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jcmarchi · 2 years ago

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Generative AI from an enterprise architecture strategy perspective

New Post has been published on https://thedigitalinsider.com/generative-ai-from-an-enterprise-architecture-strategy-perspective/

Generative AI from an enterprise architecture strategy perspective

Eyal Lantzman, Global Head of Architecture, AI/ML at JPMorgan, gave this presentation at the London Generative AI Summit in November 2023.

I’ve been at JPMorgan for five years, mostly doing AI, ML, and architecture. My background is in cloud infrastructure, engineering, and platform SaaS. I normally support AI/ML development, tooling processes, and use cases.

Some interesting observations have come about from machine learning and deep learning. Foundation models and large language models are providing different new opportunities and ways for regulated enterprises to rethink how they enable those things.

So, let’s get into it.

How is machine learning done?

You have a data set and you traditionally have CPUs, although you can use GPUs as well. You run through a process and you end up with a model. You end up with something where you can pass relatively simple inputs like a row in a database or a set of features, and you get relatively simple outputs back.

We evolved roughly 20 years ago towards deep learning and have been on that journey since. You pass more data, you use GPUs, and there are some different technology changes. But what it allows you to do is pass complex inputs rather than simple ones.

Essentially, the deep learning models have some feature engineering components built in. Instead of sending the samples and petals and length and width for the Iris model and figuring out how to extract that from an image, you just send an image and it extracts those out automatically.

Governance and compliance in generative AI models

Foundation models are quite interesting because first of all, you effectively have two layers, where there’s a provider of that foundation model that uses a very large data set, and you can add as many variants as you want there. They’re GPU-based and there’s quite a bit of complexity, time, and money, but the outcome is that you can pass in complex inputs and get complex outputs.

So that’s one difference. The other is that quite a lot of them are multimodal, which means you can reuse them across different use cases, in addition to being able to take what you get out of the box for some of them and retrain or fine-tune them with a smaller data set. Again, you run additional training cycles and then get the fine-tuned models out.

Now, the interesting observation is that the first layer on top is where you get the foundation model. You might have heard statements like, “Our data scientist team likes to fine-tune models.” Yes, but that initial layer makes it available already for engineers to use GenAI, and that’s the shift that’s upon us.

It essentially moves from processes, tools, and things associated with the model development lifecycle to software because the model is an API.

But how do you govern that?

Different regulated enterprises have their own processes to govern models versus software. How do you do that with this paradigm?

That’s where things need to start shifting within the enterprise. That understanding needs to feed into the control assurance functions and control procedures, depending on what your organization calls those things.

This is in addition to the fact that most vendors today will have some GenAI in them. That essentially introduces another risk. If a regulatory company deals with a third-party vendor and that vendor happens to start using ChatGPT or LLM, if the firm wasn’t aware of that, it might not be part of their compliance profile so they need to uplift their third-party oversight processes.

They also need to be able to work contractually with those vendors to make sure that if they’re using a large language model or more general AI, they mustn’t use the firm’s data.

AWS and all the hyperscalers have those opt-out options, and this is one of those things that large enterprises check first. However, being able to think through those and introducing them into the standard procurement processes or engineering processes will become more tricky because everyone needs to understand what AI is and how it impacts the overall lifecycle of software in general.

Balancing fine-tuning and control in AI models

In an enterprise setting, to be able to use an OpenAI type of model, you need to make sure it’s protected. And, if you plan to send data to that model, you need to make sure that data is governed because you can have different regulations about where the data can be processed, and stored, and where it comes from that you might not be aware of.

Some countries have restrictions about where you can process the data, so if you happen to provision your LLM endpoint in US-1 or US central, you possibly can’t even use it.

So, being aware of those kinds of circumstances can require some sort of business logic.

Even if you do fine-tuning, you need some instructions to articulate the model to aim towards a certain goal. Or even if you fine-tune the hell out of it, you still need some kind of guardrails to evaluate the sensible outcomes.

There’s some kind of orchestration around the model itself, but the interesting point here is that this model isn’t the actual deployment, it’s a component. And that’s how thinking about it will help with some of the problems raised in how you deal with vendor-increasing prices.

What’s a component? It’s exactly like any software component. It’s a dependency you need to track from a performance perspective, cost perspective, API perspective, etc. It’s the same as you do with any dependency. It’s a component of your system. If you don’t like it, figure out how to replace it.

Now I’ll talk a bit about architecture.

Challenges and strategies for cross-cloud integration

What are those design principles? This is after analyzing different vendors, and this is my view of the world.

Treating them as a SaaS provider and as a SaaS pattern increases the control over how we deal with that because you essentially componentize it. If you have an interface, you can track it as any type of dependency from performance cost, but also from an integration with your system.

So if you’re running on AWS and you’re calling Azure OpenAPI endpoints, you’ll probably be paying for networking across cloud cost and you’ll have latency to pay for, so you need to take all of those things into account.

So, having that as an endpoint brings those dimensions front and center into that engineering world where engineering can help the rest of the data science teams.

We touched on content moderation, how it’s required, and how different vendors implement it, but it’s not consistent. They probably deal with content moderation from an ethical perspective, which might be different from an enterprise ethical perspective, which has specific language and nuances that the enterprise tries to protect.

So how do you do that?

That’s been another consideration where I think what the vendors are doing is great, but there are multiple layers of defense. There’s defense in depth, and you need ways to deal with some of that risk

To be able to effectively evaluate your total cost of ownership or the value proposition of a specific model, you need to be able to evaluate those different models. This is where if you’re working in a modern development environment, you might be working on AWS or Azure, but when you’re evaluating the model, you might be triggering a model in GCP.

Being able to have those cross-cloud considerations that start getting into the network and authentication, authorization, and all those things can become extremely important to design for and articulate in the overall architecture and strategy when dealing with those models.

Historically, there were different attempts to wrap stuff up. As cloud providers, service providers, and users of those technologies, we don’t like that because you lose the value of the ecosystem and the SDKs that those provide.

