ML Pipeline Architecture Design Patterns (With 10 Real-World Examples)

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. Getting a workflow ready which takes your data from its raw form to predictions while maintaining responsiveness and flexibility is the real deal.

At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. Many questions regarding building machine learning pipelines and systems have already been answered and come from industry best practices and patterns. But some of these queries are still recurrent and haven’t been explained well.

How should the machine learning pipeline operate? How should they be implemented to accommodate scalability and adaptability whilst maintaining an infrastructure that’s easy to troubleshoot?

ML pipelines usually consist of interconnected infrastructure that enables an organization or machine learning team to enact a consistent, modularized, and structured approach to building, training, and deploying ML systems. However, this efficient system does not just operate independently – it necessitates a comprehensive architectural approach and thoughtful design consideration.

But what do these terms – machine learning design and architecture mean, and how can a complex software system such as an ML pipeline mechanism work proficiently? This blog will answer these questions by exploring the following:

So let’s dive in!

What are ML pipeline architecture design patterns?

These two terms are often used interchangeably, yet they hold distinct meanings.

ML pipeline architecture is like the high-level musical score for the symphony. It outlines the components, stages, and workflows within the ML pipeline. The architectural considerations primarily focus on the arrangement of the components in relation to each other and the involved processes and stages. It answers the question: “What ML processes and components will be included in the pipeline, and how are they structured?”

In contrast, ML pipeline design is a deep dive into the composition of the ML pipeline, dealing with the tools, paradigms, techniques, and programming languages used to implement the pipeline and its components. It is the composer’s touch that answers the question: “How will the components and processes in the pipeline be implemented, tested, and maintained?”

Although there are a number of technical information concerning machine learning pipeline design and architectural patterns, this post primarily covers the following:

Advantages of understanding ML pipeline architecture

There are several reasons why ML Engineers, Data Scientists and ML practitioners should be aware of the patterns that exist in ML pipeline architecture and design, some of which are:

• Efficiency: understanding patterns in ML pipeline architecture and design enables practitioners to identify technical resources required for quick project delivery.

• Scalability: ML pipeline architecture and design patterns allow you to prioritize scalability, enabling practitioners to build ML systems with a scalability-first approach. These patterns introduce solutions that deal with model training on large volumes of data, low-latency model inference and more.

• Templating and reproducibility: typical pipeline stages and components become reproducible across teams utilizing familiar patterns, enabling members to replicate ML projects efficiently.

• Standardization: n organization that utilizes the same patterns for ML pipeline architecture and design, is able to update and maintain pipelines more easily across the entire organization.

Common ML pipeline architecture steps

Having touched on the importance of understanding ML pipeline architecture and design patterns, the following sections introduce a number of common architecture and design approaches found in ML pipelines at various stages or components.

ML pipelines are segmented into sections referred to as stages, consisting of one or several components or processes that operate in unison to produce the output of the ML pipeline. Over the years, the stages involved within an ML pipeline have increased.

Less than a decade ago, when the machine learning industry was primarily research-focused, stages such as model monitoring, deployment, and maintenance were nonexistent or low-priority considerations. Fast forward to current times, the monitoring, maintaining, and deployment stages within an ML pipeline have taken priority, as models in production systems require upkeep and updating. These stages are primarily considered in the domain of MLOps (machine learning operations).

Today different stages exist within ML pipelines built to meet technical, industrial, and business requirements. This section delves into the common stages in most ML pipelines, regardless of industry or business function.

Now that we understand the components within a standard ML pipeline, below are sub-pipelines or systems you’ll come across within the entire ML pipeline.

• Data Engineering Pipeline
• Feature Engineering Pipeline
• Model Training and Development Pipeline
• Model Deployment Pipeline
• Production Pipeline

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10 ML pipeline architecture examples

Let’s dig deeper into some of the most common architecture and design patterns and explore their examples, advantages, and drawbacks in more detail.

Single leader architecture

What is single leader architecture?

