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Software Development
Simplifying Enterprise Cloud Migration: A Winning Strategy for Your Business
5 min read

Enterprise cloud migration brings innovation to IT operations and takes enterprises one step closer to agility. After all, it’s all about streamlining processes, increasing efficiency, and reducing costs. But if not done right, cloud migration can prove to be a nightmare for enterprises. They have to move a large amount of data without which the organization cannot function properly.

If you are stuck with the intricate dilemma of cloud migration, be ready to decode the myths. This article is all about decrypting and implementing enterprise cloud migration. You’ll be learning the best practices to migrate your existing data from your previous infrastructure to the cloud.

What is cloud Migration?

Cloud migration is the practice of transferring your data, digital assets, and other IT resources into the cloud. Enterprises move their databases and applications from physical infrastructure or one cloud to another partially or completely.

Understanding Enterprise Cloud Migration

In enterprise cloud migration, the cloud refers to remote servers and data centers. These are usually hosted and managed by third parties that offer a range of services and resources over the Internet. This allows businesses to access and utilize computing resources on demand as per their needs.

Statistics Highlighting the Growth and Adoption of Enterprise Cloud Migration

The migration to the cloud has gained significant momentum over the past few years. According to Google, 41.4% of global technopreneurs are eager to invest in cloud-based services in 2023. The study also shows that almost half are either training existing staff or hiring new resources to improve cloud efficiency.

Recent research by Mckinsey states that cloud technology has crossed the $1 trillion dollar market value as an industry. What’s more astonishing is the fact that by 2024, out of every $10 spent on IT hosting, $8 will be on the cloud.

it-hosting spend to the cloud

Organizations that have already adopted enterprise cloud migration are reaping its benefits.

As per Salesforce, 69% of companies are spending on this technology. And 94% of businesses saw an improvement in security after migrating to the cloud.

A Google Cloud survey has shown that 54% of businesses are into enterprise cloud migration mainly to reduce costs and increase revenues. 

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Gartner’s research claims that 50% of enterprises will move towards the cloud by 2027 to achieve agility. This supports Forbes’ report which has previously claimed that 83% of organizations are expected to shift their workload to the cloud.

percent of workloads run/will run from the following

These statistics underscore the growing confidence and success organizations experience through cloud migration.

Key Models and Strategies for Enterprise Cloud Migration

When considering cloud migration, enterprises can adopt various models and strategies. Of course, the choice depends on their unique requirements and objectives. These are six different types of enterprise cloud migration models that are in practice today.

6 types of clouds migration

Lift and Shift Model or Rehosting

This cloud migration model is about lifting the database or stack from on-premises hosting and shifting it to the cloud. In doing so, an exact copy of your existing environment is transported without making changes. Rehosting is suitable for enterprises that don’t have a long-term strategy or possess a conservative culture.

Lift and Optimize or Replatforming

This is a variation of rehosting that involves a few adjustments in the cloud landscape for optimization. However, the core architecture in the re-platforming model stays the same. This is also suitable for conservative enterprises who are looking for speed and agility.

Drop and Shop or Repurchasing

Repurchasing the enterprise cloud migration model is about transferring applications to a new, cloud-native product. However, there’s always a challenge of training the team and fear of losing the familiarity of existing code. If you are moving data from a customized landscape, drop and shop model is the ideal pick for you.

Re-Architect or Refactoring

Refactoring refers to rebuilding your applications on the cloud from scratch. It’s an expensive choice but suitable for your future needs. It helps you leverage cloud capabilities that were absent in previous environments. For example, you can go for serverless computing or cloud auto-scaling.


In some cases turning application is the wise and suitable choice. This is useful if you feel some applications are no longer required after assessing their portfolio for cloud readiness. Retiring can help you boost the process and make cloud migration easier.

Hybrid Model or Retaining

This is an ideal model for enterprises for whom cloud migration doesn’t make sense. Some might have recently updated their applications while others are unable to move for compliance reasons. If you are such an enterprise, only make the decision after contacting experts.

Benefits and Challenges Associated with Enterprise Cloud Migration

As with any major IT initiative, enterprise cloud migration comes with its share of benefits and challenges:

Benefits of Enterprise Cloud Computing

Benefits of Enterprise Cloud Computing

Cost Savings: By transitioning from capital-intensive on-premises infrastructure to pay-as-you-go cloud services, organizations can reduce IT costs significantly.

