INTRODUCTION TO CLOUD NATIVE ARTIFICIAL INTELLIGENCE (CNAI):

Cloud native artificial intelligence revolutionizes traditional AI development and deployment. Whereas traditional models were often complex and difficult to scale, cloud native AI takes a modular and lightweight approach. This simplification empowers businesses to leverage the power of AI without getting bogged down in complexity.

The Emergence of Cloud Native:

Definition:

The cloud native approach revolutionizes application building and deployment for modern cloud environments (public, private, or hybrid). It employs containers, microservices, and automation to create loosely coupled, highly scalable, and easily managed systems. This enables frequent updates with minimal effort.

The Cloud Native Computing Foundation champions open-source tools and best practices, making the cloud native approach accessible to all.

  • Public Cloud:

    Cloud service providers (CSPs) like Amazon Web Services (AWS) or Microsoft Azure  create shared computing environments.  In these environments, the CSP owns and manages resources like servers, storage, and databases. These resources are then  delivered to multiple customers over the internet using a pay-as-you-go model. For instance, a company can leverage AWS, a cloud service provider, to power their e-commerce website. This allows them to benefit from the scalability and reliability offered by the cloud platform.

  • Private Cloud:

    Organizations can create dedicated computing environments, known as private clouds, for their exclusive use. These environments can be located on-premises, within the company’s data center, or hosted by a cloud service provider (CSP) with resources dedicated solely to that organization. While private clouds offer greater control and enhanced security, they also come with the downside of requiring more management overhead. To prioritize security, a bank chooses to store sensitive customer data within a private cloud. This private cloud environment offers them greater control and enhanced protection for this critical information.

  • Hybrid Cloud:

    Businesses can leverage a hybrid cloud strategy, which combines the strengths of both public and private cloud environments. This approach allows them to capitalize on the public cloud’s scalability and cost-efficiency for non-critical workloads. Meanwhile, sensitive data remains under their control and enjoys the enhanced security of the private cloud. Balancing cost-effectiveness with security, a healthcare provider utilizes a two-pronged cloud strategy.  They leverage the public cloud’s affordability for administrative tasks, while simultaneously maintaining patient medical records in the secure confines of a private cloud.

  • Containers:

    Think of a standardized shipping container. Imagine it filled with any kind of cargo (your application code, libraries, and settings). This container then ships easily around on different trucks (operating systems) without affecting the cargo inside. Containers act as a lightweight and portable way to package applications.

  • Micro services:

    Imagine a complex machine built from many smaller, independent modules. Each module, essentially a micro service, tackles a specific function, like handling user login or processing payments. These micro services communicate with each other through APIs, which act as well-defined interfaces. This modular approach simplifies development, deployment, and maintenance of applications.

  • Automation:

    In the world of cloud-native applications, automation takes center stage. It refers to the strategic use of technology to automatically handle tasks that would traditionally require manual intervention. Consequently, automation empowers engineers to deploy applications, scale resources, and manage infrastructure efficiently. This frees them up to focus on more strategic endeavors, like innovation and optimizing application functionality.

  • Loosely Coupled:

    Imagine building a house where each system functions independently, like separate modules. The plumbers can work on the pipes (one module) without needing the electricians to finish the wiring (another module). This concept translates directly to software with loosely coupled systems. Here, components have minimal dependencies on each other. This modularity allows for easier changes and updates to one part without affecting the others.  For example, an e-commerce website might leverage separate micro services for user accounts and product listings.  In this scenario, updating the user account system would not affect how products are, displayed on the website.

  • Scalable:

    Think of a child’s building block set. New blocks can be, easily added to create a larger structure.  Similarly, scalable systems act like these sets. They can handle growing workloads by adding more resources, like servers, on demand. This ensures smooth performance even with an influx of users or data.  For instance, a social media app can automatically scale up by adding more servers during a major event, ensuring users experience no lag despite the surge in traffic.