To be able to solve those problems whilst using the native APIs, the native SDK is essential because everything runs extremely fast when it comes to AI and there’s a tonne of innovation, and as soon as you start wrapping stuff then, you’re already out in terms of dealing with that problem and it’s already pointless.

How do we think about security and governance in depth?

If we start from the center, you have a model provider, and this is where the provider can be an open source one where you go through due diligence to get it into the company and do the scanning or adverse testing. It could be one that you develop, or it could be a third-party vendor such as Anthropic.

They do have some responsibility for encryption and transit, but you need to make sure that that provider is part of your process of getting into the firm. You can articulate that that provider’s dealing with generative AI, that provider will potentially be sent classified data, that provider needs to make sure that they’re not misusing that data for training, etc.

You also have the orchestrator that you need to develop, where you need to think about how to prevent prompt injection in the same way that other applications deal with SQL injection and cross-site scripting.

So, how do you prevent that?

Those are the challenges you need to consider and solve.

As you go to your endpoints, it’s about how you do content moderation of the overall request and response and then also deal with multi-stage, trying to jailbreak it through multiple attempts. This involves identifying the client, identifying who’s authenticated, articulating cross-sessions or maybe multiple sessions, and then being able to address the latency requirements.

You don’t want to kill the model’s performance by doing that, so you might have an asynchronous process that goes and analyzes the risk associated with a particular request, and then it kills it for the next time around.

Being able to understand the impact of the controls on your specific use case in terms of latency, performance, and cost is extremely important, and it’s part of your ROI. But it’s a general problem that requires thinking about how to solve it.

You’re in the process of getting a model or building a model and creating the orchestrated testing as an endpoint and developer experience lifecycle loops, etc.

But your model development cycle might have the same kind of complexity when it comes to data because if you’re in an enterprise that got to the level of maturity that they can train with real data, rather than a locked up, synthesized database, you can articulate the business need and you have approval taxes, classified data for model training, and for the appropriate people to see it.

This is where your actual environment has quite a lot of controls associated with dealing with that data. It needs to implement different data-specific compliance.

So, when you train a model, you need to train this particular region, whether it’s Indonesia or Luxembourg, or somewhere else.

Being able to kind of think about all those different dimensions is extremely important.

As you go through whatever the process is to deploy the application to production, again, you have the same data-specific requirements. There might be more because you’re talking about production applications. It’s about impacting the business rather than just processing data, so it might be even worse.

Then, it goes to the standard engineering, integration, testing, load testing, and chaos testing, because it’s your dependency. It’s another dependency of the application that you need to deal with.

And if it doesn’t scale well because there’s not enough computing in your central for open AI, then this is one of your decision points when you need to think about how this would work in the real world. How would that work when I need that capacity? Being able to have that process go through all of those layers as fast as possible is extremely important.

Threats are an ongoing area of analysis. There’s a recent example from a couple of weeks ago from apps about LLM-specific threats. You’ll see similar threats to any kind of application such as excessive agency or or denial of service. You also have ML-related risks like data poisoning and prompt object injection which are more specific to large language models.

This is one way you can communicate with your controller assurance or cyber group about those different risks, how you mitigate them, and compartmentalize all the different pieces. Whether this is a third party or something you developed, being able to articulate the associated risk will allow you to deal with that more maturely.

Navigating identity and access management

Going towards development, instead of talking about risk, I’m going to talk about how I compartmentalize, how to create the required components, how to think about all those things, and how to design those systems. It’s essentially a recipe book.

Now, the starting assumption is that as someone who’s coming from a regulated environment, you need some kind of well-defined workspace that provides security.

The starting point for your training is to figure out your identity and access management. Who’s approved to access it? What environment? What data? Where’s the region?

Once you’ve got in, what are your cloud-native environment and credentials? What do they have access to? Is it access to all the resources? Specific resources within the region or across regions? It’s about being able to articulate all of those things.

When you’re doing your interactive environment, you’ll be interacting with the repository, but you also may end up using a foundation model or RAG resource to pull into your context or be able to train jobs based on whatever the model is.

If all of them are on the same cloud provider, it’s simple. You have one or more SDLC pipelines and you go and deploy that. You can go through an SDLC process to certify and do your risk assessment, etc. But what happens if you have all of those spread across different clouds?

Essentially, you need additional pieces to ensure that you’re not connecting from a PCI environment to a non-PCI environment.

Having an identity broker that can have additional conditions about access that can enforce those controls is extremely important. This is because given the complexity in the regulatory space, there are more and more controls and it becomes more and more complex, so pushing a lot of those controls into systems that can reason about them and enforce that is extremely important.

This is where you start thinking about LLM and GenAI from a use case perspective to just an enterprise architecture pattern. You’re essentially saying, “How do we deal with that once and for all?”

Well, we identify this component that deals with identity-related problems, we articulate the different data access patterns, we identify different environments, we catalog them, and we tag them. And based on the control requirements, this is how you start dealing with those problems.

And then from that perspective, you end up with fully automated controls that you can employ, whether it’s AWS Jupyter or your VS code running in Azure to talk to Bedrock or Azure OpenAI endpoint. They’re very much the same from an architecture perspective.

Innovative approaches to content moderation

Now, I mentioned a bit about the content moderation piece, and this is where I suggested that the vendors might have things, but:

They’re not really consistent.

They don’t necessarily understand the enterprise language.

What is PI within a specific enterprise? Maybe it’s some specific kind of user ID that identifies the user, but it’s a letter and a number that LLM might never know about. But you should be careful about exposing it to others, etc.

Being able to have that level of control is important because it’s always about control when it comes to risk.

When it comes to supporting more than one provider, this is essentially where we need to standardize a lot of those things and essentially be able to say, “This provider, based on our assessment of that provider can deal with data up to x, y, and z, and in regions one to five because they don’t support deployment in Indonesia or Luxembourg.”

Being able to articulate that part of your onboarding process of that provider is extremely important because you don’t need to think about it for every use case, it’s just part of your metamodel or the information model associated with your assets within the enterprise.

