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<h1>Devops automation</h1>
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<h1>Platform automation and DevOps</h1>
<p style="clear: both"><img src="https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/ready/enterprise-scale/media/devops.png" /></p>
<p><em>Figure 1: Platform automation and DevOps.</em></p>
<h2>Planning for a DevOps approach</h2>
<p>Many traditional IT operating models aren't compatible with the cloud, and organizations must undergo operational and organizational transformation to deliver against enterprise migration targets. You should use a DevOps approach for both application and central teams.</p>
<h3>Design considerations</h3>
<p>Where central teams are concerned, you should use pipelines for continuous integration and continuous deployment. Use the pipelines to manage policy definitions, role definitions, policy assignments, management group hierarchies, and subscriptions. These pipelines help ensure that you can operationally manage multiple subscriptions while still conforming to a desired state.</p>
<p>The blanket application of a DevOps model won't instantly establish capable DevOps teams.</p>
<p>Investing in engineering capabilities and resources is critical.</p>
<p>You can arrange internal and external DevOps roles and functions from a variety of sources that align with your organization's strategy.</p>
<p>For some legacy applications, the associated application team might not have engineering resources required to align with a DevOps strategy.</p>
<h3>Design recommendations</h3>
<p>Establish a cross-functional DevOps platform team to build, manage, and maintain your enterprise-scale architecture. This team should include members from your central IT team, security, compliance, and business units to ensure that a wide spectrum of your enterprise is represented. The following list presents a recommended set of DevOps roles for a central platform team:</p>
<p><strong>PlatformOps</strong> (platform operations) for:</p>
<p>Subscription provisioning and delegation of required network, identity and access management, and policies.</p>
<p>Platform management and monitoring (holistic).</p>
<p>Cost management (holistic).</p>
<p>Platform-as-code (managing templates, scripts, and other assets).</p>
<p>Responsible for overall operations on Microsoft Azure within the Azure Active Directory tenant (managing service principals, Graph API registration, and defining roles).</p>
<p><strong>SecOps</strong> (security operations)</p>
<p>Role-based access control (RBAC) (holistic).</p>
<p>Key management (for central services, simple mail-transfer protocol, and domain controller).</p>
<p>Policy management and enforcement (holistic).</p>
<p>Security monitoring and audit (holistic).</p>
<p><strong>NetOps</strong> (network operations)</p>
<p><ul>
<li>Network management (holistic).</li>
</ul></p>
<p><strong>AppDevOps.</strong> Allow application owners to create and manage application resources through a DevOps model. The following list presents a recommended DevOps role for application teams:</p>
<p>Application migration or transformation.</p>
<p>Application management and monitoring.</p>
<p>RBAC (application resources).</p>
<p>Security monitoring and audit (application resources).</p>
<p>Cost management (application resources).</p>
<p>Network management (application resources).</p>
<p>In some instances, you might want to break AppDevOps into more granular roles such as AppDataOps for database management or AppSecOps for more security-sensitive applications.</p>
<p>Provide a central application DevOps function to support applications that don't have existing DevOps capabilities or a business case to establish one (for example, legacy applications with minimal development capabilities).</p>
<p>Use a policy-driven approach with clear RBAC boundaries to centrally enforce consistency and security across application teams. This ensures a least privilege approach is taken by using a combination of RBAC assignments and Azure Policy, and that workloads are compliant with Azure Policy assignments at all times.</p>
<p>To accelerate Azure adoption, the central platform team should establish a common set of templates and libraries for application teams to draw upon. For example, horizontal (cross-function) guidance can help to support migrations through subject matter expertise and to ensure alignment with the overall target enterprise-scale architecture.</p>
<p>Don't restrict application teams to use central artifacts or approaches because it hinders their agility. You can enforce consistent baseline configurations through a policy-driven infrastructure approach and RBAC assignments. This ensures that application (business unit) teams are flexible enough to innovate while still able to draw from a predefined set of templates.</p>
<p>Don't force application teams to use a central process or provisioning pipeline for the instantiation or management of application resources. Existing teams that already rely on a DevOps pipeline for application delivery should still be able to use the same tools they have been using. Remember that you can still use Azure Policy to maintain guardrails, independent of how resources are deployed in Azure.</p>
<h2>Define central and federated responsibilities</h2>
<p>The distribution of roles, responsibilities, and trust between central IT teams and applications teams is paramount to the operational transformation your organization must undergo when adopting the cloud at scale.</p>
<h3>Design considerations</h3>
<p>Central teams strive to maintain full control while application owners seek to maximize agility. The balance between these goals can greatly influence the success of the migration.</p>
<h3>Design recommendations</h3>
<p>The following list presents a recommended distribution of responsibilities between the central IT team and application teams. You're striving to empower migration and transformation activities with minimal central dependencies. At the same time, you want to support the centralized governance of security and operability across the entire estate.</p>
<p><strong>Application functions</strong></p>
<p>Application migration and transformation.</p>
<p>Application management and monitoring (application resources).</p>
<p>Key management (application keys).</p>
<p>RBAC (application resources).</p>
<p>Security monitoring and audit (application resources).</p>
<p>Cost management (application resources).</p>
<p>Network management (application resources).</p>
<p><strong>Central functions</strong></p>
<p>Platform as code (management of templates, scripts, and other assets).</p>
<p>Policy management and enforcement (holistic).</p>
<p>Platform management and monitoring (holistic).</p>
<p>Key management (central services).</p>
<p>Network management (including networks and network virtual appliances).</p>
<p>Security monitoring and audit (holistic).</p>
<p>Cost management (holistic).</p>
<p>An Azure DevOps model that's based on these recommendations provides the desired control for central teams and the migration agility required by application teams.</p>
<h2>Devops automation</h2>

<h3>Devops automation</h3>
<p>[youtube]</p>
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<h4>Devops automation</h4>
Platform automation and DevOps.
