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Tag: DevOps

Snapshots from the history of AI, plus AI education resources

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In Hello World issue 12, our free magazine for computing educators, George Boukeas, DevOps Engineer for the Astro Pi Challenge here at the Foundation, introduces big moments in the history of artificial intelligence (AI) to share with your learners:The story of artificial intelligence (AI) is a story about humans trying to understand what makes them human. Some of the episodes in this story are fascinating. These could help your learners catch a glimpse of what this field is about and, with luck, compel them to investigate further.               …

MLOps vs. DevOps: Why data makes it different

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more

Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting…

AI in Production: the Final Frontier

Production is often viewed as the final frontier in the machine learning process. By now, your data scientists trained a model on your data, the machine learning and software engineers incorporated that model into an application, the DevOps team configured the automation that containerizes the application for use by the rest of the organization, and the IT department set up infrastructure to host your model’s application. At this point, most program managers flip the proverbial switch,…

Top Data Science Skills

  1. Introduction
  2. Cloud Computing
  3. Machine Learning
  4. Query Languages
  5. Python Coding
  6. Data Wrangling
  7. Summary
  8. References

This article is intended for those who are looking to become a data scientist or are generally interested in what the most marketable skills are to have as a data scientist. This information was gathered by Indeed [2] as 12 marketable skills for data scientists, and I have picked five of those 12 that I find particularly important. The job of a data scientist can require a variety of skills ranging from spreadsheets to public speaking to DevOps. With that being said, there are still some key skills that every data scientist should either be aware of or employ themselves. Some of these skills can be learned on your own, while some can be practiced in a more formal academic setting like graduate school or a bootcamp.


How to Reduce Cloud Costs: Tips from DevOps Engineers

Companies that have migrated their infrastructures to the cloud have realized the benefits of this solution but, along with that, faced a new challenge. Potential cost savings turn out to be imaginary if resources are misused. According to the 2021 report by Flexera, nearly a third of companies spend $12 million on cloud technologies, and organizations’ cloud spending is growing by an average of 9-16% per year. In this article, we’ll figure out how to eliminate unnecessary expenses and reduce cloud costs. 

Why to use cloud development services

The cloud is one of the best ways to build infrastructure and run a business. Participants in Accenture’s study noted that, by tapping into the cloud, they were able to reduce infrastructure costs in 45% of cases, increase operational efficiency in 53% of cases, and introduce new technologies (Big Data, IoT, ML) in 51% of cases.


What is DevOps and How can it give a Boost to Software Development?

Developing and implementing software in today’s fast-paced business environment requires the best software development techniques and solutions to ensure your customers get the best service and experience while using your software. 

According to a survey, 51% of DevOps users today apply DevOps to new and existing applications.  By 2026, its market will undergo a dynamic transition as advancements in automated software development and zero-touch automation technologies drive the DevOps tools’ demand.

The use of DevOps has been proven to help increase the efficiency of the software development process, leading to faster release cycles and ultimately better customer satisfaction with the products you are delivering.


Why enterprises must not ignore Azure DevOps Server

Azure DevOps is the successor to Team Foundation Server (TFS) and is said to be its advanced version. It is a complete suite of software development tools that are being used on-premises. Azure DevOps Server integrates with currently integrated development environments (IDEs) and helps teams to develop and deploy cross-functional software. It provides a set of tools and services with which you can easily manage the planning and development of your software projects through testing and deployment.

Azure DevOps practices enable IT departments to augment quality, decrease cycle times, and optimize the use of resources to improve the way software is built, delivered and operated. It increases agility and enables better software development and speeds up the delivery by providing you the following power:

Curbing cycle times
DevOps helps organizations to improve transparency and collaboration amid their development and operations teams as well as helps them to curb cycle times and enhance the traceability of every release.


Using Automated Builds in ModelOps

In this installment of the ModelOps Blog Series, we will transition from what it takes to build AI models to the process of deploying into production. Think of this as the on ramp for extracting value from your AI investments—moving your model out of the lab and into an environment where it can provide new insights for your organization or add value to customers.

Front and center is the concept of continuous integration (CI) and continuous deployment (CD). This methodology can be applied to automate the process of releasing AI models in a reproducible and reliable manner. Get ready to walk away with everything you need to know in order to leverage containers to formalize and manage AI models within your organization.

The starting point for the deployment process is a source-control, versioned AI model.


Azure Data Factory CI-CD made simple: Building and deploying ARM templates with Azure DevOps YAML…

If you work with Azure Data Factory, you probably noticed that the deployment process for this tool is unique. It is different from an API or website deployment as it generates ARM Templates and certainly at some point in time questioned “Why is so difficult to deploy it?”. Well, it doesn’t have to be…

Azure Data Factory is a great orchestration tool for the Big Data process. It has many integrations and capabilities that make the Data Engineer life very easy.

Although when we talk about deployments there are some tricks, for example, the publish button inside the workspace, that is necessary to generate the ARM Templates to be deployed. Also, there is no deployment into the development environment, as you are already working there as an editor tool.


MLOPs And Machine Learning RoadMap

By Ben Rogojan, Data Science and Data Engineering Solutions Architect

Image Created By Author — PDF Source Here

Whenever I look to learn a new topic, I create some form of learning plan. There is so much content out there that it can be difficult to approach learning in the modern era.

It’s almost comical. We have so much access to knowledge that many of us struggle to learn because we don’t know where to go.

This is why I put together roadmaps and learning plans.

Below is my MLOps learning plan that I will be taking on for the next few months.

The focus will be on first taking a quick refresher in ML as well as taking an advanced Kubernetes course.

From there I will be focused on Kubeflow, Azure ML, and DataRobot.

Background Of MLOps

In 2014 a group of Google researchers put out a paper titled Machine Learning: The High-Interest Credit Card of Technical Debt. 


Taking a Cloud-Native Approach to Software Development & Microservices

Taking a Cloud-Native Approach to Software Development & Microservices

Time for hardware and on-premises infrastructure has disappeared. With the emergence of cloud computing, most of the businesses, small or big, have already adopted or are transitioning to cloud native architecture to keep innovating in a fast and efficient manner. This approach leverages the benefits of cloud by using open-source software stack to develop and deploy easily scalable and resilient applications.

Through this blog, we will understand a cloud native approach why it matters in the world of software development.

Cloud Native Defined

Cloud native is an approach to developing, running, and optimizing applications by using advantages of cloud computing delivery model.


DevSecOps is the key to safeguarding assets and value

The market for DevSecOps is projected to grow from 32% to 34% mid-decade. DevOps is the discipline of creating a process by which code moves quickly from a developer’s environment to a production environment. By making security an integral part of software development rather than something bolted on at the end, you increase value and decrease risk. Modzy shares some insights as you consider your own DevSec Ops. The company behind AI-powered systems and applications is a platform for organizations and developers to responsibly deploy, monitor, and get value from AI at scale.

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A software platform for organizations and developers to responsibly deploy, monitor, and get value from AI – at scale.

Developing and deploying AI-powered systems and applications is a complex business, especially in our extended remote reality.