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You can use the Govern methodology to help maintain consistent control of your environment and address tangible risks. The Govern methodology provides a structured approach that you can use to establish and optimize governance in Azure. The Govern methodology consists of five steps.

Build a team

Select a small, diverse team to encourage quick decision-making and include various perspectives. Define the functions, authority, and scope of your team. Ensure that your organization supports your cloud governance team so that you can enforce important security policies.

Assess cloud risks

Before you create new policies or update existing policies, you need to assess cloud risks to help define the new or updated policy. To effectively assess risks in the cloud:

• Identify risks and catalog them. Use Azure tools to list cloud assets and discover cloud risks.
• Analyze risks and assign a qualitative or quantitative value to each risk. Prioritize the risks by severity.
• Determine the impact of a risk, for example downtime or cost.
• Document risks, and inform all necessary parties in your organization about the risks.
• Review risks regularly and in response to events to ensure that they remain valid and accurate.

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Cloud-native solutions create new business value by building applications or adding features that use cloud capabilities for scalability, resilience, and agility. This guidance provides a structured approach to plan cloud-native development projects that align with business goals and minimize delivery risks.

Planning cloud-native solutions on Azure

1. Define business objectives for cloud-native solutions. Start with clear, measurable business goals, identify constraints and success criteria, and validate stakeholder alignment to ensure everyone shares the same expectations from project inception.
2. Define requirements for cloud-native solutions. Document functional requirements that tie to business objectives, establish nonfunctional requirements including reliability metrics and security baselines, and control scope by clearly defining what is in-scope versus out-of-scope for the initial release.
3. Plan the cloud-native architectures. Explore validated reference architectures from Azure Architecture Center, select appropriate architecture styles based on workload characteristics, apply design best practices and cloud patterns, integrate the Well-Architected Framework’s five pillars into design decisions, plan integrations with existing systems, select appropriate Azure services and service tiers based on requirements, determine regional deployment strategy based on reliability targets, and document architectures with detailed diagrams and design decisions.
4. Plan the cloud-native deployment strategy. Establish DevOps practices for deployment automation, plan operational readiness with monitoring and incident response procedures, define development practices that support reliable deployments, use progressive exposure for new workloads starting with pilot groups, plan feature integration using change management processes with appropriate deployment patterns (in-place updates for minor changes, blue-green for major changes), and define ownership and support responsibilities for post-deployment operations.
5. Define rollback plan for cloud-native solutions. Create comprehensive rollback procedures to quickly recover from deployment issues and ensure business continuity during release activities.

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Cloud modernization is the practice of improving existing cloud-based workloads to better meet business needs. It aligns workloads with cloud best practices, without adding new features.

Prepare organization for cloud modernization

1. Define modernization for your organization. Establish a common definition that focuses on improving existing workloads through replatforming, refactoring, and rearchitecting within the cloud, excluding net-new features or complete rewrites. Communicate this definition across all teams and stakeholders to prevent misalignment. Create shared responsibility between development, operations, security, and architecture teams with clear roles and cross-team coordination.
2. Assess modernization readiness and skills. Evaluate your team’s capabilities in cloud services knowledge, DevOps/CI/CD maturity, modern architecture patterns, and monitoring/automation tools. Identify skills gaps and create plans to address them through training, certifications, new hires, or external expertise from Microsoft or partners to ensure smooth modernization execution.
3. Prioritize what workloads to modernize. Assess business value by rating workloads based on their importance to revenue, customer experience, compliance, and internal dependencies. Evaluate technical risk by examining technical debt, outdated technology, maintenance effort, performance issues, and scalability limitations. Combine these assessments into a priority matrix to determine which workloads to modernize first.
4. Understand how to modernize. Use the Azure Well-Architected Framework to conduct reviews that identify gaps and generate modernization roadmaps across five pillars: Reliability, Security, Cost Optimization, Operational Excellence, and Performance Efficiency. Enable workload teams to make modernization decisions by providing business context and decision-making authority within defined boundaries while maintaining regular check-ins for organizational alignment.

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Migrations involve planning, executing, and optimizing workload migrations from on-premises data centers and other cloud platforms to Azure. The recommendations help organizations minimize migration risks, reduce costs, and achieve successful cloud adoption outcomes.