  • Manageable:

    Imagine a well-organized toolbox where every tool has its designated place.  Finding and using the right tool becomes a breeze. Manageable systems share this philosophy.  They are, designed with efficient operation and maintenance in mind. This translates to features like automated monitoring, logging, and configuration management. These features simplify troubleshooting and updates by providing real-time insights and streamlined processes. For example, a cloud-based application can leverage automated alerts to proactively signal potential issues and enable the ability to roll back changes if necessary.

Cloud Native Artificial Intelligence (CNAI) emerges as the next evolution of Cloud Native technology. It extends the core principles of scalability, flexibility, and automation to the realm of building and deploying AI applications. This approach frees up AI experts to focus on their core tasks, like designing algorithms, by automating repetitive tasks involved in running AI models. Essentially, Cloud Native Artificial Intelligence (CNAI) streamlines AI development and deployment, allowing specialists to dedicate less time to infrastructure management.

To understand CNAI’s context let us rewind to around 2013. Cloud Native (CN) gained significant traction alongside the rise of container technology like Docker and Kubernetes. While the term “Cloud Native” existed earlier, it has now become a guiding principle for building software systems that prioritize specific characteristics:

  • Modular: Broken down into smaller, reusable pieces (like micro services).
  • Deployable: Easy to put into production environments.
  • Scalable: Able to handle increased workloads by adding resources.
  • Resilient: Can recover from failures without significant downtime.

This approach offers greater flexibility and efficiency in developing and running applications.

Kubernetes (K8s):

Imagine Kubernetes (K8s) as a powerful conductor for your cloud resources. It has become the industry standard for managing these resources across diverse cloud environments, including private, public, and hybrid clouds. K8s orchestrates several key tasks:

  • Efficient workload distribution: It intelligently assigns tasks to different machines to optimize performance.
  • Resource management: K8s takes control of managing networks, storage, and computing power to ensure efficient utilization.
  • DevOps integration: It seamlessly integrates with GitOps workflows, enabling version control and streamlined deployments.

Recognizing the power of Kubernetes, most cloud providers now offer a version of Kubernetes as a service (KaaS). This makes it easier than ever to access the infrastructure and support needed to run various tasks, including AI and machine learning workloads.

Evolution of Artificial Intelligence:

Definition:

Artificial Intelligence (AI) stands as a prominent field within computer science. It focuses on creating intelligent machines capable of replicating human cognitive functions, such as learning and problem-solving. With roots dating back to 1956, AI has found applications in various fields, including speech recognition, image processing, and even game playing. Recent advancements in artificial neural networks (ANNs) and deep learning have significantly fueled its growth, particularly in areas like natural language understanding. To better understand AI’s capabilities, it can be broadly classified into two types: discriminative and generative. Discriminative AI excels at tasks like image recognition, while generative AI specializes in tasks like creating realistic images.

Artificial Neural Networks (ANNs):

Inspired by the structure and function of the human brain, an ANN is a series of interconnected nodes (artificial neurons) that loosely mimic the way biological neurons process information. These nodes receive inputs, apply mathematical functions, and send outputs to other nodes. By adjusting the connections between these nodes (weights), ANNs can learn patterns from data and make predictions on new data.

Deep Learning:

A subfield of machine learning, deep learning utilizes artificial neural networks with multiple layers (often much deeper than traditional ANNs). These layers allow the network to learn complex relationships within data. Deep learning models excel at tasks like image recognition, natural language processing, and speech recognition where vast amounts of data are available for training.

Discriminative AI:

Discriminative AI acts like a master classifier. It actively learns the differences between things and then leverages that knowledge to make predictions on new data.

  • Imagine a spam filter: It analyzes emails, learning the patterns that distinguish spam from real messages. This knowledge, essentially the “model,” is then used to predict whether a new email belongs in your spam folder.
  • Another example: Consider an AI trained on countless cat and dog pictures. It identifies the key features that differentiate cats from dogs. This model can then be applied to predict whether a new image contains a feline or a canine.

Discriminative AI thrives when there’s a clear distinction to be made and a wealth of existing data to learn from, often through supervised learning. For instance, it can analyze your writing style and predict the next word you’re likely to type based on massive amounts of text data.