That content moderation layer can be another type of AI. It can be as simple as a kill switch that’s based on regular expression, but the opportunity is there to make something learn and adjust over time. But from a pure cyber perspective, it’s a kill switch that you need to think about, how you kill something in a point solution based on the specific type of prompt.

For those familiar with AWS, there’s an AWS Gateway Load Bouncer. It’s extremely useful when we’re coming to those patterns because it allows you to have the controls as a sidecar rather than part of your application, so the data scientist or the application team can focus on your orchestrator.

You can have another team that can specialize in security that creates that other container effectively and deploys and manages that as a separate lifecycle. This is also good from a biased perspective because you could have one team that’s focused on making or breaking the model, versus the application team that tries to create or extract the value out of it.

From a production perspective, this is very similar because in the previous step, you created a whole bunch of code, and the whole purpose of that code was becoming that container, one or more depending on how you think about that.

That’s where I suggest that content moderation is yet another type of kind of container that sits outside of the application and allows you to have that separate control over the type of content moderation. Maybe it’s something that forces a request-response, maybe it’s more asynchronous and kicks it off based on the session.

You can have multiple profiles of those content moderation systems and apply them based on the specific risk and associated model.

Identity broker is the same pattern. This is extremely important because if you’re developing and testing in such environments, you want your code to be very similar to how it progresses.

In the way you got your credentials, you probably want some configuration in which you can point to a similar setup in your production environment to inject those tokens into your workload.

This is where you probably don’t have your fine-tuning in production, but you still have data access that you need to support.

So, having different types of identities that are associated with your flow and being able to interact, whether it’s the same cloud or multi-cloud, depending on your business use case, ROI, latency, etc., will be extremely important.

But this allows you to have that framework of thinking, Is this box a different identity boundary? Yes or no? If it’s a no, then it’s simple. It’s an IM policy in AWS as an example, versus a different cloud, how do you federate access to it? How do you rotate credential secrets?

Conclusion

To summarise, you have two types of flows. In standard ML, you have the MDLC flow where you go and train a model and containerize it.

In GenAI, you have the MDLC only when you fine-tune. If you didn’t fine-tune, you run through pure SDLC flow. It’s a container that you just containerized that you test and do all those different steps.

You don’t have the actual data scientists necessarily involved in that process. That’s the opportunity but also the change that you need to introduce to the enterprise thinking and the cyber maturity associated with that.

Think through how engineers, who traditionally don’t really have access to production data, will be able to test those things in real life. Create all sorts of interesting discussions about the environments where you can do secure development with data versus standard developer environments with mock data or synthesized data.

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mariacallous · 28 days ago

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Elon Musk’s so-called Department of Government Efficiency (DOGE) used artificial intelligence from Meta’s Llama model to comb through and analyze emails from federal workers.

Materials viewed by WIRED show that DOGE affiliates within the Office of Personnel Management (OPM) tested and used Meta’s Llama 2 model to review and classify responses from federal workers to the infamous “Fork in the Road” email that was sent across the government in late January.

The email offered deferred resignation to anyone opposed to changes the Trump administration was making to its federal workforce, including an enforced return-to-office policy, downsizing, and a requirement to be “loyal.” To leave their position, recipients merely needed to reply with the word “resign.” This email closely mirrored one that Musk sent to Twitter employees shortly after he took over the company in 2022.

Records show that Llama was deployed to sort through email responses from federal workers to determine how many accepted the offer. The model appears to have run locally, according to materials viewed by WIRED, meaning it’s unlikely to have sent data over the internet.

Meta and OPM did not respond to requests for comment from WIRED.

Meta CEO Mark Zuckerberg appeared alongside other Silicon Valley tech leaders like Musk and Amazon founder Jeff Bezos at Trump’s inauguration in January, but little has been publicly known about his company’s tech being used in government. Because of Llama’s open-source nature, the tool can easily be used by the government to support Musk’s goals without the company’s explicit consent.

Soon after Trump took office in January, DOGE operatives burrowed into OPM, an independent agency that essentially serves as the human resources department for the federal government. The new administration’s first big goal for the agency was to create a government-wide email service, according to current and former OPM employees. Riccardo Biasini, a former Tesla engineer, was involved in building the infrastructure for the service that would send out the original “Fork in the Road” email, according to material viewed by WIRED and reviewed by two government tech workers.

In late February, weeks after the Fork email, OPM sent out another request to all government workers and asked them to submit five bullet points outlining what they accomplished each week. These emails threw a number of agencies into chaos, with workers unsure how to manage email responses that had to be mindful of security clearances and sensitive information. (Adding to the confusion, it has been reported that some workers who turned on read receipts say they found that the responses weren’t actually being opened.) In February, NBC News reported that these emails were expected to go into an AI system for analysis. While the materials seen by WIRED do not explicitly show DOGE affiliates analyzing these weekly “five points” emails with Meta’s Llama models, the way they did with the Fork emails, it wouldn’t be difficult for them to do so, two federal workers tell WIRED.

“We don’t know for sure,” says one federal worker on whether DOGE used Meta’s Llama to review the “five points” emails. “Though if they were smart they’d reuse their code.”

DOGE did not appear to use Musk’s own AI model, Grok, when it set out to build the government-wide email system in the first few weeks of the Trump administration. At the time, Grok was a proprietary model belonging to xAI, and access to its API was limited. But earlier this week, Microsoft announced that it would begin hosting xAi’s Grok 3 models as options in its Azure AI Foundry, making the xAI models more accessible in Microsoft environments like the one used at OPM. This potentially, should they want it, would enable Grok as an option as an AI system going forward. In February, Palantir struck a deal to include Grok as an AI option in the company’s software, which is frequently used in government.

Over the past few months, DOGE has rolled out and used a variety of AI-based tools at government agencies. In March, WIRED reported that the US Army was using a tool called CamoGPT to remove DEI-related language from training materials. The General Services Administration rolled out “GSAi” earlier this year, a chatbot aimed at boosting overall agency productivity. OPM has also accessed software called AutoRIF that could assist in the mass firing of federal workers.