The exploration of common machine learning pipeline architecture and patterns starts with a pattern found in not just machine learning systems but also database systems, streaming platforms, web applications, and modern computing infrastructure. The Single Leader architecture is a pattern leveraged in developing machine learning pipelines designed to operate at scale whilst providing a manageable infrastructure of individual components.

The Single Leader Architecture utilises the master-slave paradigm; in this architecture, the leader or master node is aware of the system’s overall state, manages the execution and distribution of tasks according to resource availability, and handles write operations.

The follower or slave nodes primarily execute read operations. In the context of ML pipelines, the leader node would be responsible for orchestrating the execution of various tasks, distributing the workload among the follower nodes based on resource availability, and managing the system’s overall state.

Meanwhile, the follower nodes carry out the tasks the leader node assign, such as data preprocessing, feature extraction, model training, and validation.

A real-world example of single leader architecture

In order to see the Single Leader Architecture utilised at scale within a machine learning pipeline, we have to look at one of the biggest streaming platforms that provide personalised video recommendations to millions of users around the globe, Netflix.

Internally within Netflix’s engineering team, Meson was built to manage, orchestrate, schedule, and execute workflows within ML/Data pipelines. Meson managed the lifecycle of ML pipelines, providing functionality such as recommendations and content analysis, and leveraged the Single Leader Architecture.

Meson had 70,000 workflows scheduled, with over 500,000 jobs executed daily. Within Meson, the leader node tracked and managed the state of each job execution assigned to a follower node provided fault tolerance by identifying and rectifying failed jobs, and handled job execution and scheduling. 

Advantages and disadvantages of single leader architecture

In order to understand when to leverage the Single Leader Architecture within machine learning pipeline components, it helps to explore its key advantages and disadvantages.

• Notable advantages of the Single Leader Arthcutecture are fault tolerance, scalability, consistency, and decentralization. 
• With one node or part of the system responsible for workflow operations and management, identifying points of failure within pipelines that adopt Single Leader architecture is straightforward. 
• It effectively handles unexpected processing failures by redirecting/redistributing the execution of jobs, providing consistency of data and state within the entire ML pipeline, and acting as a single source of truth for all processes. 
• ML pipelines that adopt the Single Leader Architecture can scale horizontally for additional read operations by increasing the number of follower nodes.

However, in all its advantages, the single leader architecture for ML pipelines can present issues such as scaling, data loss, and availability. 

• Write scalability within the single leader architecture is limited, and this limitation can act as a bottleneck to the speed of the overall job/workflow orchestration and management. 
• All write operations are handled by the single leader node in the architecture, which means that although read operations can scale horizontally, the write operation handled by the leader node does not scale proportionally or at all.
• The single leader architecture can have significant downtime if the leader node fails; this presents pipeline availability issues and causes entire system failure due to the architecture’s reliance on the leader node.

As the number of workflows managed by Meson grew, the single-leader architecture started showing signs of scale issues. For instance, it experienced slowness during peak traffic moments and required close monitoring during non-business hours. As usage increased, the system had to be scaled vertically, approaching AWS instance-type limits. 

This led to the development of Maestro, which uses a shared-nothing architecture to horizontally scale and manage the states of millions of workflow and step instances simultaneously.

Maestro incorporates several architectural patterns in modern applications powered by machine learning functionalities. These include shared-nothing architecture, event-driven architecture, and directed acyclic graphs (DAGs). Each of these architectural patterns plays a crucial role in enhancing the efficiency of machine learning pipelines. 

The next section delves into these architectural patterns, exploring how they are leveraged in machine learning pipelines to streamline data ingestion, processing, model training, and deployment.

Directed acyclic graphs (DAG)

What is directed acyclic graphs architecture?

Directed graphs are made up of nodes, edges, and directions. The nodes represent processes; edges in graphs depict relationships between processes, and the direction of the edges signifies the flow of process execution or data/signal transfer within the graph.