Plan migration

1. Assess migration readiness and skills. Evaluate your team’s Azure capabilities across infrastructure, security, and application domains, then engage Microsoft partners or Azure solution architects to fill expertise gaps.
2. Choose your data migration path. Select ExpressRoute for high-bandwidth transfers, VPN gateways for encrypted connections, Azure Data Box for offline migrations, or public internet for non-sensitive data.
3. Determine the migration sequence. Map application dependencies using Azure Migrate, prioritize workloads by business criticality, and create migration schedules that avoid peak business periods.
4. Choose the migration method for each workload. Select near-zero downtime migration for mission-critical workloads or planned downtime migration for applications that accommodate maintenance windows.
5. Define rollback plan. Develop backup strategies with automated recovery scripts, establish rollback timeframes, and test recovery procedures in non-production environments.
6. Engage stakeholders on migration plan. Document migration approaches with business justification, present tested rollback procedures, validate schedules against business constraints, and establish clear success criteria.

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You can use the Ready methodology of the Cloud Adoption Framework to help guide the following aspects of preparation:

• Set up your Azure environment.
• Define a cloud operating model.
• Implement landing zones.
• Consider operational aspects.
• Develop necessary skills.

Implement these elements to create a strong foundation for cloud adoption. The Azure setup guide offers step-by-step instructions to help you organize resources, control costs, and secure your environment before you deploy solutions.

Define a cloud operating model

A cloud operating model defines how you want to operate technology in the cloud. The key components of an operating model include alignment to business strategy, organization of people, change management (or adoption processes), operations management, governance and compliance, and security.

A cloud operating model shifts the focus from hardware to digital assets and workloads. The purpose of an operating model is to ensure consistent operations. To determine which operating model to use, compare common operating models.

Implement landing zones

Landing zones provide a scalable and modular environment to help you manage your cloud environment. They provide a foundation for security, governance, and resource management. The landing zone implementation that you choose depends on your organizational needs, required customizations, and how you use various technologies. To deploy a landing zone, you can use tools like the Azure portal, Bicep, or Terraform.

After you implement your landing zones, you must optimize your landing zone operations as you scale. Continuous optimizations can help you:

• Identify and eliminate unnecessary expenses.
• Enhance the performance of applications and services.
• Identify and mitigate security vulnerabilities.
• Ensure that the landing zone can scale efficiently to meet new demands.
• Maintain compliance with industry standards and regulations.
• Create reliable and resilient systems.

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Successful cloud adoption requires more than technical readiness. It requires a cloud adoption plan that converts your cloud strategy into actionable steps specific to your goals.

Prepare your organization for the cloud

Organizations must align their structure and processes with cloud adoption goals to effectively deploy, manage, and optimize cloud resources while meeting business objectives.

1. Map your cloud adoption journey based on your organization type. Startups should build cloud-native solutions using CAF Plan, Ready, and Cloud-native development phases. Enterprises should evaluate their IT estate and follow the complete CAF Plan process plus Ready, Migrate, and Modernize phases.
2. Choose the management model that fits your organizational structure. Small organizations should select centralized operations for consistent policy enforcement. Mid-size enterprises should implement shared management where platform teams manage landing zones while workload teams operate autonomously. Organizations with skilled teams should adopt decentralized operations for full ownership.
3. Plan cloud responsibilities across governance, security, and management functions. Establish governance teams to assess risks and define policies. Embed security into every stage of the cloud lifecycle. Define operational processes that align with business goals. Develop your AI strategy and build appropriate teams for AI adoption.
4. Document cloud responsibilities with clear ownership assignments. Map responsibilities across governance, security, and operations with specific ownership. Define partner roles and communicate responsibilities to all stakeholders. Review responsibilities regularly as your environment evolves.

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Determine your motivations, mission, and objectives

To ensure a successful cloud adoption journey, identify and understand your motivations to move to the cloud. Key motivations include cost savings, agility, scalability, and innovation. Align your cloud adoption strategies with these motivations so that you can achieve your business goals more effectively. For example, if critical business events are a top priority, you might start migration early, alongside strategy and planning efforts.

