Welcome to a world of endless possibilities, a world where advancements in Artificial Intelligence (AI) have transformed the way we live and work. From self-driving cars to intelligent personal assistants, AI’s impact on various industries is undeniable, revolutionizing how we approach tasks and processes that were once exclusively human domain.

In the realm of web development, there’s an ongoing debate: will AI completely replace web developers? The potential is certainly there. AI-powered tools are becoming increasingly sophisticated, capable of designing websites, optimizing user experience, and even troubleshooting technical issues. But while these developments are impressive, we’re not quite at the point of fully automated web development…yet.

In this blog post, we delve into the dynamics of AI in web development, exploring its capabilities, its current limitations, and what the future may hold. Stay with us as we navigate this fascinating intersection of technology and creativity.

Understanding AI in Web Development

To truly grasp the impact of AI on web development, it’s essential first to understand what AI is and how it operates. Artificial Intelligence, at its core, is a subset of computer science that aims to create systems capable of performing tasks that would normally require human intelligence. These tasks include learning from experience, interpreting complex data, understanding and responding to language, and making decisions.

Two subsets of AI that are particularly relevant to web development are Machine Learning (ML) and Natural Language Processing (NLP). ML involves algorithms that improve automatically through experience, allowing systems to learn from data without being explicitly programmed. This can significantly speed up the development process, as ML algorithms can be trained to automate repetitive coding tasks.

On the other hand, NLP is the technology used to aid computers to understand the human’s natural language. It’s the driving force behind chatbots and voice assistants, which are becoming increasingly common features on websites, offering instant customer service and providing a more interactive user experience.

The applications of AI in web development are vast and continually evolving. AI-powered design tools can now generate website layouts based on user preferences, greatly reducing the time spent on initial design stages. These tools can also analyze user behavior data to optimize website layouts for maximum engagement and conversion rates.

AI can also automate many testing and debugging processes. For instance, AI can scan a website for bugs or security vulnerabilities, report back on any issues found, and even suggest fixes. This not only speeds up the development process but also helps to ensure a higher level of accuracy and consistency compared to manual testing.

Current Capabilities of AI in Web Development

AI is already making a significant impact on the web development industry, with numerous tools and platforms leveraging its capabilities to streamline processes and enhance output quality.

AI Tools and Platforms in Web Development

Several AI-powered tools and platforms are currently available that aid various stages of web development. For instance, platforms like Wix ADI (Artificial Design Intelligence) and Bookmark use AI to create customized website designs based on user preferences. These tools can generate a fully functional site within minutes, significantly reducing the time spent on the initial design stage.

On the coding front, tools like Kite leverage Machine Learning to provide intelligent code completions, helping developers write code faster and with fewer errors. Similarly, DeepCode uses AI to review code and find potential issues, acting as an AI-powered ‘pair programmer.’

Tasks AI Can Perform in Web Development

AI has been trained to perform an array of tasks within the realm of web development. One of the most significant is code generation. AI algorithms can be trained to automate repetitive coding tasks, thereby increasing efficiency and reducing the likelihood of human error.

Design optimization is another area where AI shines. AI-powered design tools can analyze user behavior data to optimize website layouts for maximum engagement and conversion rates. This allows for a more personalized user experience, which can significantly improve website performance metrics.

AI also plays a crucial role in testing and debugging, as we mentioned before. 

Challenges and Limitations

Despite the impressive capabilities of AI in web development, there are certain areas where it falls short, primarily due to the inherent limitations of current technology. Here are some key challenges and limitations that prevent AI from fully replacing web developers.

Lack of Creative Intuition and Human-Centric Understanding

One of the most significant limitations of AI is its lack of creative intuition. While AI can analyze data and make decisions based on predefined algorithms, it lacks the ability to think outside the box and come up with innovative solutions. This is especially crucial in web development, where creativity plays a key role in designing unique, engaging websites.

Moreover, AI lacks a human-centric understanding, which is essential for creating websites that resonate with users. For instance, understanding cultural nuances, emotions, and user motivations are areas where humans excel, but AI struggles.