Scalability and Flexibility: Cloud services allow businesses to scale resources up or down as needed. This provides them with the agility to meet changing demands of their business landscape.

Enhanced Security: Leading cloud providers invest in robust security measures. These are security measures that often surpass what organizations can achieve by themselves on-premises.

Innovation Acceleration: Cloud-native technologies and services enable organizations to innovate rapidly and deploy new features quickly.

Challenges of Cloud Migration

Data Migration Complexity: Transferring large volumes of data to the cloud can be time-consuming and challenging. This is especially challenging if you are migrating data with limited bandwidth.

Application Compatibility: Some legacy applications may require modification to work effectively in the cloud environment.

Security and Compliance Concerns: Ensuring data security and regulatory compliance during and after migration is crucial.

Vendor Lock-In: Moving between cloud providers can be complex, potentially leading to vendor lock-in.

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Best Practices for Successful Enterprise Cloud Migration

A successful enterprise cloud migration requires meticulous planning, thoughtful decision-making, and effective execution. Let's explore some best practices to ensure a smooth and successful migration process.

Pre-migration Planning and Assessment

Before embarking on the cloud migration journey, organizations must undertake a comprehensive planning and assessment process:

Pre-migration Planning and Assessment

Importance of Cloud Migration Checklist

Creating a detailed checklist of migration tasks and requirements helps organizations stay organized. This is the practice that ensures no critical aspects are overlooked during the process. The checklist should cover areas such as application dependencies, data backup, security measures, and post-migration testing.

Evaluating Existing Infrastructure and Applications

A thorough assessment of existing infrastructure and applications is essential to determine their suitability for the cloud environment. This assessment helps identify potential roadblocks and provides insights into which applications can be migrated. Furthermore, you can know which applications require adjustments for migration.

Considerations for Data Migration and Security

For data migration, organizations have to establish robust strategies for data integrity, privacy, and compliance. Implementing proper encryption and access controls is vital to safeguard sensitive data during transit and storage.

Selecting the Right Cloud Provider and Model

Choosing the most suitable cloud service provider and deployment model is pivotal for the success of cloud migration:

Evaluating Different Cloud Service Providers

The choice of cloud provider impacts various aspects of migration, including performance, service offerings, pricing, and regional availability. Organizations should conduct a thorough evaluation of major cloud providers like AWS, Azure, Google Cloud, and others, considering their specific needs.

According to Statista, Cloud services generated $63 billion dollars in revenue in the first quarter of 2023. Amazon AWS, Microsoft Azure, and Google Cloud have 32%, 23%, and 10% shares respectively in this.

Evaluating Different Cloud Service Providers

It’s hard to calculate the exact cost of service providers as it depends on what you are getting. But the big three (AWS, Azure, and Google Cloud) as well as IBM offer various discounts to its users. By leveraging these discounts, you can greatly reduce the overall cost of your cloud hosting.

cloud service providers

Choosing Between Public, Private, or Hybrid Cloud Models

Your decision to opt for public, private, or hybrid cloud models depends on various factors. These include but aren’t limited to data sensitivity, regulatory requirements, and budget constraints. A hybrid cloud approach, which combines public and private clouds, offers flexibility in managing sensitive data. It also helps in leveraging the scalability of public cloud services.

Aligning Cloud Strategy with Business Goals

The cloud migration strategy should align with the organization's broader business objectives. Whether it's achieving cost savings, improving performance, or enhancing customer experience, the cloud migration approach should support these goals.

Implementing the Migration Process

With a robust plan in place, it's time to execute the migration process:

Implementing the Migration Process

Creating a Phased Approach

Adopting a phased approach is often more manageable and minimizes the risk of disruption. Migrating applications and data in batches allows the IT team to address any challenges that arise in a controlled manner.

Prioritizing Critical Applications and Data

Not all applications and data have the same level of urgency for migration. Prioritizing critical business applications and data ensures that essential functions continue smoothly during the migration process.

Ensuring Minimal Disruption to Operations

You should try to minimize any disruption to ongoing business operations during the migration process. So, ensure proper communication and collaboration among stakeholders, IT teams, and end-users. This plays a crucial role in ensuring a seamless transition.