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digitaldetoxworld · 1 month ago

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Web to Mobile: Building Seamless Apps with .NET"

.NET is a effective, flexible, and open-supply developer platform created with the aid of Microsoft. It enables the creation of a huge range of applications—from computing device to cellular, net, cloud, gaming, and IoT. Over the years, .NET has evolved substantially and has become one of the maximum extensively used frameworks inside the software improvement enterprise.

Dot Net Programming Language

A Brief History of .NET

The .NET Framework become first delivered through Microsoft in the early 2000s. The original cause turned into to offer a steady item-oriented programming surroundings regardless of whether code became stored and finished locally, remotely, or via the internet.

Over time, Microsoft developed .NET right into a cross-platform, open-supply framework. In 2016, Microsoft launched .NET Core, a modular, high-performance, cross-platform implementation of .NET. In 2020, the company unified all its .NET technologies beneath one umbrella with the discharge of .NET five, and later persisted with .NET 6, .NET 7, and past.

Today, the unified platform is actually called .NET, and it allows builders to build apps for Windows, macOS, Linux, iOS, Android, and greater using a single codebase.

Key Features of .NET

1. Cross-Platform Development

One of the maximum tremendous features of present day .NET (publish .NET Core) is its ability to run on a couple of platforms. Developers can construct and deploy apps on Windows, Linux, and macOS with out enhancing their codebases.

2. Multiple Language Support

.NET supports numerous programming languages, together with:

C# – the maximum extensively used language in .NET development

F# – a purposeful-first programming language

Visual Basic – an smooth-to-analyze language, regularly used in legacy programs

This multilingual capability allows developers to pick out the nice language for their precise use cases.

3. Extensive Library and Framework Support

.NET offers a comprehensive base magnificence library (BCL) and framework libraries that aid the whole lot from record studying/writing to XML manipulation, statistics get entry to, cryptography, and extra.

Four. ASP.NET for Web Development

ASP.NET is a part of the .NET platform specially designed for net improvement. ASP.NET Core, the cross-platform model, permits builders to build scalable internet APIs, dynamic web sites, and actual-time packages the usage of technology like SignalR.

5. Rich Development Environment

.NET integrates seamlessly with Visual Studio, one of the most function-wealthy integrated development environments (IDEs) available. Visual Studio offers capabilities together with IntelliSense, debugging tools, challenge templates, and code refactoring.

6. Performance and Scalability

.NET is thought for high performance and scalability, especially with its guide for asynchronous programming using async/wait for and its Just-In-Time (JIT) compilation.

7. Secure and Reliable

.NET presents sturdy safety features, including code get entry to security, role-based protection, and cryptography training. It also handles reminiscence management thru rubbish series, minimizing reminiscence leaks.

Common Applications Built with .NET

1. Web Applications

With ASP.NET Core, builders can create cutting-edge, scalable internet programs and RESTful APIs. Razor Pages and Blazor are technology within ASP.NET Core that help server-facet and purchaser-facet rendering.

2. Desktop Applications

Using Windows Forms or Windows Presentation Foundation (WPF), builders can build conventional computing device applications. .NET MAUI (Multi-platform App UI) now extends this functionality to move-platform computer and cellular programs.

3. Mobile Applications

Through Xamarin (now incorporated into .NET MAUI), developers can create native mobile applications for Android and iOS the usage of C#.

4. Cloud-Based Applications

.NET is nicely-acceptable for cloud development, in particular with Microsoft Azure. Developers can build cloud-local apps, serverless capabilities, and containerized microservices the usage of Docker and Kubernetes.

5. IoT Applications

.NET helps Internet of Things (IoT) development, allowing builders to construct applications that engage with sensors and gadgets.

6. Games

With the Unity sport engine, which helps C#, developers can use .NET languages to create 2D, three-D, AR, and VR games.

Components of .NET

1. .NET SDK

The Software Development Kit includes everything had to build and run .NET packages: compilers, libraries, and command-line tools.

2. CLR (Common Language Runtime)

It handles reminiscence control, exception managing, and rubbish collection.

Three. BCL (Base Class Library)

The BCL offers center functionalities including collections, record I/O, records kinds, and extra.

4. NuGet

NuGet is the package manager for .NET. It lets in builders to install, manage, and share libraries without problems.

Modern .NET Versions

.NET five (2020): Unified the .NET platform (Core + Framework)

.NET 7 (2022): Further overall performance enhancements and more desirable APIs

.NET 8 (2023): Continued attention on cloud-native, cellular, and web improvement

Advantages of Using .NET

Cross-platform assist – construct as soon as, run everywhere

Large developer network – widespread sources, libraries, and frameworks

Robust tooling – especially with Visual Studio and JetBrains Rider

Active improvement – backed by using Microsoft and open-source community

Challenges and Considerations

Learning curve – particularly for beginners due to its giant atmosphere

Legacy framework – older .NET Framework tasks aren't like minded with .NET Core or more recent variations without migration

Platform differences – sure APIs or libraries might also behave in a different way throughout operating systems

Getting Started with .NET

To begin growing with .NET:

Install the .NET SDK from the legitimate .NET internet site.

Create a new project: Use the dotnet new command or Visual Studio templates.

Write code: Develop your logic the usage of C#, F#, or VB.NET.

#btech students #bca students #online programming courses #offline institute programming courses #regular colleges university #Dot Net Programming Language

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cyber-sec · 9 months ago

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Azure API Management Vulnerability Let Attackers Escalate Privileges

Source: https://gbhackers.com/azure-api-management-vulnerability/

More info: https://binarysecurity.no/posts/2024/09/apim-privilege-escalation

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cyberanalyst023 · 5 months ago

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Exploring the Azure Technology Stack: A Solution Architect’s Journey

Kavin

As a solution architect, my career revolves around solving complex problems and designing systems that are scalable, secure, and efficient. The rise of cloud computing has transformed the way we think about technology, and Microsoft Azure has been at the forefront of this evolution. With its diverse and powerful technology stack, Azure offers endless possibilities for businesses and developers alike. My journey with Azure began with Microsoft Azure training online, which not only deepened my understanding of cloud concepts but also helped me unlock the potential of Azure’s ecosystem.