Complex Decision-Making and Problem-Solving Abilities

Another limitation of AI is its inability to handle complex decision-making and problem-solving tasks that are common in web development. For example, when faced with a novel problem or an unexpected error, a human developer can draw on their experience and knowledge to devise a solution. In contrast, an AI system can only respond based on its programming and the data it has been trained on.

Difficulty in Understanding Nuances and Context Specific to Each Project

Every web development project is unique, with its own set of requirements, objectives, and constraints. Understanding these nuances and adapting to them requires a level of context-awareness that AI currently lacks. For instance, AI might struggle to understand why a particular design element is crucial for a specific client’s brand identity, or why certain features are essential for a particular user group.

The Human Touch: What Web Developers Bring

While AI continues to make strides in web development, there are certain aspects that it simply cannot replicate – the human touch. Here’s what web developers bring to the table that AI can’t match.

Creativity, Intuition, and User-Centric Design Thinking

Creativity and intuition are the heart and soul of web development. Developers often need to think outside the box to create engaging, unique designs that resonate with users. This creative process involves a deep understanding of user psychology, aesthetic sensibilities, and the latest design trends – something AI is yet to master.

Moreover, human developers excel at user-centric design thinking. They can empathize with users, understand their needs and frustrations, and design solutions that address these issues effectively. This human-centric approach is crucial in creating websites that offer an intuitive, satisfying user experience.

Adaptability and Problem-Solving Skills in Unique Project Scenarios

As we’ve already pointed out, every web development project is special, with its own set of challenges and requirements. Human developers can adapt to these unique scenarios, using their problem-solving skills and experience to overcome hurdles and deliver successful outcomes.

For instance, if a particular feature isn’t working as expected, developers can investigate the issue, identify the root cause, and implement a fix – a process that requires a level of critical thinking and adaptability that AI doesn’t possess.

Understanding of the Business Context and Effective Communication With Clients

Web development isn’t just about creating a functional, aesthetically pleasing website. It’s also about understanding the business context, aligning the website with the client’s brand identity, and meeting their business objectives.

This is where the value of web development consulting comes in. Human developers can communicate effectively with clients, understand their needs and expectations, and provide strategic advice to help them achieve their goals. They can explain complex technical concepts in layman’s terms, ensuring that clients are fully informed and involved in the development process.

Collaboration: AI and Web Developers Working Together

While AI has its limitations, it doesn’t mean that it’s not an incredibly valuable tool. By working together, AI tools and human developers can achieve a synergy that results in enhanced productivity and improved outcomes.

Synergy between AI Tools and Human Developers for Enhanced Productivity

AI tools can handle repetitive tasks, analyze large amounts of data, and perform tests at a speed and scale that humans simply cannot match. By automating these aspects of web development, AI frees up human developers to focus on complex problem-solving, strategic decision-making, and creative design work.

The result is a powerful synergy where AI and humans complement each other’s strengths: the speed, efficiency, and scalability of AI, combined with the creativity, intuition, and adaptability of human developers, can significantly enhance productivity and lead to better websites.

Examples of Successful Collaboration and Improved Outcomes

There are numerous examples of successful collaboration between AI and human developers. For instance, GitHub, a popular platform for software development, uses AI to suggest code completions to developers, speeding up the coding process and reducing the risk of errors.

In another example, web development firm Wix uses AI to automate the initial stages of website creation, such as selecting a suitable template and arranging basic layout elements. However, human developers then take over to customize the website according to the client’s specific needs, ensuring a unique, high-quality result.

These examples highlight how AI and human developers can work together effectively, leveraging their respective strengths to produce superior outcomes. It’s clear that the future of web development lies not in AI replacing humans, but in AI augmenting human capabilities, enabling developers to deliver more value and create better websites than ever before.

Looking Ahead: Future Prospects

As we look towards the future, it’s clear that AI will continue to play an increasingly prominent role in web development. However, the question remains: how might AI evolve to bridge the gap between its capabilities and human expertise?