Applying constraints to graphs allows for the expression and implementation of systems with a sequential execution flow. For instance, a condition in graphs where loops between vertices or nodes are disallowed. This type of graph is called an acyclic graph, meaning there are no circular relationships (directed cycles) among one or more nodes. 

Acyclic graphs eliminate repetition between nodes, points, or processes by avoiding loops between two nodes. We get the directed acyclic graph by combining the features of directed edges and non-circular relationships between nodes.

A directed acyclic graph (DAG) represents activities in a manner that depicts activities as nodes and dependencies between nodes as edges directed to another node. Notably, within a DAG, cycles or loops are avoided in the direction of the edges between nodes.

DAGs have a topological property, which implies that nodes in a DAG are ordered linearly, with nodes arranged sequentially. 

In this ordering, a node connecting to other nodes is positioned before the nodes it points to. This linear arrangement ensures that the directed edges only move forward in the sequence, preventing any cycles or loops from occurring.

A real-world example of directed acyclic graphs architecture

A fitting real-world example illustrating the use of DAGs is the process within ride-hailing apps like Uber or Lyft. In this context, a DAG represents the sequence of activities, tasks, or jobs as nodes, and the directed edges connecting each node indicate the execution order or flow. For instance, a user must request a driver through the app before the driver can proceed to the user’s location.

Furthermore, Netflix’s Maestro platform uses DAGs to orchestrate and manage workflows within machine learning/data pipelines. Here, the DAGs represent workflows comprising units embodying job definitions for operations to be carried out, known as Steps.

Practitioners looking to leverage the DAG architecture within ML pipelines and projects can do so by utilizing the architectural characteristics of DAGs to enforce and manage a description of a sequence of operations that is to be executed in a predictable and efficient manner. 

This main characteristic of DAGs enables the definition of the execution of workflows in complex ML pipelines to be more manageable, especially where there are high levels of dependencies between processes, jobs, or operations within the ML pipelines.

For example, the image below depicts a standard ML pipeline that includes data ingestion, preprocessing, feature extraction, model training, model validation, and prediction. The stages in the pipeline are executed consecutively, one after the other, when the previous stage is marked as complete and provides an output. Each of the stages within can again be defined as nodes within DAGs, with the directed edges indicating the dependencies between the pipeline stages/components.

Advantages and disadvantages of directed acyclic graphs architecture

• Using DAGs provides an efficient way to execute processes and tasks in various applications, including big data analytics, machine learning, and artificial intelligence, where task dependencies and the order of execution are crucial.

• In the case of ride-hailing apps, each activity outcome contributes to completing the ride-hailing process. The topological ordering of DAGs ensures the correct sequence of activities, thus facilitating a smoother process flow.

• For machine learning pipelines like those in Netflix’s Maestro, DAGs offer a logical way to illustrate and organize the sequence of process operations. The nodes in a DAG representation correspond to standard components or stages such as data ingestion, data preprocessing, feature extraction, etc. 

• The directed edges denote the dependencies between processes and the sequence of process execution. This feature ensures that all operations are executed in the correct order and can also identify opportunities for parallel execution, reducing overall execution time.

Although DAGs provide the advantage of visualizing interdependencies between tasks, this advantage can become disadvantageous in a large complex machine-learning pipeline that consists of numerous nodes and dependencies between tasks. 

• Machine learning systems that eventually reach a high level of complexity and are modelled by DAGs become challenging to manage, understand and visualize.

• In modern machine learning pipelines that are expected to be adaptable and operate within dynamic environments or workflows, DAGs are unsuitable for modelling and managing these systems or pipelines, primarily because DAGs are ideal for static workflows with predefined dependencies. 

However, there may be better choices for today’s dynamic Machine Learning pipelines. For example, imagine a pipeline that detects real-time anomalies in network traffic. This pipeline has to adapt to constant changes in network structure and traffic. A static DAG might struggle to model such dynamic dependencies.

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Foreach pattern

What is foreach pattern?