Post-migration Optimization and Evaluation

Once migration is complete, the focus shifts to optimizing and evaluating the newly established cloud infrastructure:

Post-migration Optimization and Evaluation

Monitoring and Optimizing Performance

Continuous monitoring of cloud infrastructure performance is essential to identify and address any bottlenecks or inefficiencies. Cloud-native monitoring tools can provide real-time insights into resource utilization and application performance.

Assessing the Success through Key Performance Indicators (KPIs)

Defining key performance indicators (KPIs) for cloud migration success is vital to measure the project's outcomes. KPIs may include:

  • Cost savings
  • Uptime
  • Customer satisfaction
  • Application response times

Addressing Any Challenges or Failures 

Migrating to the cloud is a complex process, and challenges or failures may arise. A post-mortem analysis of any issues encountered can provide valuable insights for future improvements and projects. Some common challenges that data scientists often have to face include:

  • Incomplete inventory (artifacts and respective ETLs)
  • Limited documentation of current systems
  • Data Redundancy
  • Complex workflow, job flow, and data flow.
  • Multiple processing (Scripts, View, SP, ETL, etc.)
data migration challenges and solution for successful implementation

How shopdev Can Help to Migrate

Shopdev has been helping enterprises with cloud migration for over half a decade now. We provide tailored cloud migration solutions to businesses with a team of seasoned cloud experts. With our proven track record, we bring a wealth of experience to ensure the success of every cloud migration project.

Tailored Solutions for Enterprise Cloud Migration

Shopdev offers a range of cloud migration services to enterprises looking to migrate to the cloud. We are a proud partner of all the big companies such as Amazon AWS, Azure, Google Cloud, and more.

Tailored Solutions for Enterprise Cloud Migration

We conduct an in-depth assessments of your organization's existing infrastructure and applications to gauge their readiness for migration. Based on the assessment, we craft a detailed migration plan. This helps us address potential challenges and in optimizing the migration process.

The Shopdev team works closely with you to identify the most suitable cloud provider and deployment model. We consider your organization's unique requirements while offering suggestions and recommendations. This collaborative approach ensures alignment with the client's goals and budget.

In the end, our experts execute the migration process in a phased manner, ensuring minimal disruption to business operations. In the post-migration stage, we provide ongoing support to address issues and optimize the cloud infrastructure for optimal performance.

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Enterprise cloud migration is a powerful strategy that empowers organizations with scalability, cost-efficiency, and enhanced security. By following best practices and aligning cloud strategy with business goals, enterprises can unlock the full potential of the cloud. However, enterprises need competent partners like Shopdev to navigate the complexities of this transformational journey successfully.

Artificial Intelligence
7 Mistakes to Avoid When Selecting an AI Development Company in 2024
5 min read

In the fast-evolving landscape of artificial intelligence, selecting the right AI development company is more crucial than ever. As AI technologies become integral to business operations across various industries, the stakes for choosing a competent and reliable AI partner have soared. The decision you make can either propel your business forward by leveraging cutting-edge AI solutions or set you back due to mismatches in expertise and expectations.

With AI advancements rapidly progressing each year, businesses must approach the selection process with a well-informed strategy. However, common pitfalls often trip up even the most astute business leaders. From failing to conduct adequate research to overlooking crucial aspects like scalability and data security, these mistakes can lead to disappointing outcomes. In this article, we will guide you through seven critical mistakes to avoid when selecting an AI development company in 2024, ensuring that your business makes a choice that aligns with its long-term technological and operational goals.

1. Insufficient Research

The foundation of a successful partnership with an AI development company is thorough research. Yet, many businesses rush into decisions, swayed by superficial evaluations or compelling sales pitches. This oversight can lead to a poor match between your project's requirements and the chosen company's capabilities, potentially resulting in project failure.

To avoid this, it's essential to dive deep into the backgrounds of potential AI partners. Start by examining their past projects, client testimonials, and independent reviews. Understanding their track record in detail will help you gauge their success rate and reliability. Websites like Clutch or Glassdoor provide comprehensive reviews that reflect real customer experiences and can be invaluable in this phase.

Also, consider reaching out to past clients directly. This can give you unfiltered insights into the AI company’s ability to deliver on promises and handle challenges. Learning from the experiences of others can steer you away from companies with a history of projects that do not meet client expectations.

Finally, do not overlook the importance of case studies. These often contain detailed descriptions of how the company approached different challenges and the outcomes of their projects. A company that can demonstrate a history of innovative solutions and successful implementations across various scenarios is more likely to adapt and thrive in your specific context.