In this blog, I will share my experience working with a specific Azure technology stack that has proven to be transformative in various projects. This stack primarily focuses on serverless computing, container orchestration, DevOps integration, and globally distributed data management. Let’s dive into how these components come together to create robust solutions for modern business challenges.

Understanding the Azure Ecosystem

Azure’s ecosystem is vast, encompassing services that cater to infrastructure, application development, analytics, machine learning, and more. For this blog, I will focus on a specific stack that includes:

Azure Functions for serverless computing.

Azure Kubernetes Service (AKS) for container orchestration.

Azure DevOps for streamlined development and deployment.

Azure Cosmos DB for globally distributed, scalable data storage.

Each of these services has unique strengths, and when used together, they form a powerful foundation for building modern, cloud-native applications.

1. Azure Functions: Embracing Serverless Architecture

Serverless computing has redefined how we build and deploy applications. With Azure Functions, developers can focus on writing code without worrying about managing infrastructure. Azure Functions supports multiple programming languages and offers seamless integration with other Azure services.

Real-World Application

In one of my projects, we needed to process real-time data from IoT devices deployed across multiple locations. Azure Functions was the perfect choice for this task. By integrating Azure Functions with Azure Event Hubs, we were able to create an event-driven architecture that processed millions of events daily. The serverless nature of Azure Functions allowed us to scale dynamically based on workload, ensuring cost-efficiency and high performance.

Key Benefits:

Auto-scaling: Automatically adjusts to handle workload variations.

Cost-effective: Pay only for the resources consumed during function execution.

Integration-ready: Easily connects with services like Logic Apps, Event Grid, and API Management.

2. Azure Kubernetes Service (AKS): The Power of Containers

Containers have become the backbone of modern application development, and Azure Kubernetes Service (AKS) simplifies container orchestration. AKS provides a managed Kubernetes environment, making it easier to deploy, manage, and scale containerized applications.

Real-World Application

In a project for a healthcare client, we built a microservices architecture using AKS. Each service—such as patient records, appointment scheduling, and billing—was containerized and deployed on AKS. This approach provided several advantages:

Isolation: Each service operated independently, improving fault tolerance.

Scalability: AKS scaled specific services based on demand, optimizing resource usage.

Observability: Using Azure Monitor, we gained deep insights into application performance and quickly resolved issues.

The integration of AKS with Azure DevOps further streamlined our CI/CD pipelines, enabling rapid deployment and updates without downtime.

Key Benefits:

Managed Kubernetes: Reduces operational overhead with automated updates and patching.

Multi-region support: Enables global application deployments.

Built-in security: Integrates with Azure Active Directory and offers role-based access control (RBAC).

3. Azure DevOps: Streamlining Development Workflows

Azure DevOps is an all-in-one platform for managing development workflows, from planning to deployment. It includes tools like Azure Repos, Azure Pipelines, and Azure Artifacts, which support collaboration and automation.

Real-World Application

For an e-commerce client, we used Azure DevOps to establish an efficient CI/CD pipeline. The project involved multiple teams working on front-end, back-end, and database components. Azure DevOps provided:

Version control: Using Azure Repos for centralized code management.

Automated pipelines: Azure Pipelines for building, testing, and deploying code.

Artifact management: Storing dependencies in Azure Artifacts for seamless integration.

The result? Deployment cycles that previously took weeks were reduced to just a few hours, enabling faster time-to-market and improved customer satisfaction.

Key Benefits:

End-to-end integration: Unifies tools for seamless development and deployment.

Scalability: Supports projects of all sizes, from startups to enterprises.

Collaboration: Facilitates team communication with built-in dashboards and tracking.

4. Azure Cosmos DB: Global Data at Scale

Azure Cosmos DB is a globally distributed, multi-model database service designed for mission-critical applications. It guarantees low latency, high availability, and scalability, making it ideal for applications requiring real-time data access across multiple regions.

Real-World Application

In a project for a financial services company, we used Azure Cosmos DB to manage transaction data across multiple continents. The database’s multi-region replication ensure data consistency and availability, even during regional outages. Additionally, Cosmos DB’s support for multiple APIs (SQL, MongoDB, Cassandra, etc.) allowed us to integrate seamlessly with existing systems.

Key Benefits:

Global distribution: Data is replicated across regions with minimal latency.

Flexibility: Supports various data models, including key-value, document, and graph.

SLAs: Offers industry-leading SLAs for availability, throughput, and latency.

Building a Cohesive Solution

Combining these Azure services creates a technology stack that is flexible, scalable, and efficient. Here’s how they work together in a hypothetical solution:

Data Ingestion: IoT devices send data to Azure Event Hubs.

Processing: Azure Functions processes the data in real-time.

Storage: Processed data is stored in Azure Cosmos DB for global access.

Application Logic: Containerized microservices run on AKS, providing APIs for accessing and manipulating data.

Deployment: Azure DevOps manages the CI/CD pipeline, ensuring seamless updates to the application.

This architecture demonstrates how Azure’s technology stack can address modern business challenges while maintaining high performance and reliability.

Final Thoughts

My journey with Azure has been both rewarding and transformative. The training I received at ACTE Institute provided me with a strong foundation to explore Azure’s capabilities and apply them effectively in real-world scenarios. For those new to cloud computing, I recommend starting with a solid training program that offers hands-on experience and practical insights.

As the demand for cloud professionals continues to grow, specializing in Azure’s technology stack can open doors to exciting opportunities. If you’re based in Hyderabad or prefer online learning, consider enrolling in Microsoft Azure training in Hyderabad to kickstart your journey.