Convolutional Neural Networks (CNNs):

Convolutional Neural Networks (CNNs) are a type of deep learning model that excels at image processing tasks. Though invented in the 1980s, they only recently gained traction due to:

  • Increased computing power: CNNs require a lot of processing power to train on large image datasets.
  • Availability of data: The massive amount of image data available today (think social media photos) is perfect for training CNNs.

Example: Imagine a CNN trained on countless pictures of cats and dogs. It can then analyze a new image and determine whether it contains a cat or a dog with high accuracy. CNNs are also used for tasks like object detection (finding specific objects in images) and image segmentation (breaking down images into different parts).

Generative AI:

Generative AI is like a creative artist inspired by data. It analyzes existing data to understand the underlying patterns and then uses that knowledge to generate entirely new things.

  • Imagine an AI trained on a massive dataset of music: It learns the patterns and structures that make up different musical styles. This AI can then generate new pieces of music that sound original but retain the characteristics of a particular genre.
  • Another example: An AI trained on countless paintings can “dream up” new works of art that capture the essence of different artistic styles.

Generative AI thrives in situations where the desired outcome is open-ended or subjective. It pushes the boundaries of AI by venturing into creative territories previously thought to be exclusive to humans. This has led to some truly remarkable breakthroughs in areas like music composition and artistic creation.

Transformers:

Transformers are a recent innovation (2017) in deep learning, particularly effective for tasks involving language. Unlike traditional models, they can “pay attention” to different parts of the input data (text) simultaneously, like having a kind of memory.

Think of it like this: Imagine translating a sentence. A traditional model might analyze each word one by one. A Transformer can consider the entire sentence at once, focusing on relevant parts (like verb tense) to produce a more accurate translation. This makes them powerful for tasks like question answering, text summarization, and machine translation. They’re also a key component in many Large Language Models (LLMs) like GPT-3, which is behind services like ChatGPT.

Large Language Models (LLMs):

Large Language Models (LLMs) are super-powered AI trained on massive data. They can handle complex prompts, adapt to different fields (law, medicine etc.), and keep learning with new data. Researchers are constantly improving how to train them (fine-tuning) for better results. This, along with faster computing hardware, lets scientists develop better AI models quicker.

Here’s a breakdown of the key points:

  • Powerful LLMs: These AI models are trained on a massive amount of data.
  • Versatility: They can handle long prompts, adapt to specific domains, and keep learning.
  • Improved Training: New techniques like RLHF and DPO are making LLMs even more effective.
  • Faster Development: Advancements in hardware and software allow for quicker creation of high-quality models.
  • Data Science Evolution: Traditional data analysis techniques are being adapted to work better with LLMs.

Both RLHF and DPO are techniques used to fine-tune Large Language Models (LLMs) to improve their performance and tailor them to specific tasks.

Reinforcement Learning from Human Feedback (RLHF):

This method involves training the LLM through a reward system based on human feedback. Imagine a trainer giving positive reinforcement (rewards) to a dog for good behavior. Similarly, RLHF rewards the LLM for generating outputs that align with human preferences. This can be a complex process as it requires setting up a feedback loop and interpreting human input effectively.

Example: Think of a chef learning from a food critic’s feedback. The critic provides detailed comments (rewards) to help the chef improve their dishes.
Direct Preference Optimization (DPO):

This method takes a more straightforward approach. It directly exposes the LLM to pairs of outputs and allows it to learn which output is preferred by humans. The LLM then adjusts its internal parameters to favor generating outputs that are more likely to be chosen as preferable. DPO is simpler to implement compared to RLHF but might require a larger amount of labeled data with clear preferences.

Example: Imagine showing the chef two different dishes and asking them to pick the better one. By repeatedly doing this, the chef learns which ingredients and cooking methods lead to more desirable meals.

Merging of Cloud Native and Artificial Intelligence:

AI is the big picture goal of creating intelligent machines. Machine Learning is a technique used by AI to learn from data and make predictions, like a spam filter learning to identify spam emails.