Clearly define a mission and objectives to provide direction and purpose during your cloud adoption. A mission is only valuable if you can take action on it, achieve objectives effectively, and measure the results. Create a mission statement that helps you align your objectives and key results to your organization’s overall business mission.

Define clear steps that you can take to achieve your mission. These steps become your objectives. Define specific key performance indicators (KPIs) that indicate the success of your objectives.

Assign accountability for each key result, and review key results and their associated KPIs regularly. A well-defined mission and objectives help you stay focused and motivated throughout your cloud adoption journey.

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The Transformer architecture has allowed us to train models for Natural Language Processing (NLP) in a more efficient way. Instead of processing each token in a sentence or sequence, attention allows a model to process tokens in parallel in various ways.

To train a model using the Transformer architecture, you need to use a large amount of text data as input. Different models have been trained, which mostly differ by the data they’ve been trained on, or by how they implement attention within their architectures. Since the models are trained on large datasets, and the models themselves are large in size, they’re often referred to as Large Language Models (LLMs).

Many LLMs are open-source and publicly available through communities like Hugging Face. Azure also offers the most commonly used LLMs as foundation models in the Azure Machine Learning model catalog. Foundation models are pretrained on large texts and can be fine-tuned for specific tasks with a relatively small dataset.

Explore the model catalog

In the Azure Machine Learning studio, you can navigate to the model catalog to explore all available foundation models. Additionally, you can import any model from the Hugging Face open-source library into the model catalog.

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The latest breakthrough in Natural Language Processing (NLP) is owed to the development of the Transformer architecture.

Transformers were introduced in the Attention is all you need paper by Vaswani, et al. from 2017. The Transformer architecture provides an alternative to the Recurrent Neural Networks (RNNS) to do NLP. Whereas RNNs are compute-intensive since they process words sequentially, Transformers don’t process the words sequentially, but instead process each word independently in parallel by using attention.

The position of a word and the order of words in a sentence are important to understand the meaning of a text. To include this information, without having to process text sequentially, Transformers use positional encoding.

Understand positional encoding

Before Transformers, language models used word embeddings to encode text into vectors. In the Transformer architecture, positional encoding is used to encode text into vectors. Positional encoding is the sum of word embedding vectors and positional vectors. By doing so, the encoded text includes information about the meaning and position of a word in a sentence.

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Statistical techniques were relatively good at Natural Language Processing (NLP) tasks like text classification. For tasks like translation, there was still much room for improvement.

A recent technique that has advanced the field of Natural Language Processing (NLP) for tasks like translation is deep learning.

When you want to translate text, you shouldn’t just translate each word to another language. You may remember translation services from years ago that translated sentences too literally, often resulting in interesting results. Instead, you want a language model to understand the meaning (or semantics) of a text, and use that information to create a grammatically correct sentence in the target language.

Understand word embeddings

One of the key concepts introduced by applying deep learning techniques to NLP is word embeddings. Word embeddings solved the problem of not being able to define the semantic relationship between words.

Before word embeddings, a prevailing challenge with NLP was to detect the semantic relationship between words. Word embeddings represent words in a vector space, so that the relationship between words can be easily described and calculated.

Word embeddings are created during self-supervised learning. During the training process, the model analyzes the cooccurrence patterns of words in sentences and learns to represent them as vectors. The vectors represent the words with coordinates in a multidimensional space. The distance between words can then be calculated by determining the distance between the relative vectors, describing the semantic relationship between words.

Imagine you train a model on a large corpus of text data. During the training process, the model finds that the words bike and car are often used in the same patterns of words. Next to finding bike and car in the same text, you can also find each of them to be used when describing similar things. For example, someone may drive a bike or a car, or buy a bike or a car at a shop.

The model learns that the two words are often found in similar contexts and therefore plots the word vectors for bike and car close to each other in the vector space.

Imagine we have a three-dimensional vector space where each dimension corresponds to a semantic feature. In this case, let’s say the dimensions represent factors like vehicle type, mode of transportation, and activity. We can then assign hypothetical vectors to the words based on their semantic relationships:

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