Architectural and design patterns in machine learning pipelines can be found in operation implementation within the pipeline phases. Implemented patterns are leveraged within the machine learning pipeline, enabling sequential and efficient execution of operations that act on datasets. One such pattern is the foreach pattern.

The foreach pattern is a code execution paradigm that iteratively executes a piece of code for the number of times an item appears within a collection or set of data. This pattern is particularly useful in processes, components, or stages within machine learning pipelines that are executed sequentially and recursively. This means that the same process can be executed a certain number of times before providing output and progressing to the next process or stage.

For example, a standard dataset comprises several data points that must go through the same data preprocessing script to be transformed into a desired data format. In this example, the foreach pattern lends itself as a method of repeatedly calling the processing function ‘n’ a number of times. Typically ‘n’ corresponds to the number of data points. 

Another application of the foreach pattern can be observed in the model training stage, where a model is repeatedly exposed to different partitions of the dataset for training and others for testing for a specified amount of time.

A real-world example of foreach pattern

A real-world application of the foreach pattern is in Netflix’s ML/Data pipeline orchestrator and scheduler, Maestro. Maestro workflows consist of job definitions that contain steps/jobs executed in an order defined by the DAG (Directed Acyclic Graph) architecture. Within Maestro, the foreach pattern is leveraged internally as a sub-workflow consisting of defined steps/jobs, where steps are executed repeatedly.

As mentioned earlier, the foreach pattern can be used in the model training stage of ML pipelines, where a model is repeatedly exposed to different partitions of the dataset for training and others for testing over a specified amount of time.

Advantages and disadvantages of foreach pattern

• Utilizing the DAG architecture and foreach pattern in an ML pipeline enables a robust, scalable, and manageable ML pipeline solution. 
• The foreach pattern can then be utilized within each pipeline stage to apply an operation in a repeated manner, such as repeatedly calling a processing function a number of times in a dataset preprocessing scenario. 
• This setup offers efficient management of complex workflows in ML pipelines.

Below is an illustration of an ML pipeline leveraging DAG and foreach pattern. The flowchart represents a machine learning pipeline where each stage (Data Collection, Data Preprocessing, Feature Extraction, Model Training, Model Validation, and Prediction Generation) is represented as a Directed Acyclic Graph (DAG) node. Within each stage, the “foreach” pattern is used to apply a specific operation to each item in a collection. 

For instance, each data point is cleaned and transformed during data preprocessing. The directed edges between the stages represent the dependencies, indicating that a stage cannot start until the preceding stage has been completed. This flowchart illustrates the efficient management of complex workflows in machine learning pipelines using the DAG architecture and the foreach pattern.

But there are some disadvantages to it as well.

When utilizing the foreach pattern in data or feature processing stages, all data must be loaded into memory before the operations can be executed. This can lead to poor computational performance, mainly when processing large volumes of data that may exceed available memory resources. For instance, in a use-case where the dataset is several terabytes large, the system may run out of memory, slow down, or even crash if it attempts to load all the data simultaneously.

Another limitation of the foreach pattern lies in the execution order of elements within a data collection. The foreach pattern does not guarantee a consistent order of execution or order in the same form the data was loaded. 

Inconsistent order of execution within foreach patterns can be problematic in scenarios where the sequence in which data or features are processed is significant. For example, if processing a time-series dataset where the order of data points is critical to understanding trends or patterns, an unordered execution could lead to inaccurate model training and predictions.

Embeddings

What is embeddings design pattern?

Embeddings are a design pattern present in traditional and modern machine learning pipelines and are defined as low-dimensional representations of high-dimensional data, capturing the key features, relationships, and characteristics of the data’s inherent structures. 

Embeddings are typically presented as vectors of floating-point numbers, and the relationships or similarities between two embeddings vectors can be deduced using various distance measurement techniques.

In machine learning, embeddings play a significant role in various areas, such as model training, computation efficiency, model interpretability, and dimensionality reduction.