  • Artificial Intelligence (AI): The overall field focused on building intelligent systems. (Example: A self-driving car)
  • Machine Learning (ML): A technique within AI where computers learn from data without explicit programming. (Example: A spam filter that gets better at identifying spam over time)
  • Data Science: A field that combines statistics, math, and computer science to analyze data and apply machine learning. (Example: A data scientist might analyze customer data to recommend products)

Think of it like this: AI is like building a robot. Machine learning is a way to train the robot to perform tasks by learning from examples. Data science provides the tools and techniques to gather and analyze the data needed for machine learning.

Artificial Intelligence (AI) applications fall into two main categories: predicting things and creating new things.

  • Predictive AI: This type of AI focuses on analyzing data to understand patterns and predict future outcomes. It’s used for tasks like classifying emails (spam or not), grouping customers (by interests), or recognizing objects in images.
  • Generative AI: This type of AI uses data to create entirely new content. It can generate realistic images, compose music, write different kinds of creative text formats, or even design new drugs like RAG17 (an example mentioned in the passage).

The techniques used for each type of AI are quite different, reflecting the different goals of prediction vs. generation.

Analysis of Predictive vs. Generative AI Needs:

The table highlights the different computing, networking, and storage requirements for Predictive AI and Generative AI. Here’s a breakdown with examples:

Needs Generative AI Predictive AI Example
Computational Power
 Extremely high Moderate to high Generative AI needs a lot of processing power to train complex models that can create new content. Imagine training an AI to generate realistic images. This requires significant computational resources.
Predictive AI, while still requiring processing power, can often function well with general-purpose hardware. For instance, a spam filter doesn’t need the same level of power as an image-generating AI.
Data Volume and Diversity
 Massive, diverse datasets Specific historical data Generative AI thrives on a vast amount of data and variety. Training an AI to write different creative text formats would require a massive dataset of text and code.
Predictive AI typically focuses on analyzing specific historical data. For example, a system predicting customer churn (why customers leave) would use past customer data.
Model Training and Fine-tuning
 Complex, iterative training Moderate training Generative AI models are often intricate and require a lot of experimentation and fine-tuning during training. This can be a complex and iterative process.
Predictive AI models generally require less complex training, although some fine-tuning might be needed.
Scalability and Elasticity
 Highly Scalable and Elastic Scalability is necessary Generative AI workloads can have variable and intensive computational demands. The infrastructure needs to be highly scalable (grow as needed) and elastic (adjust resources up or down quickly).
Predictive AI, while needing scalability to handle data growth, may have less demanding elasticity requirements. Tasks might be handled in batches or triggered by events.
Storage and Throughput
 High-performance storage with excellent throughput Efficient storage with moderate throughput Generative AI often deals with diverse data types (text, images, code) requiring high-performance storage with fast access (throughput).
Predictive AI often focuses on analyzing structured data and may prioritize efficient storage with moderate throughput needs.
Networking
 High bandwidth and low latency Consistent and reliable Generative AI, particularly during distributed training, benefits from high bandwidth and low latency networks for data transfer and model synchronization.
Predictive AI requires a reliable network connection for data access, but latency might not be as critical.
Generative AI pushes the boundaries of computing resources due to its complex and data-hungry nature. Predictive AI, while still requiring significant resources, has more moderate needs compared to its generative counterpart.

What is Cloud Native Artificial Intelligence?

Cloud Native Artificial Intelligence (CNAI) addresses the complexities of building and running AI applications in the cloud. Imagine it as constructing a well-organized and efficient home for your AI model, contrasting the traditional approach which can be cumbersome to manage. CNAI provides the tools and techniques to simplify this process.

Benefits of CNAI:

  • Effortless Deployment: CNAI streamlines the deployment of your AI model on cloud infrastructure. Think of it as effortlessly relocating your house (AI model) to a new cloud environment.
  • On-Demand Scalability: CNAI empowers you to scale your AI model up or down based on your needs. Imagine expanding your house (AI model) to accommodate more visitors (data).
  • Proactive Monitoring: CNAI offers tools to continuously monitor your AI model’s performance, ensuring everything runs smoothly, just like checking on your house.
  • Cost Optimization: By effectively leveraging cloud resources, CNAI can help minimize the costs associated with running your AI model.
  • Cloud Power at Your Disposal: CNAI harnesses the cloud’s processing power (CPUs, GPUs), network, and storage to accelerate your AI model’s performance. Imagine having all the utilities (electricity, water) readily available in your cloud-based house.