<h5>Devops automation</h5>
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SOURCE: <h6>Devops automation</h6> <a href="https://dev-ops.engineer/">Devops automation</a> Devops automation
#tags#[replace: -,-Devops automation] Devops automation#tags#
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<h1>Ml devops</h1>
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<h1>The Next Generation of DevOps: ML Ops</h1>

<p style="clear: both"><em><img style="float: left; margin: 0 10px 5px 0;" src="https://insidebigdata.com/wp-content/uploads/2018/04/Debashis-Saha_headshot.jpg" />In this special guest feature, Debashis Saha, Vice President of Platform Engineering at Intuit, discusses how DevOps methodologies can be applied to machine learning, in what he calls “ML Ops.” With ML Ops, he believes it can provide an end-to-end automation of the process, creating transparency and delivering efficiency and productivity for everyone involved to deliver value rapidly. As VP of Platform Engineering for Intuit, Debashis leads the engineering teams responsible for the platform, developer and application services that enable developers to be productive and innovate for our customers. Prior to this, he was the Executive VP and CTO for Jiff, Inc. Prior to Jiff, he was VP of Commerce Platform Infrastructure at eBay, where he led the engineering teams who built, managed and operated the platforms and infrastructure services for eBay. His portfolio included eBay’s entire infrastructure ranging from data centers to cloud, platforms, frameworks, data, engineering services and operations. Debashis earned an M.S. in Electrical Engineering and Computer Science from Massachusetts Institute of Technology and a Bachelor of Technology in Computer Science and Engineering from the Indian Institute of Technology, Kharagpur.</em> </p>
<p>The age of AI is upon us. As AI becomes more ubiquitous, many are finding new and innovative ways to operationalize data science in order to increase efficiency, speed and scale.</p>
<p>As I look at traditional DevOp methodologies, there are synergies and parallels that can also be applied to the data science world. The new chasm involves multiple disciplines: Data Engineering, Data Science and Software Engineering. Traditional DevOps is the battleground for developers and operations which continues in the world of data science in a more pronounced manner – data engineers, data scientists, software developers and operations. These four personas come with different requirements, constraints and velocity. It is extremely hard to balance all four that satisfy the business requirements while complying with corporate and organizational policies.</p>
<p><strong>The Rise of ML Ops</strong></p>
<p>Appropriate data engineering is necessary to transform raw data into processed data appropriate for use in machine learning algorithms. This leads to a fusion of data engineering and data science, and if not done effectively, can lead to a reduction in productivity, efficiency, and speed development, deployment and ultimately broad adoption of data science. I call this “ML Ops”, an essential element of machine learning development that complements and completes the life cycle of an ML developer.</p>
<p>ML Ops encapsulates aspects of data engineering, software engineering, and data science to provide an end-to-end view of applying intelligence from data to a business use case. A majority of data science projects stay in the labs because integration with production environments is extremely complicated, manual and prone to error. The lack of sophisticated ML Ops therefore hinders any company or business to extract intelligence from the data they already have and apply them to their business processes and triggers disillusionment of data science and machine learning in general.</p>
<p>We are starting to see automation frameworks and services emerge in the public and private domain that bridge the skill set and process gaps of data science, software development and data engineering.</p>
<p><strong>Applying ML Ops to Your Organization</strong></p>
<p>An ML Ops platform will provide end-to-end automation of the processes that involve solving a business problem. A typical automation process includes iterative life cycles in data engineering (preparation, cleaning, refining and transformation), data science (model development, training, testing, validation, and optimization) and deployment (further testing, deployment, experimentation, monitoring, performance engineering and operating). Each of these are very complex processes and have separate tools and systems that typically don’t integrate well, include lots of manual touch points and handoffs and sometimes don’t even interoperate. The first order problem is lack of visibility and transparency in the end-to-end process. A modern ML ops engineering platform will stitch together these disparate steps into a seamless workflow that will enable collaboration between everybody involved in solving the business problem.</p>
<p>We are in the very early stages of “Data Science Productivity”, similar to the days when the first tools like compilers or editors started to appear for developing software for computers. We see two primary reasons for a lack of integrated tools and platforms in this space: a) The rapidly changing landscape in each of the three contributing areas: data platforms, data science algorithms and platforms, and cloud infrastructure b) Talent and skill set gaps in comprehensively understanding all the disciplines involved to be able to provide meaningful abstraction, automation and productivity in a generic fashion that is broadly applicable and useful to a lot of real practical use cases.</p>
<p>The fundamental need of the hour is to be able to deliver and apply quality intelligence that can be trusted to a business problem rapidly. There is a tremendous need to refresh the models in near real-time, if not real-time. In addition, the business wants to experiment with a variety of intelligence for improving the customer’s experience, which means applying different cross sections of data, model and software. Lack of quality and hence testing in this whole cycle diminishes trust and repeatability of the results. In many industries, like banking and insurance, there is a regulatory need for proving reproducibility and veracity of the model using the same data. And therefore, ability to provide capabilities to test during the development, deployment and post usage are extremely critical for both efficiency and compliance.</p>
<p>As the encapsulation of Data Science with Data becomes more sophisticated, we can expect to deliver AI and machine learning in an extremely scalable manner through many cloud services. A true ML Ops driven end-to-end data science platform can have transformative impact in the world by unlocking the latent intelligence in all of the data present in each business and in public domain.</p>
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The Next Generation of DevOps: ML Ops In this special guest feature, Debashis Saha, Vice President of Platform Engineering at Intuit, discusses how DevOps methodologies can be applied to
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Эдуард Кабринский
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