A real-world example of embeddings design pattern

Notable companies such as Google and OpenAI utilize embeddings for several tasks present in processes within machine learning pipelines. Google’s flagship product, Google Search, leverages embeddings in its search engine and recommendation engine, transforming high-dimensional vectors into lower-level vectors that capture the semantic meaning of words within the text. This leads to improved search result performance regarding the relevance of search results to search queries.

OpenAI, on the other hand, has been at the forefront of advancements in generative AI models, such as GPT-3, which heavily rely on embeddings. In these models, embeddings represent words or tokens in the input text, capturing the semantic and syntactic relationships between words, thereby enabling the model to generate coherent and contextually relevant text. OpenAI also uses embeddings in reinforcement learning tasks, where they represent the state of the environment or the actions of an agent.

Advantages and disadvantages of embeddings design pattern

The advantages of the embedding method of data representation in machine learning pipelines lie in its applicability to several ML tasks and ML pipeline components. Embeddings are utilized in computer vision tasks, NLP tasks, and statistics. More specifically, embeddings enable neural networks to consume training data in formats that allow extracting features from the data, which is particularly important in tasks such as natural language processing (NLP) or image recognition. Additionally, embeddings play a significant role in model interpretability, a fundamental aspect of Explainable AI, and serve as a strategy employed to demystify the internal processes of a model, thereby fostering a deeper understanding of the model’s decision-making process. They also act as a data representation form that retains the key information, patterns, and features, providing a lower-dimensional representation of high-dimensional data that retains key patterns and information.

Within the context of machine learning, embeddings play a significant role in a number of areas.

1. Model Training: Embeddings enable neural networks to consume training data in formats that extract features from the data. In machine learning tasks such as natural language processing (NLP) or image recognition, the initial format of the data – whether it is words or sentences in text or pixels in images and videos – is not directly conducive to training neural networks. This is where embeddings come into play. By transforming this high-dimensional data into dense vectors of real numbers, embeddings provide a format that allows the network’s parameters, such as weights and biases, to adapt appropriately to the dataset.
2. Model Interpretability: The models’ capacity to generate prediction results and provide accompanying insights detailing how these predictions were inferred based on the model’s internal parameters, training dataset, and heuristics can significantly enhance the adoption of AI systems. The concept of Explainable AI revolves around developing models that offer inference results and a form of explanation detailing the process behind the prediction. Model interpretability is a fundamental aspect of Explainable AI, serving as a strategy employed to demystify the internal processes of a model, thereby fostering a deeper understanding of the model’s decision-making process. This transparency is crucial in building trust among users and stakeholders, facilitating the debugging and improvement of the model, and ensuring compliance with regulatory requirements. Embeddings provide an approach to model interpretability, especially in NLP tasks where visualizing the semantic relationship between sentences or words in a sentence provides an understanding of how a model understands the text content it has been provided with.
3. Dimensionality Reduction: Embeddings form data representation that retains key information, patterns, and features. In machine learning pipelines, data contain a vast amount of information captured in varying levels of dimensionality. This means that the vast amount of data increases compute cost, storage requirements, model training, and data processing, all pointing to items found in the curse of dimensionality scenario. Embeddings provide a lower-dimensional representation of high-dimensional data that retains key patterns and information.
4. Other areas in ML pipelines: transfer learning, anomaly detection, vector similarity search, clustering, etc.

Although embeddings are useful data representation approaches for many ML tasks, there are a few scenarios where the representational power of embeddings is limited due to sparse data and the lack of inherent patterns in the dataset. This is known as the “cold start” problem, an embedding is a data representation approach that’s generated by identifying the patterns and correlations within elements of datasets, but in situations where there are scarce patterns or insufficient amounts of data, the representational benefits of embeddings can be lost, which results in poor performance in machine learning systems such as recommender and ranking systems.