By simplifying the process of building and running AI applications in the cloud, CNAI leads to faster development, improved performance, and reduced costs.

Why Cloud Native Artificial Intelligence?

Imagine building a complex machine, like an AI model. Traditionally, this process requires you to purchase all the parts (hardware) yourself and dedicate space to build it. This approach can be expensive and time-consuming.

Cloud Native Artificial Intelligence (CNAI) offers a solution – a machine building workshop in the cloud:

  • Shared Resources: The workshop is already equipped with all the tools and materials (computing power, storage) you need. These resources can be shared with others, making it more cost-effective than buying everything yourself.
  • Effortless Setup: You don’t need to waste time setting up the space (infrastructure) – the workshop is ready to use from the get-go.
  • Flexibility: The workshop can handle projects of all sizes, accommodating both small-scale training for basic AI models and large-scale projects for complex models.

Just as the workshop streamlines machine building, CNAI simplifies the development and running of AI models, making them faster, more efficient, and more affordable.

Using AI to Improve Cloud Native Systems

Artificial Intelligence (AI) is being used to improve Cloud Native systems, making them easier to manage. Imagine you’re a mechanic working on a complex car (Cloud Native system). Traditionally, you’d need a lot of technical knowledge to diagnose problems.

  • AI as a Mechanic’s Assistant: AI tools can be like a helpful assistant for the mechanic. These tools can analyze data (like car engine logs) and suggest potential problems in a way a human can understand (using natural language). An example is K8sGPT, which helps K8s operators (mechanics) by analyzing logs using AI.
  • New Opportunities: By combining Cloud Native (CN) and AI, entirely new possibilities emerge. In the future, even people with less technical knowledge might be able to operate and manage complex systems with the help of AI assistants. Imagine the mechanic’s assistant becoming so good that even someone with less car knowledge can fix the engine with its guidance.

AI is making Cloud Native systems more user-friendly and opening doors for wider adoption in the future.

CHALLENGES FOR CLOUD NATIVE ARTIFICIAL INTELLIGENCE:

Cloud Native Artificial Intelligence (CNAI) is a powerful approach, but it’s not without its hurdles. Here’s a breakdown of the challenges and how they relate to the stages of a typical Machine Learning (ML) pipeline:

Imagine building a race car (AI model) using a high-tech workshop (Cloud Native platform).

Challenges Vary:

Different people involved (data scientists, engineers) might face different challenges in the workshop, just like different roles in building a car might have different complexities.

ML Pipeline Stages:

Building an AI model is like assembling a car, with different stages:

  • Data Preparation: Cleaning and organizing data can be complex, especially for large datasets.
  • Model Training: This stage requires a lot of experimentation (like trying different engine configurations) to find the best model. Cloud Native tools are still evolving to handle this efficiently.
  • CI/CD & Model Registry: Securely storing and managing different versions of your AI model (like car blueprints) is crucial.
  • Model Serving: Deploying the AI model for real-world use needs careful consideration.
  • Observability: Tracking the AI model’s performance and identifying any issues (like monitoring the car’s engine) is essential.

Specific Challenges:

  • Data Volume and Complexity: Large datasets and complex models (like high-performance engines) require significant computing power and storage, which can be challenging for CNAI to manage.
  • GPU Sharing: While Cloud Native can handle CPU scheduling, efficiently sharing powerful graphics cards (GPUs) needed for training complex models is still under development. Imagine needing a special tool (GPU) in the workshop, and efficiently sharing it between different car projects.
  • Security: The sensitive nature of data and the value of AI models themselves necessitate robust security measures. This is like needing high security for the car blueprints and the finished race car.
  • Observability and Model Tuning: Monitoring the AI model’s performance and fine-tuning it over time requires advanced tools. Imagine needing sophisticated equipment to monitor the car’s performance and make adjustments for optimal racing.