Predictions for the Role of AI in Web Development in the Near Future

In the near future, we can expect AI to take on even more of the routine, repetitive tasks involved in web development. This includes aspects like coding, testing, and bug fixing, which can be automated to a large extent, freeing up human developers to focus on more complex, creative tasks.

AI will also become more integrated into the web design process. Tools like Adobe’s Sensei are already using AI to suggest design elements like color schemes and layouts, and this trend is likely to continue.

However, while AI will undoubtedly become more prevalent in web development, it’s unlikely to replace human developers entirely. The creativity, intuition, and critical thinking skills of human developers, as well as their ability to understand the unique needs of each client, are things that AI simply cannot replicate.

Potential Advancements That Could Bridge the Gap Between AI and Human Expertise

While AI currently lags behind humans in certain areas, advancements in technology could help bridge this gap. For instance, the development of more sophisticated AI algorithms could potentially enable AI tools to better mimic human creativity and intuition.

Moreover, advancements in natural language processing could improve AI’s ability to understand and interpret human language, making it easier for developers to communicate with AI tools and use them more effectively.

Ultimately, the goal should not be to replace human developers with AI, but rather to create a collaborative environment where AI tools and human developers work together to achieve superior outcomes. 

As we move forward, the synergy between AI and human expertise will continue to shape the future of web development, opening up new possibilities and paving the way for a more efficient, innovative industry.

Conclusion

As we’ve explored in this post, AI has made significant strides in the realm of web development, offering tools that can automate routine tasks, analyze large amounts of data quickly and accurately, and even assist with design decisions. 

However, it’s also clear that AI has its limits. It lacks the human touch – the creativity, intuition, and critical thinking skills that are so crucial to developing a unique, high-quality website.

Furthermore, while AI is excellent at following instructions and patterns, it struggles with tasks that require a deep understanding of context or subjective judgment. This is where human developers shine. They can understand the unique needs of each client, make strategic decisions, and inject creativity into their work in a way that AI simply cannot match.

Yet, the story doesn’t end here. AI is continually evolving, and future advancements could see it taking on a more prominent role in web development. However, it’s unlikely to replace human developers entirely. Instead, the future of web development lies in the synergy between AI and human expertise.

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AI and Web Scraping: The Dynamic Duo

In the dynamic realm of digital technology, few combinations have shown as much potential and impact as AI and web scraping. Imagine the power of predictive analysis, neural networks, and machine learning fused with the capacity to extract vast amounts of data seamlessly from the web. It’s a match made in digital heaven, setting the stage for a new wave of innovations.

The Rising Tide of a Digital Renaissance

The term ‘Renaissance’ refers to a period characterized by a revival of art, culture, and intellectual achievements. Similarly, today’s digital domain is experiencing its own renaissance, spurred by advanced technologies. And at the heart of this transformation lies the combined potential of AI and web scraping, revolutionizing how businesses perceive, obtain, and utilize data.

The digital renaissance spurred by AI in data extraction

A Story of Digital Elevation

Consider a medium-sized e-commerce business. Just a decade ago, their decisions were largely based on generalized market research, periodic customer surveys, and intuition. Fast forward to today, with AI-driven web scraping at their disposal, they can tailor user experiences based on real-time data, predict market trends, and even adjust pricing models instantaneously.

The business no longer just ‘hopes’ to meet consumer expectations; it dynamically evolves with them. This isn’t just an isolated business transformation story; it’s a reflection of the new norm in the digital era.

The Role of AI in Sculpting the Digital Landscape

Artificial Intelligence, often seen as a mystical entity, has now rooted itself in practical, tangible changes in the digital domain. From simple tasks to complex problem-solving, AI has reshaped the way processes function. Combine this with web scraping, and you’ve got a powerful tool that not only extracts data but does so intelligently.