An expected disadvantage of lower dimensional data representation is loss of information; embeddings generated from high dimensional data might sometimes succumb to loss of information in the dimensionality reduction process, contributing to poor performance of machine learning systems and pipelines.

Data parallelism

What is data parallelism?

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A real-world example of data parallelism

A real-world scenario of how the principles of data parallelism are embodied in real-life applications is the groundbreaking work by Facebook AI Research (FAIR) Engineering with their novel system – the Fully Sharded Data Parallel (FSDP) system. 

This innovative creation has the sole purpose of enhancing the training process of massive AI models. It does so by disseminating an AI model’s variables over data parallel operators while also optionally offloading a fraction of the training computation to CPUs.

FSDP sets itself apart by its unique approach to sharding parameters. It takes a more balanced approach which results in superior performance. This is achieved by allowing training-related communication and computation to overlap. What is exciting about FSDP is how it optimizes the training of vastly larger models but uses fewer GPUs in the process. 

This optimization becomes particularly relevant and valuable in specialized areas such as Natural Language Processing (NLP) and computer vision. Both these areas often demand large-scale model training.

A practical application of FSDP is evident within the operations of Facebook. They have incorporated FSDP in the training process of some of their NLP and Vision models, a testament to its effectiveness. Moreover, it is a part of the FairScale library, providing a straightforward API to enable developers and engineers to improve and scale their model training.

The influence of FSDP extends to numerous machine learning frameworks, like fairseq for language models, VISSL for computer vision models, and PyTorch Lightning for a wide range of other applications. This broad integration showcases the applicability and usability of data parallelism in modern machine learning pipelines.

Advantages and disadvantages of data parallelism

• The concept of data parallelism presents a compelling approach to reducing training time in machine learning models. 
• The fundamental idea is to subdivide the dataset and then concurrently process these divisions on various computing platforms, be it multiple CPUs or GPUs. As a result, you get the most out of the available computing resources.
• Integrating data parallelism into your processes and ML pipeline is challenging. For instance, synchronizing model parameters across diverse computing resources has added complexity. Particularly in distributed systems, this synchronization may incur overhead costs due to possible communication latency issues. 
• Moreover, it is essential to note that the utility of data parallelism only extends to some machine learning models or datasets. There are models with sequential dependencies, like certain types of recurrent neural networks, which might not align well with a data parallel approach.

Model parallelism

What is model parallelism?

Model parallelism is used within machine learning pipelines to efficiently utilize compute resources when the deep learning model is too large to be held on a single instance of GPU or CPU. This compute efficiency is achieved by splitting the initial model into subparts and holding those parts on different GPUs, CPUs, or machines. 

The model parallelism strategy hosts different parts of the model on different computing resources. Additionally, the computations of model gradients and training are executed on each machine for their respective segment of the initial model. This strategy was born in the era of deep learning, where models are large enough to contain billions of parameters, meaning they cannot be held or stored on a single GPU.

A real-world example of model parallelism

Deep learning models today are inherently large in terms of the number of internal parameters; this results in needing scalable computing resources to hold and calculate model parameters during training and inference phases in ML pipeline. For example, GPT-3 has 175 billion parameters and requires 800GB of memory space, and other foundation models, such as LLaMA, created by Meta, have parameters ranging from 7 billion to 70 billion. 

These models require significant computational resources during the training phase. Model parallelism offers a method of training parts of the model across different compute resources, where each resource trains the model on a mini-batch of the training data and computes the gradients for their allocated part of the original model.

Advantages and disadvantages of model parallelism

Implementing model parallelism within ML pipelines comes with unique challenges. 

• There’s a requirement for constant communication between machines holding parts of the initial model as the output of one part of the model is used as input for another. 
• In addition, understanding what part of the model to split into segments requires a deep understanding and experience with complex deep learning models and, in most cases, the particular model itself. 
• One key advantage is the efficient use of compute resources to handle and train large models.

Federated learning

What is federated learning architecture?

Federated Learning is an approach to distributed learning that attempts to enable innovative advancements made possible through machine learning while also considering the evolving perspective of privacy and sensitive data. 