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2. Unclear Objectives

A fundamental error many companies make when embarking on an AI project is not having clear, defined objectives. This lack of clarity can cause misalignment between your company's needs and the AI development approach, leading to outcomes that don't meet your expectations.

With 77% of companies either using or exploring AI, it's evident that businesses are increasingly dependent on AI to enhance various aspects of their operations, such as operational efficiency, customer experience, and product innovation​ (National University)​. This trend underscores the necessity of having well-defined objectives to effectively leverage AI for these purposes.

Before you start looking for an AI partner, it's crucial to outline what you want to achieve with AI. Are you looking to improve operational efficiency, enhance customer experience, or perhaps innovate a new product? Clearly articulating your goals will guide you in selecting an AI company that specializes in the specific areas you need.

For instance, if your goal is to integrate AI into customer service operations, you would look for a company with proven expertise in natural language processing and chatbot development. This specificity will narrow down your options and increase the chances of success.

It's also important to communicate these objectives clearly when engaging with potential partners. Clear communication ensures that the AI development fully understands your expectations and can provide a tailored solution that meets your specific requirements.

To facilitate this, consider creating a detailed project brief that includes not only your goals but also any constraints, such as budget and timeline. Share this document with potential AI companies during your initial discussions to ensure everyone is on the same page from the start.

Defining and communicating your objectives is not just about avoiding missteps; it's about paving a path to a successful AI implementation that is aligned with your strategic business goals.

3. Neglecting Industry Expertise

Choosing an AI development software solution company without considering its expertise in your specific industry is a critical oversight. Each industry presents unique challenges and regulatory requirements that require specialized knowledge. A company proficient in AI for finance may not be as adept in AI applications for healthcare, for example.

When selecting your AI development partner, it’s crucial to verify their experience and success in projects within your industry. This can involve reviewing case studies that highlight their work in similar sectors or requesting references from past clients in your industry.

Furthermore, consider the specific challenges your industry faces. For instance, if you are in healthcare, data privacy is a paramount concern, governed by strict regulations like HIPAA in the U.S. An AI company with a deep understanding of these regulations will be better equipped to ensure compliance and secure handling of sensitive data.

Inquiring about the technology stack the company uses is also essential, as some technologies might be more suited to particular industries than others. For example, a company using advanced neural networks would be preferable for image recognition tasks commonly required in the security or automotive industries.

To effectively assess a company’s industry expertise, prepare questions related to their past projects, the challenges they faced, and the solutions they implemented. This will help you gauge whether they have the practical experience and in-depth knowledge necessary to handle the specific needs of your industry.

4. Overlooking Data Security

Ignoring data security when selecting an AI development solution can expose your business to significant risks. Data breaches can lead to financial loss, legal repercussions, and severe damage to your company's reputation. It's crucial to ensure that the AI company you choose has robust security measures in place.

Start by inquiring about the data security practices the AI company follows. This includes understanding their data encryption methods, how they manage data access, and their compliance with relevant data protection regulations, such as GDPR or HIPAA. A reputable AI company should be transparent about their security protocols and willing to discuss how they will protect your data.

Additionally, consider the security of the technology stack they use. The programming languages, frameworks, and infrastructure should all meet the highest security standards to prevent vulnerabilities. For example, ask if they employ secure coding practices and regular security audits to detect and mitigate potential security issues.

It’s also wise to discuss how the AI company handles data breaches, should they occur. Understanding their incident response plan can give you confidence that they are prepared to act swiftly and effectively to minimize any damage.

To assess their commitment to data security, request to see certifications or attestations such as ISO 27001 or SOC 2. These certifications demonstrate a level of dedication to maintaining high-security standards.

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5. Ignoring Scalability

Scalability is often underestimated during the selection of an AI development solution. As your business grows, so will your demands for AI capabilities. Selecting a partner that can scale solutions to match your evolving needs is essential for long-term success.

To ensure scalability, assess the potential AI company’s infrastructure and technology stack. Ask about their experience handling increasing volumes of data and user engagement. A company that has successfully scaled projects similar to yours is likely to replicate that success with your project.

Moreover, discuss future growth prospects during initial meetings. Understand how the company plans to accommodate your growth, such as adding more resources or utilizing cloud technologies that offer flexibility. This discussion will give you insight into their capacity for adaptation and innovation.