Azure’s ecosystem is continuously evolving, offering new tools and features to address emerging challenges. By staying committed to learning and experimenting, we can harness the full potential of this powerful platform and drive innovation in every project we undertake.

#cybersecurity #database #marketingstrategy #digitalmarketing #adtech #artificialintelligence #machinelearning #ai

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itcareerblogs · 6 months ago

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Top 10 In- Demand Tech Jobs in 2025

Technology is growing faster than ever, and so is the need for skilled professionals in the field. From artificial intelligence to cloud computing, businesses are looking for experts who can keep up with the latest advancements. These tech jobs not only pay well but also offer great career growth and exciting challenges.

In this blog, we’ll look at the top 10 tech jobs that are in high demand today. Whether you’re starting your career or thinking of learning new skills, these jobs can help you plan a bright future in the tech world.

1. AI and Machine Learning Specialists

Artificial Intelligence (AI) and Machine Learning are changing the game by helping machines learn and improve on their own without needing step-by-step instructions. They’re being used in many areas, like chatbots, spotting fraud, and predicting trends.

Key Skills: Python, TensorFlow, PyTorch, data analysis, deep learning, and natural language processing (NLP).

Industries Hiring: Healthcare, finance, retail, and manufacturing.

Career Tip: Keep up with AI and machine learning by working on projects and getting an AI certification. Joining AI hackathons helps you learn and meet others in the field.

2. Data Scientists

Data scientists work with large sets of data to find patterns, trends, and useful insights that help businesses make smart decisions. They play a key role in everything from personalized marketing to predicting health outcomes.

Key Skills: Data visualization, statistical analysis, R, Python, SQL, and data mining.

Industries Hiring: E-commerce, telecommunications, and pharmaceuticals.

Career Tip: Work with real-world data and build a strong portfolio to showcase your skills. Earning certifications in data science tools can help you stand out.

3. Cloud Computing Engineers: These professionals create and manage cloud systems that allow businesses to store data and run apps without needing physical servers, making operations more efficient.

Key Skills: AWS, Azure, Google Cloud Platform (GCP), DevOps, and containerization (Docker, Kubernetes).

Industries Hiring: IT services, startups, and enterprises undergoing digital transformation.

Career Tip: Get certified in cloud platforms like AWS (e.g., AWS Certified Solutions Architect).

4. Cybersecurity Experts

Cybersecurity professionals protect companies from data breaches, malware, and other online threats. As remote work grows, keeping digital information safe is more crucial than ever.

Key Skills: Ethical hacking, penetration testing, risk management, and cybersecurity tools.

Industries Hiring: Banking, IT, and government agencies.

Career Tip: Stay updated on new cybersecurity threats and trends. Certifications like CEH (Certified Ethical Hacker) or CISSP (Certified Information Systems Security Professional) can help you advance in your career.

5. Full-Stack Developers

Full-stack developers are skilled programmers who can work on both the front-end (what users see) and the back-end (server and database) of web applications.

Key Skills: JavaScript, React, Node.js, HTML/CSS, and APIs.

Industries Hiring: Tech startups, e-commerce, and digital media.

Career Tip: Create a strong GitHub profile with projects that highlight your full-stack skills. Learn popular frameworks like React Native to expand into mobile app development.

6. DevOps Engineers

DevOps engineers help make software faster and more reliable by connecting development and operations teams. They streamline the process for quicker deployments.

Key Skills: CI/CD pipelines, automation tools, scripting, and system administration.

Industries Hiring: SaaS companies, cloud service providers, and enterprise IT.

Career Tip: Earn key tools like Jenkins, Ansible, and Kubernetes, and develop scripting skills in languages like Bash or Python. Earning a DevOps certification is a plus and can enhance your expertise in the field.

7. Blockchain Developers

They build secure, transparent, and unchangeable systems. Blockchain is not just for cryptocurrencies; it’s also used in tracking supply chains, managing healthcare records, and even in voting systems.

Key Skills: Solidity, Ethereum, smart contracts, cryptography, and DApp development.

Industries Hiring: Fintech, logistics, and healthcare.

Career Tip: Create and share your own blockchain projects to show your skills. Joining blockchain communities can help you learn more and connect with others in the field.

8. Robotics Engineers

Robotics engineers design, build, and program robots to do tasks faster or safer than humans. Their work is especially important in industries like manufacturing and healthcare.

Key Skills: Programming (C++, Python), robotics process automation (RPA), and mechanical engineering.

Industries Hiring: Automotive, healthcare, and logistics.

Career Tip: Stay updated on new trends like self-driving cars and AI in robotics.

9. Internet of Things (IoT) Specialists

IoT specialists work on systems that connect devices to the internet, allowing them to communicate and be controlled easily. This is crucial for creating smart cities, homes, and industries.

Key Skills: Embedded systems, wireless communication protocols, data analytics, and IoT platforms.

Industries Hiring: Consumer electronics, automotive, and smart city projects.

Career Tip: Create IoT prototypes and learn to use platforms like AWS IoT or Microsoft Azure IoT. Stay updated on 5G technology and edge computing trends.

10. Product Managers

Product managers oversee the development of products, from idea to launch, making sure they are both technically possible and meet market demands. They connect technical teams with business stakeholders.

Key Skills: Agile methodologies, market research, UX design, and project management.

Industries Hiring: Software development, e-commerce, and SaaS companies.

Career Tip: Work on improving your communication and leadership skills. Getting certifications like PMP (Project Management Professional) or CSPO (Certified Scrum Product Owner) can help you advance.

Importance of Upskilling in the Tech Industry

Stay Up-to-Date: Technology changes fast, and learning new skills helps you keep up with the latest trends and tools.

Grow in Your Career: By learning new skills, you open doors to better job opportunities and promotions.

Earn a Higher Salary: The more skills you have, the more valuable you are to employers, which can lead to higher-paying jobs.

Feel More Confident: Learning new things makes you feel more prepared and ready to take on tougher tasks.

Adapt to Changes: Technology keeps evolving, and upskilling helps you stay flexible and ready for any new changes in the industry.