A relatively new method, Federated Learning decentralizes the model training processes across devices or machines so that the data doesn’t have to leave the premises of the machine. Instead, only the updates to the model’s internal parameters, which are trained on a copy of the model using unique user-centric data stored on the device, are transferred to a central server. This central server accumulates all updates from other local devices and applies the changes to a model residing on the centralised server.

A real-world example of federated learning architecture

Within the Federated Learning approach to distributed machine learning, the user’s privacy and data are preserved as they never leave the user’s device or machine where the data is stored. This approach is a strategic model training method in ML pipelines where data sensitivity and access are highly prioritized. It allows for machine learning functionality without transmitting user data across devices or to centralized systems such as cloud storage solutions.

Advantages and disadvantages of federated learning architecture

Federated Learning steers an organization toward a more data-friendly future by ensuring user privacy and preserving data. However, it does have limitations. 

• Federated learning is still in its infancy, which means a limited number of tools and technologies are available to facilitate the implementation of efficient, federated learning procedures. 
• Adopting federated learning in a fully matured organization with a standardized ML pipeline requires significant effort and investment as it introduces a new approach to model training, implementation, and evaluation that requires a complete restructuring of existing ML infrastructure. 
• Additionally, the central model’s overall performance relies on several user-centric factors, such as data quality and transmission speed.

Synchronous training

What is synchronous training architecture?

Synchronous Training is a machine learning pipeline strategy that comes into play when complex deep learning models are partitioned or distributed across different compute resources, and there is an increased requirement for consistency during the training process. 

In this context, synchronous training involves a coordinated effort among all independent computational units, referred to as ‘workers’. Each worker holds a partition of the model and updates its parameters using its portion of the evenly distributed data. 

The key characteristic of synchronous training is that all workers operate in synchrony, which means that every worker must complete the training phase before any of them can proceed to the next operation or training step.

A real-world example of synchronous training architecture

Synchronous Training is relevant to scenarios or use cases where there is a need for even distribution of training data across compute resources, uniform computational capacity across all resources, and low latency communication between these independent resources. 

Advantages and disadvantages of synchronous training architecture

• The advantages of synchronous training are consistency, uniformity, improved accuracy and simplicity.
• All workers conclude their training phases before progressing to the next step, thereby retaining consistency across all units’ model parameters. 
• Compared to asynchronous methods, synchronous training often achieves superior results as workers’ synchronized and uniform operation reduces variance in parameter updates at each step.
• One major disadvantage is the longevity of the training phase within synchronous training. 
• Synchronous training may pose time efficiency issues as it requires the completion of tasks by all workers before proceeding to the next step. 
• This could introduce inefficiencies, especially in systems with heterogeneous computing resources.

Parameter server architecture

What is parameter server architecture?

The Parameter Server Architecture is designed to tackle distributed machine learning problems such as worker interdependencies, complexity in implementing strategies, consistency, and synchronization. 

This architecture operates on the principle of server-client relationships, where the client nodes, referred to as ‘workers’, are assigned specific tasks such as handling data, managing model partitions, and executing defined operations. 

On the other hand, the server node plays a central role in managing and aggregating the updated model parameters and is also responsible for communicating these updates to the client nodes.

A real-world example of parameter server architecture

In the context of distributed machine learning systems, the Parameter Server Architecture is used to facilitate efficient and coordinated learning. The server node in this architecture ensures consistency in the model’s parameters across the distributed system, making it a viable choice for handling large-scale machine-learning tasks that require careful management of model parameters across multiple nodes or workers.

Advantages and disadvantages of parameter server architecture

• The Parameter Server Architecture facilitates a high level of organization within machine learning pipelines and workflows, mainly due to servers’ and client nodes’ distinct, defined responsibilities. 
• This clear distinction simplifies the operation, streamlines problem-solving, and optimizes pipeline management. 
• Centralizing the upkeep and consistency of model parameters at the server node ensures the transmission of the most recent updates to all client nodes or workers, reinforcing the performance and trustworthiness of the model’s output.

