 36 months by accounting for post-migration AWS costs, saving costs from alternative options, and providing the correct infrastructure sizing and configurations.

· Proof of Concept (POC) — While the previous phases of this program focus on helping you get complete visibility of all facets of cloud adoption, we go one step further to help you get a direct hands-on taste of it. Within ACE, we also include a PoC to provide our customers with a sandbox environment or application on AWS to experience the advantages of a migration firsthand and get their developers a “look and feel” of their post-migration infrastructure.

­· Carbon Emission Calculation — In every MAP Assess project we make use of the AWS Carbon Footprint tool which allows us to include detailed calculations and comparisons of on-prem vs. AWS CO2 emissions into the report and highlight CO2 savings for the customer.

How Much Does ACE Cost?

Depending on your current and future IT Infrastructure plans, we can provide ACE program free of charge (i.e. 100% discount/ funding).

Furthermore, after this program there is further incentivization in working with us — any follow-up activities that you would like to work on with us, for example — database and server migration, application migration, and creation of various IT environments are discounted by 50%.

And there’s more — If you do choose to migrate your workloads to AWS after the ACE program, you get 25% off on your AWS bills towards any new migrated workload for the first 36 months.

Sounds Interesting?

Our ACE Program, in collaboration with AWS, is the perfect way to start exploring the cloud as the next step in your IT or Product expansion and scaling plans. And you can now make that decision with an experienced external partner with potentially zero costs. If that sounds like an exciting proposition reach out to us at cloudengagement@ankercloud.com

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What are the challenges of cloud migration?

August 3, 2023
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In today's rapidly evolving digital landscape, many organizations are embracing the potential of cloud computing to drive innovation, enhance scalability, and improve operational efficiency. Nevertheless, the process of transitioning to cloud computing comes with its own set of difficulties.To ensure a successful transition, it is crucial to understand and address the obstacles that can arise during the cloud migration process. In this article, we explore some of the common challenges organizations face.

1. Data transfer and bandwidth limitations

Transferring large volumes of data to the cloud can be time-consuming and bandwidth-intensive. Limited network bandwidth or unreliable internet connectivity can result in extended migration periods, causing disruptions to normal business operations. Careful planning, including the use of data compression techniques, prioritization of critical data, and leveraging cloud-based data transfer solutions, can help mitigate these challenges.

2. Security and compliance concerns

One of the primary concerns when moving to the cloud is ensuring the security and compliance of sensitive data. Organizations must evaluate their cloud provider's security measures, including data encryption, access controls, and compliance certifications. Additionally, they need to assess whether the cloud environment aligns with their specific industry regulations and privacy requirements.

3. Compatibility and complexity of existing systems

Migrating existing systems and applications to the cloud can be challenging due to compatibility issues and complex dependencies. Legacy systems may require modifications or redevelopment to work efficiently in a cloud environment.

4. Lack of Migration Strategy and Planning

Lack of a comprehensive migration strategy and proper planning can lead to significant challenges. It's crucial to evaluate the existing infrastructure, determine the optimal cloud architecture, and establish a well-defined migration roadmap. Failure to do so may result in cost overruns, project delays, or even operational disruptions.

5. Effective cost management

Cloud migration introduces new cost models and pricing structures, such as pay-as-you-go or resource-based billing. Organizations must carefully analyze their usage patterns, optimize resource allocation, and implement cost management strategies to avoid unexpected expenses. Failure to monitor and control costs may result in budget overruns and inefficient resource utilization.

6. Vendor lock-in risks

Choosing the right cloud service provider is crucial, as switching providers later can be complicated and costly. Organizations should carefully evaluate vendor offerings, contract terms, and consider adopting a multi-cloud or hybrid cloud strategy to minimize the risk of vendor lock-in.

7. Organizational change and skills gap

Cloud migration often requires organizational and cultural changes. Employees need to adapt to new technologies, processes, and workflows. A lack of cloud expertise and skills within the organization can slow down the migration process and impact successful implementation.

8. Application dependencies and interoperability

Applications designed to operate in traditional on-premises environments may not function optimally in the cloud. Differences in infrastructure, operating systems, and dependencies can lead to compatibility issues, requiring modifications or even complete redevelopment of the applications. This challenge demands careful planning, extensive testing, and sometimes the need for skilled developers to ensure a smooth transition.

9. Operational resilience during outages or disruptions

Cloud service outages or disruptions can affect business continuity. Organizations must plan for potential risks and design resilient architectures to minimize the impact of downtime or service interruptions on critical business operations.

Navigating these challenges effectively requires a proactive and well-informed approach. Partnering with experienced cloud migration consultants or leveraging the expertise of cloud service providers can significantly ease the transition and ensure a successful migration journey.

At Ankercloud, we understand the complexities of cloud migration. Our team of experts is dedicated to helping businesses navigate these challenges and leverage the full potential of cloud technologies. Contact us today to learn more about our services and how we can support your cloud migration journey.

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WAFR and Architecture validation

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