For instance, the Automation boon has led to a 70% reduction in manual data entry tasks, providing employees more time for strategic tasks. Another digital milestone? Predictive product placements based on AI-driven web scraping analytics, allowing e-commerce businesses to strategically position products that are more likely to be purchased by a visiting customer.

Facts, Figures, and the Way Forward

Harnessing the potential of AI and web scraping isn’t just about real-time solutions; it’s about preparing for the future. By 2024, experts predict:

● A 120% increase in spending on artificial intelligence.

● Web scraping techniques will evolve to extract not just quantitative, but qualitative data, painting a clearer picture of market landscapes.

With these statistics and advancements, it’s evident that the digital renaissance is not just a fleeting trend but a sustainable, long-term movement. The road ahead seems promising for businesses willing to adapt, evolve, and embrace the pioneering change via web scraping.

The intricate dance of machine learning and massive data extraction

Machine Learning: The Heartbeat of Intelligent Data Extraction

Machine Learning (ML) is the essence of modern AI. It’s the mechanism that lets AI “learn” from data, and adapt without explicit programming. When combined with web scraping, ML takes raw, unstructured internet data and transforms it into something digestible and actionable.

Imagine it this way: If web scraping is the process of mining raw diamonds (data), then machine learning is the expert craftsmanship that turns these diamonds into exquisite jewelry (information). It’s not just about extraction; it’s about refinement.

Neural Networks: Simulating Human Brains for Superior Data Handling

Borrowing inspiration from our human neural networks, these algorithms are designed to recognize patterns. They interpret sensory data, clustering raw data through a process that mirrors human cognition. When neural magic in web scraping happens, it’s largely because of these neural networks.

For instance, a standard web scraping tool might extract all product reviews from an e-commerce site. In contrast, one powered by neural networks could discern the sentiment behind the reviews, differentiating positive feedback from negative, providing invaluable insights to businesses.

Big Data: The Vast Ocean and AI’s Incredible Diving Ability

In today’s age, we often hear the term ‘Big Data’. It’s vast, almost unfathomable. But it’s one thing to dive into this ocean and another to retrieve treasures from its depths. That’s where AI’s big data prowess comes into play.

It’s not about merely extracting information; it’s about making sense of it. Consider social media platforms that generate terabytes of data daily. Using AI and web scraping, companies can derive trends, preferences, and even predict future behaviors, giving them an edge in hyper-competitive markets.

AI-Driven Web Scraping: The Undeniable Business Catalyst

Case Studies: The Proof is in the Success

Behind every statistic about AI and web scraping’s efficacy, there’s a real-world story of a company that scaled new heights. These businesses don’t just benefit from more data; they harness better, clearer, and actionable insights.

Consider a fashion retailer that once relied on seasonality and intuition to stock inventory. With AI-driven web scraping, they now monitor global fashion trends, celebrity influences, and even regional preferences in real-time. Their inventory is never outdated; it’s always in sync with demand, optimizing both sales and customer satisfaction.

The Scalability Revolution: Not Just Growth, but Exponential Growth

In the world of business, growth is paramount. But the type of growth AI facilitates isn’t linear; it’s exponential. It’s a shift from adding resources for incremental gains to optimizing current resources for multiplicative results.

AI allows for predictive analysis, which means businesses can anticipate market shifts and consumer behaviors. This isn’t just scaling in terms of size or revenue, but scaling in intelligence, responsiveness, and agility.

The Edge of Actionable Business Intelligence

Data on its own holds potential energy, like a boulder atop a hill. Business intelligence is the force that nudges this boulder, converting potential energy into kinetic, resulting in movement, momentum, and change.

Through AI-driven web scraping, companies transition from being reactive to proactive. They don’t wait for quarterly reports to adjust strategies; they evolve in real-time. Whether it’s adjusting marketing strategies based on current events or reconfiguring supply chains due to anticipated disruptions, businesses are no longer mere players; they are ahead of the game.