Top Companies Hiring for These Roles

Global Tech Giants: Google, Microsoft, Amazon, and IBM.

Startups: Fintech, health tech, and AI-based startups are often at the forefront of innovation.

Consulting Firms: Companies like Accenture, Deloitte, and PwC increasingly seek tech talent.

In conclusion, the tech world is constantly changing, and staying updated is key to having a successful career. In 2025, jobs in fields like AI, cybersecurity, data science, and software development will be in high demand. By learning the right skills and keeping up with new trends, you can prepare yourself for these exciting roles. Whether you're just starting or looking to improve your skills, the tech industry offers many opportunities for growth and success.

#Top 10 Tech Jobs in 2025 #In- Demand Tech Jobs #High paying Tech Jobs #artificial intelligence #datascience #cybersecurity

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fabvancesolution · 8 months ago

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The Future of Web Development: Trends, Techniques, and Tools

Web development is a dynamic field that is continually evolving to meet the demands of an increasingly digital world. With businesses relying more on online presence and user experience becoming a priority, web developers must stay abreast of the latest trends, technologies, and best practices. In this blog, we’ll delve into the current landscape of web development, explore emerging trends and tools, and discuss best practices to ensure successful web projects.

Understanding Web Development

Web development involves the creation and maintenance of websites and web applications. It encompasses a variety of tasks, including front-end development (what users see and interact with) and back-end development (the server-side that powers the application). A successful web project requires a blend of design, programming, and usability skills, with a focus on delivering a seamless user experience.

Key Trends in Web Development

Progressive Web Apps (PWAs): PWAs are web applications that provide a native app-like experience within the browser. They offer benefits like offline access, push notifications, and fast loading times. By leveraging modern web capabilities, PWAs enhance user engagement and can lead to higher conversion rates.

Single Page Applications (SPAs): SPAs load a single HTML page and dynamically update content as users interact with the app. This approach reduces page load times and provides a smoother experience. Frameworks like React, Angular, and Vue.js have made developing SPAs easier, allowing developers to create responsive and efficient applications.

Responsive Web Design: With the increasing use of mobile devices, responsive design has become essential. Websites must adapt to various screen sizes and orientations to ensure a consistent user experience. CSS frameworks like Bootstrap and Foundation help developers create fluid, responsive layouts quickly.

Voice Search Optimization: As voice-activated devices like Amazon Alexa and Google Home gain popularity, optimizing websites for voice search is crucial. This involves focusing on natural language processing and long-tail keywords, as users tend to speak in full sentences rather than typing short phrases.

Artificial Intelligence (AI) and Machine Learning: AI is transforming web development by enabling personalized user experiences and smarter applications. Chatbots, for instance, can provide instant customer support, while AI-driven analytics tools help developers understand user behavior and optimize websites accordingly.

Emerging Technologies in Web Development

JAMstack Architecture: JAMstack (JavaScript, APIs, Markup) is a modern web development architecture that decouples the front end from the back end. This approach enhances performance, security, and scalability by serving static content and fetching dynamic content through APIs.

WebAssembly (Wasm): WebAssembly allows developers to run high-performance code on the web. It opens the door for languages like C, C++, and Rust to be used for web applications, enabling complex computations and graphics rendering that were previously difficult to achieve in a browser.

Serverless Computing: Serverless architecture allows developers to build and run applications without managing server infrastructure. Platforms like AWS Lambda and Azure Functions enable developers to focus on writing code while the cloud provider handles scaling and maintenance, resulting in more efficient workflows.

Static Site Generators (SSGs): SSGs like Gatsby and Next.js allow developers to build fast and secure static websites. By pre-rendering pages at build time, SSGs improve performance and enhance SEO, making them ideal for blogs, portfolios, and documentation sites.

API-First Development: This approach prioritizes building APIs before developing the front end. API-first development ensures that various components of an application can communicate effectively and allows for easier integration with third-party services.

Best Practices for Successful Web Development

Focus on User Experience (UX): Prioritizing user experience is essential for any web project. Conduct user research to understand your audience's needs, create wireframes, and test prototypes to ensure your design is intuitive and engaging.

Emphasize Accessibility: Making your website accessible to all users, including those with disabilities, is a fundamental aspect of web development. Adhere to the Web Content Accessibility Guidelines (WCAG) by using semantic HTML, providing alt text for images, and ensuring keyboard navigation is possible.

Optimize Performance: Website performance significantly impacts user satisfaction and SEO. Optimize images, minify CSS and JavaScript, and leverage browser caching to ensure fast loading times. Tools like Google PageSpeed Insights can help identify areas for improvement.

Implement Security Best Practices: Security is paramount in web development. Use HTTPS to encrypt data, implement secure authentication methods, and validate user input to protect against vulnerabilities. Regularly update dependencies to guard against known exploits.

Stay Current with Technology: The web development landscape is constantly changing. Stay informed about the latest trends, tools, and technologies by participating in online courses, attending webinars, and engaging with the developer community. Continuous learning is crucial to maintaining relevance in this field.

Essential Tools for Web Development

Version Control Systems: Git is an essential tool for managing code changes and collaboration among developers. Platforms like GitHub and GitLab facilitate version control and provide features for issue tracking and code reviews.

Development Frameworks: Frameworks like React, Angular, and Vue.js streamline the development process by providing pre-built components and structures. For back-end development, frameworks like Express.js and Django can speed up the creation of server-side applications.

Content Management Systems (CMS): CMS platforms like WordPress, Joomla, and Drupal enable developers to create and manage websites easily. They offer flexibility and scalability, making it simple to update content without requiring extensive coding knowledge.

Design Tools: Tools like Figma, Sketch, and Adobe XD help designers create user interfaces and prototypes. These tools facilitate collaboration between designers and developers, ensuring that the final product aligns with the initial vision.

Analytics and Monitoring Tools: Google Analytics, Hotjar, and other analytics tools provide insights into user behavior, allowing developers to assess the effectiveness of their websites. Monitoring tools can alert developers to issues such as downtime or performance degradation.