CNAI offers a powerful approach for building and deploying AI models, but challenges remain, particularly in handling large data volumes, complex models, and ensuring efficient resource utilization and security.

Data Preparation:

The first step in building any AI model is data preparation, and Cloud Native AI (CNAI) faces some unique challenges here. Imagine you’re building a race car (AI model), and the first step is gathering parts (data preparation).

Key Challenges:

  • Massive Data Size:

    Building better AI models requires ever-increasing amounts of data, growing faster than Moore’s Law. This is like needing a huge pile of parts for your race car, which can be overwhelming to manage.

  • Data Synchronization:

    Data often comes from various sources and formats, and you need to ensure it’s consistent between development and deployment environments. This is like collecting parts from different stores and making sure they all fit together seamlessly in the final car (model).  Additionally, distributed computing adds complexity, like needing different tools to manage parts in different locations of your workshop.

    • Challenge: Data scientists might develop code using small data sets locally, but scaling this up for production can be difficult, requiring code rewrites. This is inefficient.
    • Potential Solutions:
      • Standardized Interfaces: Using industry-standard interfaces across the entire AI development process can streamline things. Imagine using a universal instruction manual (interface) for all the car parts, making assembly (development) easier.
      • Distributed Computing Frameworks: Tools like Kubeflow or Ray can help run the same code locally and in production environments, eliminating the need for rewrites. Think of a workshop that can handle assembling the car both as a small model and a full-scale race car.
  • Data Governance:

    Ensuring responsible AI development requires strong data governance practices. This is like having clear rules and guidelines for handling all the car parts in your workshop.

    • Privacy and Security: Complying with data privacy regulations and protecting sensitive data with strong security measures is crucial. Imagine needing secure storage for all the car parts, especially the valuable ones.
    • Ownership and Lineage: Knowing who owns and has access to the data throughout the AI lifecycle is essential. This is like keeping track of who has access to which car parts and their history (lineage). Data lineage tools can help with this.
    • Mitigating Bias: AI models can reflect biases in the data they’re trained on. It’s important to use diverse datasets, monitor for bias, and ensure fair and ethical outcomes. This is like making sure your race car doesn’t have any design flaws that cause it to swerve in a certain direction unfairly.

Model Training:

Training AI models requires a lot of data and processing power. Here’s why it can be tricky in Cloud Native AI (CNAI):

  • Data Explosion: The amount of data needed to train models is growing rapidly. Imagine needing a massive amount of bricks (data) to build a complex castle (AI model).
  • Parallelizing the Build: To train models faster, we need to break down the work into smaller tasks and complete them simultaneously (like having multiple builders working on different sections of the castle). CNAI needs efficient ways to distribute this workload.
  • Scaling Up Smoothly: Training is an ongoing process with many steps (like constantly modifying the castle design). Scaling up the training process to handle more data or computing power smoothly can be complex (like adding more builders and materials without everything falling apart).

Rising Processing Demands:

Training complex AI models requires a lot of processing power, like expensive tools for building a castle (AI model). Cloud Native AI (CNAI) faces these challenges:

  • Powerful Tools, Limited Availability: Powerful tools like GPUs (special hammers and drills) are in high demand and can be expensive.
  • Sharing the Tools: CNAI is working on ways to efficiently share these powerful tools (GPUs) between multiple projects (like multiple builders using the same tools to work on different parts of the castle). Technologies like vGPUs and MIG allow this sharing while keeping things isolated (each builder has their own designated workspace).
  • Careful Management Needed: Sharing resources effectively requires careful planning and coordination (like making sure builders don’t get in each other’s way). Close collaboration between AI and Cloud Native teams is crucial for smooth operation.