However, this architectural approach has its drawbacks. 

• A significant downside is its vulnerability to a total system failure, stemming from its reliance on the server node. 
• Consequently, if the server node experiences any malfunction, it could potentially cripple the entire system, underscoring the inherent risk of single points of failure in this architecture.

Ring-AllReduce architecture

What is ring-allreduce architecture?

The Ring-AllReduce Architecture is a distributed machine learning training architecture leveraged in modern machine learning pipelines. It provides a method to manage the gradient computation and model parameter updates made through backpropagation in large complex machine learning models training on extensive datasets. Each worker node is provided with a copy of the complete model’s parameters and a subset of the training data in this architecture. 

The workers independently compute their gradients during backward propagation on their own partition of the training data. A ring-like structure is applied to ensure each worker on a device has a model with parameters that include the gradient updates made on all other independent workers. 

This is achieved by passing the sum of gradients from one worker to the next worker in the ring, which then adds its own computed gradient to the sum and passes it on to the following worker. This process is repeated until all the workers have the complete sum of the gradients aggregated from all workers in the ring.

A real-world example of ring-allreduce architecture

The Ring-AllReduce Architecture has proven instrumental in various real-world applications involving distributed machine learning training, particularly in scenarios requiring handling extensive datasets. For instance, leading tech companies like Facebook and Google successfully integrated this architecture into their machine learning pipelines.

Facebook’s AI Research (FAIR) team utilizes the Ring-AllReduce architecture for distributed deep learning, helping to enhance the training efficiency of their models and effectively handle extensive and complex datasets. Google also incorporates this architecture into its TensorFlow machine learning framework, thus enabling efficient multi-node training of deep learning models.

Advantages and disadvantages of ring-allreduce architecture

• The advantage of the Ring-AllReduce architecture is that it is an efficient strategy for managing distributed machine learning tasks, especially when dealing with large datasets. 
• It enables effective data parallelism by ensuring optimal utilization of computational resources. Each worker node holds a complete copy of the model and is responsible for training on its subset of the data. 
• Another advantage of Ring-AllReduce is that it allows for the aggregation of model parameter updates across multiple devices. While each worker trains on a subset of the data, it also benefits from gradient updates computed by other workers. 
• This approach accelerates the model training phase and enhances the scalability of the machine learning pipeline, allowing for an increase in the number of models as demand grows.

Conclusion

This article covered various aspects, including pipeline architecture, design considerations, standard practices in leading tech corporations, common patterns, and typical components of ML pipelines.

We also introduced tools, methodologies, and software essential for constructing and maintaining ML pipelines, alongside discussing best practices. We provided illustrated overviews of architecture and design patterns like Single Leader Architecture, Directed Acyclic Graphs, and the Foreach Pattern.

Additionally, we examined various distribution strategies offering unique solutions to distributed machine learning problems, including Data Parallelism, Model Parallelism, Federated Learning, Synchronous Training, and Parameter Server Architecture.

For ML practitioners who are focused on career longevity, it is crucial to recognize how an ML pipeline should function and how it can scale and adapt while maintaining a troubleshoot-friendly infrastructure. I hope this article brought you much-needed clarity around the same.

References

• Netflix Tech Blog: Meson Workflow Orchestration for Netflix Recommendations
• Netflix. (2022, January 18). Meson workflow orchestration for Netflix recommendations. [Online]. Retrieved from https://netflixtechblog.com/meson-workflow-orchestration-for-netflix-recommendations-fc932625c1d9 
• Facebook Engineering: fSDP: A Fast and Scalable Distributed Parallel Programming Framework
• Facebook. (2021, July 15). fSDP: A Fast and Scalable Distributed Parallel Programming Framework. [Online]. Retrieved from https://engineering.fb.com/2021/07/15/open-source/fsdp/