It's also crucial to consider the scalability of the AI models themselves. Inquire about the AI company’s approach to updating and refining AI models as more data becomes available and as business needs change. The ability to continually improve and adapt AI models is key to maintaining the relevance and effectiveness of AI solutions.

Lastly, review case studies or testimonials that demonstrate the company’s track record with scalable AI projects. These will provide concrete examples of their ability to expand and adapt their services in response to client growth.

6. Inadequate Post-Development Support

Neglecting the importance of post-development support is a common oversight that can undermine the long-term success of AI projects. Once an AI solution is deployed, it requires continuous maintenance, updates, and support to ensure its effectiveness and relevance over time.

When selecting an AI development company, evaluate their commitment to post-launch support. This includes regular updates to the software to incorporate the latest AI advancements and patches for any security vulnerabilities that may arise. A company that offers comprehensive support will help ensure your AI system remains robust against evolving threats and technologies.

It's also important to discuss the specifics of the support agreement. Determine what types of support are available—such as 24/7 customer service, on-demand technical assistance, and routine check-ups. These services are crucial for quickly resolving any issues that might arise, thereby minimizing downtime and maintaining operational continuity.

Furthermore, ask about the company's procedures for handling feedback and implementing changes. An AI development that actively seeks and responds to client feedback demonstrates a dedication to service quality and continuous improvement.

Finally, consider the scalability of support services. As your business grows, your support needs may increase. The right AI development partner will offer flexible support options that can be scaled to match your evolving requirements.

7. Poor Communication and Collaboration

Effective communication and collaboration are pivotal for the success of any AI project, yet they are often overlooked when selecting an AI development. Poor communication can lead to misunderstandings, misaligned objectives, and project failures.

When evaluating potential AI partners, prioritize those who demonstrate strong communication skills right from the initial interactions. Assess their responsiveness to your inquiries and the clarity of their communication. This can be an indicator of how they will engage throughout the project lifecycle.

During your discussions, observe whether the AI company seeks to understand your business context and objectives thoroughly. A partner who asks insightful questions and proposes ideas that align with your vision is likely to be a collaborative and communicative team player.

It's also important to consider the company's tools and processes for project management and communication. Do they use established platforms like Slack, Microsoft Teams, or Asana? Are their processes structured to ensure regular updates and feedback loops? These tools and processes are vital for maintaining clear and continuous communication.

Moreover, inquire about the involvement of project managers or points of contact who will manage the relationship. Having a dedicated person or team to handle your project ensures that there is always someone available to address your concerns and coordinate between different teams.

Finally, discuss the company's approach to conflict resolution and problem-solving. Understanding how they handle challenges and setbacks can provide insight into their ability to maintain a productive working relationship even under pressure.

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Selecting the right AI development company is a critical decision that can significantly influence your project's success. Rushing the selection process without adequate research, or partnering with a firm that lacks the necessary industry expertise, can derail your goals. Clearly defining your objectives, prioritizing data security, and ensuring scalability are essential steps.

Equally important is securing comprehensive post-development support. Regular updates, technical assistance, and adapting to your growing business needs will ensure your AI solution remains relevant. Effective communication and collaboration, facilitated through strong project management and regular feedback loops, are vital for seamless project execution.

Avoiding these seven mistakes will not only help your business choose an AI development partner that aligns with your goals, but will also set the foundation for a long-term, successful partnership. By being diligent in your selection process, you can safeguard your investment and fully harness the transformative power of AI.

Artificial Intelligence
7 Steps to Build a Generative AI Solution from Scratch in 2024
5 min read

Generative AI is a revolutionary advancement in the tech world that has taken businesses and decision-makers by storm. With everyone using AI models like ChatGPT and Stable Diffusion to speed up development processes, it's safe to say that generative AI is here to stay. 

Jasper, one of the successful generative AI tools, has raised $125 million at a valuation of $1.5 billion by assisting businesses with copywriting. Similarly, Stability AI has made $101 million with a valuation of $1 billion. But the most significant achievement is that of OpenAI, which generated over $1 billion from Microsoft at a valuation of $25 billion in 2019. 

Clearly, the scope for generative AI solutions is broadening. Read on to learn everything you need to know about how to build a generative AI solution from scratch. 

What is Generative AI?

Back in the 1950s, researchers first explored the basic principles of artificial intelligence and generative AI. The initial focus was on simple neural networks and rule-based models to mimic human decision-making. 