Cost Efficiency:

Cloud Native AI (CNAI) offers benefits beyond just building powerful AI models. Here’s how it helps with cost and sustainability:

  • Right-sizing Resources: Imagine renting tools (cloud resources) to build a house (AI model). CNAI lets you rent only the tools you need at any given time, not the whole toolbox (like renting a hammer and saw for a small job, not the entire construction equipment set). This saves money on unused resources.
  • Sharing Expensive Tools: Expensive tools like GPUs (specialized construction equipment) can be shared between multiple projects (building different parts of the house). This makes them more affordable.
  • Green AI: CNAI helps reduce the environmental impact of AI in a few ways:
    • Scaling Up Efficiently: Only using the resources you need (like using a smaller generator for a smaller job) reduces energy consumption.
    • Smaller Models: Using smaller, more efficient AI models (like a simpler house design) requires less processing power overall.
    • Renewable Energy: Running AI models in regions powered by renewable energy sources (like building your house using solar panels) is more sustainable.
  • Tracking the Impact: Tools are being developed to track and minimize the environmental impact of AI models (like calculating the carbon footprint of building your house).

In short, CNAI promotes cost-effective and environmentally conscious development of AI models.

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Scalability:

Cloud Native AI (CNAI) is great for building AI models, but scaling them up (making them handle more data or complex tasks) can be tricky. Here’s why:

  • AI Workflows are Complex: Imagine building a city (AI model) instead of a shed. There are many moving parts (data processing, training, etc.) that need to work together seamlessly.
  • Lots of Data, Lots of Training: Training AI models often involves large datasets and multiple training cycles (like needing a lot of materials and constantly revising the city plans). Scaling up the resources needed for this (like adding more construction workers and materials) requires careful coordination.
  • Different Needs, Different Solutions: Not all AI models are created equal (every city has unique needs). This makes it challenging to create one-size-fits-all solutions for scaling them up (a standardized construction plan won’t work for every city).

CNAI needs to find ways to manage these complexities and ensure smooth scaling of AI models in a distributed environment.

Orchestration:

Cloud Native AI (CNAI) uses tools and techniques to manage how AI models are built and run, similar to how a conductor organizes an orchestra (AI model) or a construction foreman schedules workers (different parts of the model).

Benefits of CNAI Orchestration:
  • Modular Workflows: Complex AI tasks are broken down into smaller, manageable pieces (like dividing instruments for the orchestra or assigning specific tasks to construction workers). This makes it easier to manage and scale individual parts.
Challenges:
  • GPU Sharing: Powerful graphics cards (GPUs) are like the lead violinist in the orchestra – essential but limited in number. CNAI needs to efficiently share GPUs between different AI projects.
  • Advanced Scheduling: Scheduling needs to be precise. Imagine the orchestra needing instruments to be available at specific times and construction workers needing materials delivered on schedule. CNAI is developing better scheduling tools to handle these complexities.
Future Advancements:
  • Batch Scheduling: New tools are being developed to better manage scheduling AI training jobs, which often involve large datasets and require all parts to run together (like the entire orchestra practicing a piece).

By improving orchestration and scheduling, CNAI aims to streamline the development and execution of AI models.

Custom Dependencies:

Building AI models often involves using specific tools and libraries (like using specialized tools for building a house). Cloud Native AI (CNAI) can face challenges with these custom dependencies:

  • Finding the Right Tools: Some AI projects might require specific libraries or frameworks that aren’t readily available in standard pre-built packages (like needing a unique tool not found in most toolboxes).
  • GPU Compatibility: For tasks requiring powerful graphics cards (GPUs), CNAI needs to ensure the right drivers and libraries are available to work with different GPU vendors and models (like needing the right attachments for the specific power tools you’re using).
  • Example: When training AI models on Nvidia GPUs, a specific library (NCCL) might be needed for optimal performance. Different versions of this library can impact results (like using the right drill bit for the specific material you’re drilling into).
The Importance of Reproducible Builds:

Just like good construction practices involve using the same materials and plans for consistency, using specific versions of tools and libraries helps ensure your AI model works the same way every time it’s built (reproducible builds). This avoids unexpected issues and makes troubleshooting easier.

In essence, CNAI needs to find ways to handle custom dependencies effectively to ensure smooth development and deployment of AI models.