Charting the responsible path in the age of limitless data

The Ethical Implications: Why Web Scraping Isn’t Always Black and White

The realm of web scraping, when infused with AI’s capabilities, has certainly granted businesses unparalleled insights. However, with this immense power comes significant responsibility. The ethical concerns surrounding web scraping often circle the debate between open-source information and personal privacy. What data is truly public? And even if it is, does that automatically grant businesses the right to harness it without explicit consent?

Consider the public outcry when people discover their personal reviews, comments, or even photos being used without their knowledge. Ethical web scraping isn’t just about adhering to legal guidelines but respecting the unsaid boundaries of personal space in the digital domain.

AI’s Solutions to Ethical Dilemmas: Navigating the Gray Areas

The AI realm has not been a silent spectator to these ethical challenges. On the contrary, advancements in responsible AI aim to create a harmonious balance between data extraction and ethical considerations.

For instance, AI models are now being trained to recognize and avoid personally identifiable information during web scraping processes, ensuring the data extracted respects individual privacy. Moreover, sophisticated AI models are also helping businesses navigate the maze of international data privacy laws, ensuring compliance and safeguarding against potential legal pitfalls.

The Delicate Dance of Innovation and Integrity

At the intersection of AI and web scraping lies a critical balance — the balance between pushing the boundaries of innovation while upholding the principles of digital morality. As businesses increasingly rely on these tools, it’s paramount that they also champion the cause of responsible and ethical data collection, thus setting a benchmark for the entire industry.

A glimpse into the promising and balanced future of web scraping

Merging Technological Wonders with Ethical Practices

As we gaze into the crystal ball of the future, it’s clear that web scraping with AI is not just a passing phase but a cornerstone of future digital strategies. However, the dream is not just about technological marvels but about a future where innovation and ethics walk hand in hand.

For the naysayers who believe this to be a utopian dream, advancements are already underway. AI models of tomorrow are being designed with an ethical foundation, ensuring they respect user privacy, adhere to global data regulations, and more importantly, uphold the ethos of digital respect.

The Coexistence Dream: Data and Morality in the Digital Age

The digital age, often deemed as the age of information, has a responsibility. A responsibility to ensure that while we chase the endless horizons of innovation, we don’t leave behind the essential human values of respect, privacy, and integrity.

The future promises AI models that not only understand this but champion this cause. Imagine a world where businesses don’t just collect data but do so with explicit consent, where AI models transparently explain their data sources, and where users feel safe, knowing their digital footprints are respected.

Challenges and Triumphs: The Road Ahead for the Next Generation

While the journey towards a balanced AI future is filled with promise, it isn’t devoid of challenges. From constantly evolving data regulations to the ever-changing dynamics of the digital world, the path is complex.

However, with challenges come opportunities. The next generation of AI enthusiasts, data scientists, and ethical hackers have a unique challenge — to sculpt a world that marvels at technological innovations while echoing with the laughter of ethical victories. It’s a world where web scraping and AI don’t just coexist but thrive in harmony.

Conclusion: Harmonizing Technology with Ethics in the Digital Landscape

In the constantly evolving world of digital innovation, the synergy between AI and web scraping stands out as a testament to human ingenuity. It’s not just about collecting data but transforming that data into meaningful insights, actionable business strategies, and forging pathways to unprecedented growth. Yet, with this potential comes the weighty responsibility of ethical considerations.

As we’ve journeyed through the intricate dance of AI-powered web scraping, it’s evident that the future beckons with promises of technological marvels harmoniously coexisting with ethical practices. From ensuring data privacy and adhering to global regulations to championing the principles of digital respect and morality, the road ahead is both challenging and promising.

For businesses, data scientists, AI enthusiasts, and every netizen, the message is clear: The future of web scraping with AI is not just about harnessing the vast ocean of data but doing so with a moral compass firmly in hand. As we venture forth, may we all be pioneers in crafting a digital world that’s not only advanced in its capabilities but also steadfast in its ethical commitments.

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• Perform code optimization and improve performance.

• Find and fix bugs in the code.

• Explain complex concepts and algorithms.