On the surface, generative AI is an exciting new development where deep learning algorithms are used to create content, graphics, music, and more. Large datasets are used as input for deep learning algorithms that can generate unique content. What makes generative AI special is its ability to generate entirely new and unique outputs, unlike traditional AI which only delivers pre-programmed outputs.

When trained, the AI algorithms learn patterns from given inputs, store them, and utilize them to create unique content for similar inputs. Even though it looks easy, generative AI becomes quite complicated when you dig deeper. For instance, there are various components like transformers, generative adversarial networks (GANs), and variational auto-encoders behind the functionality of generative AI. 

Examples of transformers include ChatGPT, LaMDA, Wu-Dao, and GPT-3. Transformers are trained to process natural language and images, as well as learn classification tasks and generate texts. These techniques are designed to mimic cognitive attention and understand the difference between varying inputs.

What Can Generative AI Do?

Generative AI is helping businesses worldwide by generating content in the form of text, images, and videos. This means voice-over and background music in videos are not a problem anymore. Generative AI is also used in data augmentation to boost the accuracy of predictive machine-learning models. As a result, retailers can offer highly personalized experiences to customers. 

The following are some of the diverse application areas of generative AI across various industries:

Art and Creativity

The new, state-of-the-art algorithms of generative AI enable you to convert any ordinary picture into an art piece. You can guide the AI tool to add your unique style, features, and other favorite items. Even when you provide the generative AI tool with a rough sketch of the image you want to create, it generates photorealistic masterpieces that go beyond your expectations. Not only that, but the AI can also imitate specific styles of a given human artist, which allows you to create art like never before.

Content Generation

When it comes to automating content creation, you don’t need to look further than generative AI. It has provided marketers with the ideal solution to save time and resources. With automated content generation, you can achieve faster time-to-market and create a variety of campaigns. For instance, you can create social media posts and blog articles, and even design email campaigns. 

AI tools like ChatGPT, Jasper, Copy.ai, and Writesonic have accelerated the process of content generation. In some cases, marketers can personalize and refine drafts generated by AI models. Not only that but generative AI can also be used to update existing content with valuable inputs and improvements.

Natural Language Processing

Generative AI is commonly used in natural language processing to create social media posts and news articles. Various natural language processing techniques are used to generate the text for such posts. These techniques includes transforming raw characters like words, letters, and punctuation into sentences. The resulting entities and actions are represented in the form of vectors through multiple encoding mechanisms.

Image Synthesis

Today’s businesses want to represent their products and services with realistic graphics and lifelike images. Here, AI algorithms assist businesses with accurate image synthesis. You can eliminate misleading aspects from your images and fill in the missing elements to portray your products in the best way. 

Medical Imaging

That’s not all - AI tools can also convert low-resolution images into high-resolution images, making them look like they were captured by professional photographers. With generative AI by your side, your images are transformed into clear and detailed masterpieces. This is particularly useful in medical imaging analysis, where accuracy is of utmost importance.

Generative AI is used in medical imaging for specific tasks like image segmentation, anomaly detection, and prediction of patient outcomes. 

Anomaly Detection

Business owners need to go through large amounts of data to understand patterns in order to make informed decisions. In this case, generative AI helps detect anomalies in a series of data. Assuming a certain range of data based on historical trends, AI can point out values that fall outside that threshold and happen less frequently.

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How Generative AI is Changing Industries

The world knows generative AI as a buzzword because of its impact on different industries. It has quickly transformed business operations and is creating new opportunities across the board. Apart from creating novel, unique content and making way for innovations, generative AI is changing the way processes are carried out.

Here are some examples of how generative AI is transforming various sectors:

Entertainment and Media

Two of the many benefits of generative AI are democratized content creation and simplified management. In the entertainment industry, AI helps accelerate research tasks and allows filmmakers to improve the efficiency of their post-production operations. The data processing power of AI makes video libraries more searchable, making life easier for broadcasters. For this reason, the market size of generative AI is expected to grow to $12,077 Million in 2032 at a CAGR of 26.7%


Whether it is medical diagnosis, imaging, or analysis of a large number of patient records, AI is always good news for the healthcare industry. You can now leverage AI-based disease prediction and diagnosis to improve healthcare operations. Generative AI monitors various factors like lifestyle risk factors, medical imaging, patient health data, environmental factors, and genetic data to provide accurate results. Moreover, statistics show that 27.5 percent of health systems currently use conversational AI, with a further 72.5 percent considering it for future use.