Conclusion

Web development is a rapidly evolving field that requires a blend of creativity, technical skills, and a user-centric approach. By understanding the latest trends and technologies, adhering to best practices, and leveraging essential tools, developers can create engaging and effective web experiences. As we look to the future, those who embrace innovation and prioritize user experience will be best positioned for success in the competitive world of web development. Whether you are a seasoned developer or just starting, staying informed and adaptable is key to thriving in this dynamic landscape.

more about details :- https://fabvancesolutions.com/

#fabvancesolutions #digitalagency #digitalmarketingservices #graphic design #startup #ecommerce #branding #marketing #digitalstrategy #googleimagesmarketing

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bdccglobal · 1 year ago

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Unlocking Seamless API Management with Azure! Explore the Essentials and Boost Your Business.

#Azure API Management #'API #tech innovation

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digicode1 · 9 months ago

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Cloud Agnostic: Achieving Flexibility and Independence in Cloud Management

As businesses increasingly migrate to the cloud, they face a critical decision: which cloud provider to choose? While AWS, Microsoft Azure, and Google Cloud offer powerful platforms, the concept of "cloud agnostic" is gaining traction. Cloud agnosticism refers to a strategy where businesses avoid vendor lock-in by designing applications and infrastructure that work across multiple cloud providers. This approach provides flexibility, independence, and resilience, allowing organizations to adapt to changing needs and avoid reliance on a single provider.

What Does It Mean to Be Cloud Agnostic?

Being cloud agnostic means creating and managing systems, applications, and services that can run on any cloud platform. Instead of committing to a single cloud provider, businesses design their architecture to function seamlessly across multiple platforms. This flexibility is achieved by using open standards, containerization technologies like Docker, and orchestration tools such as Kubernetes.

Key features of a cloud agnostic approach include:

Interoperability: Applications must be able to operate across different cloud environments.

Portability: The ability to migrate workloads between different providers without significant reconfiguration.

Standardization: Using common frameworks, APIs, and languages that work universally across platforms.

Benefits of Cloud Agnostic Strategies

Avoiding Vendor Lock-InThe primary benefit of being cloud agnostic is avoiding vendor lock-in. Once a business builds its entire infrastructure around a single cloud provider, it can be challenging to switch or expand to other platforms. This could lead to increased costs and limited innovation. With a cloud agnostic strategy, businesses can choose the best services from multiple providers, optimizing both performance and costs.

Cost OptimizationCloud agnosticism allows companies to choose the most cost-effective solutions across providers. As cloud pricing models are complex and vary by region and usage, a cloud agnostic system enables businesses to leverage competitive pricing and minimize expenses by shifting workloads to different providers when necessary.

Greater Resilience and UptimeBy operating across multiple cloud platforms, organizations reduce the risk of downtime. If one provider experiences an outage, the business can shift workloads to another platform, ensuring continuous service availability. This redundancy builds resilience, ensuring high availability in critical systems.

Flexibility and ScalabilityA cloud agnostic approach gives companies the freedom to adjust resources based on current business needs. This means scaling applications horizontally or vertically across different providers without being restricted by the limits or offerings of a single cloud vendor.

Global ReachDifferent cloud providers have varying levels of presence across geographic regions. With a cloud agnostic approach, businesses can leverage the strengths of various providers in different areas, ensuring better latency, performance, and compliance with local regulations.

Challenges of Cloud Agnosticism

Despite the advantages, adopting a cloud agnostic approach comes with its own set of challenges:

Increased ComplexityManaging and orchestrating services across multiple cloud providers is more complex than relying on a single vendor. Businesses need robust management tools, monitoring systems, and teams with expertise in multiple cloud environments to ensure smooth operations.

Higher Initial CostsThe upfront costs of designing a cloud agnostic architecture can be higher than those of a single-provider system. Developing portable applications and investing in technologies like Kubernetes or Terraform requires significant time and resources.

Limited Use of Provider-Specific ServicesCloud providers often offer unique, advanced services—such as machine learning tools, proprietary databases, and analytics platforms—that may not be easily portable to other clouds. Being cloud agnostic could mean missing out on some of these specialized services, which may limit innovation in certain areas.

Tools and Technologies for Cloud Agnostic Strategies

Several tools and technologies make cloud agnosticism more accessible for businesses:

Containerization: Docker and similar containerization tools allow businesses to encapsulate applications in lightweight, portable containers that run consistently across various environments.

Orchestration: Kubernetes is a leading tool for orchestrating containers across multiple cloud platforms. It ensures scalability, load balancing, and failover capabilities, regardless of the underlying cloud infrastructure.

Infrastructure as Code (IaC): Tools like Terraform and Ansible enable businesses to define cloud infrastructure using code. This makes it easier to manage, replicate, and migrate infrastructure across different providers.

APIs and Abstraction Layers: Using APIs and abstraction layers helps standardize interactions between applications and different cloud platforms, enabling smooth interoperability.

When Should You Consider a Cloud Agnostic Approach?

A cloud agnostic approach is not always necessary for every business. Here are a few scenarios where adopting cloud agnosticism makes sense:

Businesses operating in regulated industries that need to maintain compliance across multiple regions.

Companies require high availability and fault tolerance across different cloud platforms for mission-critical applications.

Organizations with global operations that need to optimize performance and cost across multiple cloud regions.

Businesses aim to avoid long-term vendor lock-in and maintain flexibility for future growth and scaling needs.

Conclusion

Adopting a cloud agnostic strategy offers businesses unparalleled flexibility, independence, and resilience in cloud management. While the approach comes with challenges such as increased complexity and higher upfront costs, the long-term benefits of avoiding vendor lock-in, optimizing costs, and enhancing scalability are significant. By leveraging the right tools and technologies, businesses can achieve a truly cloud-agnostic architecture that supports innovation and growth in a competitive landscape.

Embrace the cloud agnostic approach to future-proof your business operations and stay ahead in the ever-evolving digital world.

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