• Assist in developing ideas and choosing the right architecture.

• Create prototypes and demos of programs.

• Give advice on programming style and best practices.

• Automate repetitive tasks.

• Generate code based on specifications or specified parameters.

• Extend functionality with plugins and tools.

• Write documentation and comments to the code.

Today it’s incredibly stupid not to use the features of ChatGPT. It really is a universal assistant, which greatly simplifies the life of a programmer and increases the efficiency of development. This programming becomes a much more pleasant and efficient business than ever before.

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Training ML models, running experiments, being able to go back to previous versions of the model, comparing model results in step 3 and step 27 are current challenges for teams. In #CloudMTS, developers and data analysts can collaborate on these tasks in the MLOps platform.

Today, let’s talk about where else (and why) the resources to run complex models come from, and how AI and cloud computing are intertwined.

The development of artificial intelligence through the lens of cloud computing

The term artificial intelligence was coined by the founder of functional programming, John McCarthy, in 1956. Although the first programs capable of playing checkers and chess appeared at least five years earlier. Since then, artificial intelligence systems have come a long way: AlphaGo beat Korean pro Lee Sedol in a Go match, and the Watson computer won the Jeopardy intellectual quiz. Companies are investing in machine learning technologies, developing and deploying big language models like ChatGPT, and embedding them in BI systems and other analytics solutions.

Progress in AI is giving a sense of a new paradigm shift. The way software is created and delivered is fundamentally changing. Clouds have greatly empowered machine learning model companies. Some are even developing their own cloud platforms to offer AI systems in SaaS format to customers. In particular, advanced detection and response (XDR) technology in the cybersecurity market relies heavily on cloud-based AI.

AI is on its way to radically changing most aspects of the enterprise, not to mention many aspects of human life. And the persistent scalability of the cloud will play an integral, interconnected role in this.

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The author already had some experience with computer vision tasks

the task sounded as if it could be done very quickly without much effort and, what is important, without a lot of computing resources (all nets were trained in colab or in kagle)

the problem could have some practical application (well, in theory…)

At first it was planned as a purely educational project, but then I got into the idea of it and decided to refine it to the state that I can.

In what follows, I will talk about how I approached this task, and in doing so I will try to follow the code from the notebook where all the magic happened, while also trying to explain some of my actions. Maybe this will help someone get over their fear of the “blank slate” and see that this kind of thing is really easy to do!

Tools
First things first, let me tell you about the tools which were used for this project.

Colab/Kaggle: used to train networks on GPUs.

Weights And Biases: a service where I was saving models, their descriptions, adding losses, metrics values, training parameters, preprocessing. In general, I kept complete records. You can read the data here. The metadata section was slightly changed while writing the code – it actually contains the parameters of training and preprocessing. In the files section you can find a description of the network (how its layers are arranged), download the trained weights of the network and look at the values of losses and metrics.

Training data
Well, I should probably start by choosing the data to train the neural network. For this I searched data sets on Cagle (see here) and this site caught my eye.

Actually, as it turned out, there is a competition from Google, related just to the recognition of landmarks. Here was the first problem: dataset weighs \approx100gb. Realizing that the grids in the future I will learn not on my bakery, I had to give up this option. After some more research, I settled on this dataset. It contains 210 classes and about 50 pictures per class. The pictures are all different sizes, taken from different angles, from different distances. In general, the dataset is not refined at all, and so far I’ve only worked with these.

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In the first sentence of their article, the authors from OpenAI admit: “Language models have become more functional and more pervasive, but we don’t understand how they work.” This “ignorance” of exactly how individual neurons in a neural network behave to produce output data is referred to as the “black box.” According to Ars Technica, trying to look inside the “black box,” researchers from OpenAI used their GPT-4 language model to create and evaluate natural-language explanations of neuronal behavior in a simpler language model, GPT-2. Ideally, having an interpretable AI model would help achieve a more global goal called “AI matching.” In this case, we would have assurances that AI systems would behave as intended and reflect human values.