Marketing and Advertising

Until a few years ago, marketing and advertising agencies required graphic designers and content creators to create their campaigns. But now, AI is being used by 53% of IT companies for marketing and advertising. With generative AI, this process is a matter of a few prompts. You can prompt AI tools to create unique and original graphics for blog posts, social media posts, and more. 


Game development is a complex and time-consuming process that involves various tasks. Like other industries, generative AI becomes useful in game development by automating complex tasks. These include creating assets, levels, and generating dialogues. By utilizing generative AI to handle these tasks, game developers are able to unlock creativity and use the extra time to work on the strategic aspects of their development cycle. Generative AI in the gaming sector is expected to achieve the highest market value with a valuation of $7,105 Million by 2032.

Manufacturing and Design

Now here’s an industry that focuses on optimized production and quality controls along with great customer service. By processing raw data and helping in the production of valuable products, generative AI significantly improves manufacture and design operations. According to Gartner, by 2025, generative AI will account for 10% of all data produced, up from less than 1% today. It helps enhance customer service, streamline production processes, and also improves quality control procedures. 

The Right Tech Stack to Build a Generative AI Solution

Before getting into the process of building your own generative AI solution, you need to know the right tools and technologies to use. The following is a comprehensive list of the tech stack you will need to use:

Deep learning framework TensorFlow, PyTorch
Generative Model Architectures GANs, VAEs
GPU Acceleration CUDA, cuDNN
Data Processing Pandas, NumPy
Image Processing OpenCV, PIL
Cloud Resources Amazon Web Services (AWS), Microsoft Azure, Google Cloud
Model Deployment Docker, Kubernetes, TensorFlow Serving, Flask, FastAPI
Version Control GitHub or GitLab
Experiment Tracking TensorBoard, MLflow

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7 Steps to Build a Generative AI Solution from Scratch

In order to build a generative AI solution, you need to develop a comprehensive understanding of its purpose and the problem it will solve. The process involves creating and training AI models to generate unique outputs depending on varying inputs. 

7 Steps to Build a Generative AI Solution from Scratch

As today’s generative AI solutions are capable of optimizing and improving operations, building a satisfactory solution requires you to follow a series of steps.

Problem Identification      

Since you are dealing with a lot of computing power in generative AI, you need to be sure that they are designed to solve the right problem. The first step in building your generative AI solution from scratch is to identify a set of problems to solve. For instance, marketing agencies need various types of content for their blog and social media platforms. In this case, your AI solution needs to be built around natural language processing, neural networks, and generative pre-trained transformers. In other cases such as video and audio generation, you will need to adopt a different approach.

Data Collection  

Data collection is one of the important steps in the prototyping phase of generative AI development.  Since the training of your generative AI solution depends on data, you need to consider certain technical details. These include:

  • Identifying the right data sources
  • Ensuring that high quality, relevant, and diverse data is collected
  • Labeling the data if needed using crowdsourcing, semi-supervised learning, or active learning
  • Preprocessing data through data augmentation, tokenization, or normalization before it is fed to the AI model
  • Splitting the data into separate categories for training, validation, and testing
  • Storing the data in data warehouses, distributed file systems, or cloud storage depending on the scenario.

Model Selection          

Based on the defined problem that the AI solution is intended to solve, you need to select the right model and tech stack to be used in the development. To be able to choose the right model, you need to categorize the problem and determine the right algorithms to solve it. 

For instance, if you’re working with input categorization, you need to select the supervised learning model. For imaging systems that can differentiate objects from images, you can work with OpenCV. On the other hand, you can also use Jupyter Notebook to allow seamless collaborations between machine learning engineers, developers, and data scientists.

Architecture Design       

In order to ensure that your generative AI solution can process large amounts of data and handle high traffic, you need to base it on a robust and scalable architecture. The common purpose of all generative AI solutions is to maintain high performance, reliability, and availability in all situations. The best practices to build the right architecture are as follows:

  • Break down the AI solution into small, manageable components through microservices architecture
  • Adopt a modular design where each module performs its specific function
  • Incorporate message queues to efficiently manage communication between different components of the AI solution
  • Implement load balancing to ensure equal traffic distribution across multiple servers and get peak performance
  • Implement caching to reduce backend requests by storing frequently accessed data
  • Configure hyperparameters of the generative AI model to set number of layers, learning rate, batch size, regularization techniques, dropout rate, etc.

Training and Validation        

In the training phase, you feed the AI model with datasets you previously collected and categorized. With these datasets, the generative AI model learns patterns that are later used to solve problems and create content. In order to train generative AI models, you need to use self-supervised learning and semi-supervised machine learning methods. Although the process is machine dominant, you need to add the human touch to fine-tune and set the AI model’s accuracy and align it with your business objectives.

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Deployment and Integration            

While deploying your AI solution, you need to be sure of its reliability, scalability, and maintainability. This is particularly important to eliminate errors and failures and ensure the smooth functioning of your generative AI solution. Here’s what you need to consider in the deployment and integration phase:

  • Choose the right deployment environment by setting up on-premise infrastructure, cloud computing systems, or hybrid solutions depending on the requirements.
  • Implement the right hardware and software for installing operating systems, setting up databases, and configuring servers.
  • Set up dependencies like libraries, frameworks, and packages required to run your AI solution.
  • Use tools like Jenkins, Travis CI, or GitLab to set up continuous integration and deployment (CI/CD) pipelines.

Monitoring and Maintenance

The last step of the process holds equal significance. Upon deployment, you need to constantly provide support and maintenance while evaluating how the AI solution performs in real-life use cases. The key is to identify bottlenecks and failures in order to fix and improve the solution. Upon further testing, you can refine your AI solution by studying user feedback and performance benchmarks.

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Pro Tips for Crafting Effective Generative AI Solutions

Even when you follow the best practices to build your AI solution, there are always going to be some unexpected issues and challenges along the way. Based on research in the field of generative AI, here are a few pro tips that can be helpful.

  • Apart from identifying a problem to solve, you should also identify specific business processes that can be enhanced by using generative AI. Focus on small changes with meaningful outcomes.
  • Ensure data protection to provide users the confidence to use your generative AI solution and stay compliant with international regulations. Use data encryption to reduce risks of data leaks.
  • Instead of spending big bucks on training AI software like ChatGPT, you should build smaller, more targeted AI models. These can be trained to solve business problems unlike ChatGPT.
  • Since the quality of data matters, your focus should be on preparing the data for training the AI model. Try to eliminate under-generalization and bias.
  • Regardless of how well you train generative AI, it can never be perfect. Therefore, you need to inform your users about the limitations of your AI solution.

Costs of Generative AI Solution Development

Depending on the type, complexity, and size of the application you are trying to build, generative AI development becomes quite a costly process. This means you can expect a bill between $10,000 and $500,000. The most notable costs include training the AI model and deploying it. The software cost itself is between $35,000 to $120,000, while maintenance costs can go beyond $320,000 per year. 

That’s not all, you also need to invest in specialized hardware and cloud storage to get the right computing power and storage capacity. Depending on the complexity of your solution, this can cost you between $100 and $15,000 a month.

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Can I make my own generative AI?

You can create your own generative AI by adopting, modifying, and building the models you require. However, the process is quite complex and you need an increasing number of resources as you continue building your generative AI. You also need to have technical expertise in the fields of natural language processing, machine learning, and computer vision.

How do I start generative AI?

First of all, you need to understand the different generative AI models available in the market. You should consider the unique features and applications of GANs, autoregressive models, and VAEs. You need to gather and prepare the data you want to work with and choose the type of AI architecture to use. Then you need to train your AI model and evaluate, optimize, and test it in a real-world environment.

Does generative AI use NLP?

Yes, generative AI leverages natural language processing to create new outputs or content for each input. The use of NLP means that you can also generate all types of content including text, images, audio, and video.

Is generative AI free to use?

There are free versions of generative AI tools like ChatGPT 3.0 and DALL-E 2. These are great for beginners who want to try generating content using AI. AI tools by OpenAI allow you to generate content by simply creating an account on their website and entering your prompts in the search bar.

Key Takeaways

In the era of artificial intelligence, businesses are being driven by generative tools that enhance operational efficiency and also help target the right customers. With businesses adopting generative AI solutions, there really is no limit to what can be achieved in the near future. We can expect unprecedented business growth with the help of efficient processes, ease of use, and the automation of complex operations. Based on the steps discussed in this blog, you can build your generative AI solution and embrace its transformative power in your business use cases.

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