OpenAI wanted to figure out which patterns in the text cause neuron activation, and moved in stages. The first step was to explain neuron activation using GPT-4. The second was to simulate neuronal activation with GPT-4, given the explanation from the first step. The third was to evaluate the explanation by comparing simulated and real activations. GPT-4 identified specific neurons, neural circuits, and attention heads, and generated readable explanations of the roles of these components. The large language model also generated an explanation score, which OpenAI calls “a measure of the ability of the language model to compress and reconstruct neuronal activations using natural language.”

During the study, OpenAI offered to duplicate the work of GPT-4 in humans and compared their results. As the authors of the article admitted, both the neural network and the human “performed poorly in absolute terms.”

One explanation for this failure, suggested by OpenAI, is that neurons can be “polysemantic,” meaning that a typical neuron in the context of a study can have multiple meanings or be associated with multiple concepts. In addition, language patterns may contain “alien concepts” for which people simply do not have words. This paradox could arise for various reasons: for example, because language models care about the statistical constructs used to predict the next token; or because the model has discovered natural abstractions that people have yet to discover, such as a family of similar concepts in non-comparable domains.

The bottom line at OpenAI is that not all neurons can be explained in natural language; and so far, researchers can only see correlations between input data and the interpreted neuron at a fixed distribution, with past scientific work showing that this may not reflect a causal relationship between the two. Despite this, the researchers are quite optimistic and confident that they have succeeded in laying the groundwork for machine interpretability. They have now posted on GitHub the code for the automatic interpretation system, the GPT-2 XL neurons and the explanation data sets.

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Note

To create a new machine learning project from the GitHub template, go to the GitHub repository and click “Use this template”. GitHub template repositories are a very handy thing: they allow me and other users to generate new repositories with the same structure, branches, and files as the template.

The next page opens up project settings, such as repository name and privacy settings:

Having created the repository, click “Actions” on the top menu and wait a bit:

If a green checkmark appears, the project is ready – you can write code!
Next I’ll tell you why a particular file is added to the project and how the GitHub template was created.

Basic files

First, let’s look at the main files of the project, created on the basis of the template:

.gitignore

From the .gitignore file, GitHub draws information about which files to ignore when you commit a project to the GitHub repository. If you are creating a new repository from scratch, you can specify a pre-configured .gitignore file.

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Minimum requirements
All of the machine learning system patterns listed here are intended for deployment in a public cloud environment or on a Kubernetes cluster. For the purpose of this review, we have tried to abstract as much as possible from specific programming languages or platforms, although since Python is the most significant language for machine learning technology, most of the patterns can be implemented using Python.

Patterns
Service systems design patterns
Service systems design patterns are a set of off-the-shelf design solutions that can be used to organize production workflows that involve machine learning models.

Web single pattern
Using
When you need to quickly release a predictor in the simplest architecture.

The architecture
The Web single pattern offers an architecture where all the artifacts and code for the predictor model are enclosed in a single web server. Because in this pattern the REST (or GRPC) interface, preprocessing, and trained model are all in one place (on the server), you can create and deploy them as a simple predictor.
If you need to deploy multiple replicas at once, you will have to implement a load balancer or proxy server to access them. If you use GRPC as your interface, you should seriously consider implementing a client-side load balancer or layer-7 load balancer.

To build your model into a web server, you can use the model-in-image pattern or the model-loading pattern.

Diagram

Pros of
Ability to use a single programming language, such as Python, for web server, preprocessing and logical output.

• Easy to manage because of its simplicity.

• It is easier to troubleshoot.

• Minimally time-consuming to refine the model.

• As a starting production architecture, it is usually recommended to deploy on a single web server with synchronous processing.

Disadvantages

• Since all components are encapsulated in a single server or docker image, even a small patch would require updating the entire image.

• Upgrades will also require a service deployment, which in larger organizations requires doing a serious SDLC.

What you need to think about

• Procedures for upgrades and maintenance for each component.

• Scaling involves changes to web server management.

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