A full guide to building your Machine Learning pipeline
In 1959, Arthur Samuel introduced the term "machine learning" with a relation to "a computer's ability to learn without being explicitly programmed," and since then, there has been some substantial change in how we perceive this concept.
Nowadays, sources like McKinsey define at least 400 use cases for machine- and deep learning. 92% of businesses are actively investing in their AI efforts, but here’s where the story twists: only 1% of them think the technology is mature enough. This issue underlines one truth: structured workflows are as vital for ML pipelines to succeed as your team’s technical proficiency. After all, experimentation is good, but it takes a well-defined approach to receive a substantial return on your investment.
In this guide, we will describe the concept of a machine learning pipeline in great detail, define the steps and typical challenges of building it, and find the best tools for this complex job.
What is ML pipeline?
A machine learning pipeline refers to a set of linked steps that are intended to automate the process of creating and applying machine learning models to specific workflows. Through automated, coordinated stages, it makes creating and optimizing, and assessing the efficiency of machine learning workflows (Posoldova, 2020).
The main goal of ML pipelines is to solve a significant issue: a large number of ML projects fail, and many proofs of concept never make it to production. This becomes evident when you check this well-known GitHub repository alone. For instance, the voice ordering system at McDonald's Drive-Thru AI frequently misinterprets customer requests, and Tesla’s visual aid confused a horse-drawn carriage with a walking person. Sounds ridiculous, but even if such giant companies mistook their steps to create an ML model, smaller teams are at even greater risk.
The ML community's historical emphasis on developing models rather than production-ready systems is frequently the cause of this failure, which leaves data scientists to manually manage workflows and poses operational difficulties in real-world scenarios. Here, structured ML pipelines are the best way existing so far.
Key components of an end-to-end ML pipeline
Three essential steps make up an end-to-end machine learning pipeline, according to IBM’s direction documents:
Assembling and preparing data through the phases of collection, preprocessing, cleaning, and exploration is known as data processing.
The process of developing a model involves choosing suitable algorithms, training them on data that has been processed, and verifying the outcomes until they are ready for production.
With continuous performance monitoring, model deployment incorporates the verified model into production settings.
How does it happen in a real ML environment? Let’s take a look at this ML pipeline diagram:
The training and the following inference phases are depicted in the ML pipeline architecture above, which shows the direction of data movement from initial datasets through different processing steps, model training, and ultimately to real-time prediction services.
Raw input data (D) is filtered and preprocessed (C) for model training (F). Final decisions are delivered online using real-time data streams (S) and offline with the use of datasets that have previously been augmented via detection systems during the inference phase.
There’s one more example that shows it well. By using object-oriented architecture employed in order to isolate user-friendly frontends from intricate backend operations, EndToEndML, created by Pillai et al. (2024), is an example of such a comprehensive pipeline implementation. The four main classes used in their system showcase how designing your system with modular components lets users take advantage of sophisticated ML capabilities.
Machine Learning pipeline in production
For deployment to be successful, it is essential to comprehend the differences between experimental and production machine learning environments. Oracle'sstudy on production machine learning transitions identifies key distinctions in a number of operational areas:
Managing data is one of the most evident aspects of this distinction:
Experimentation. Researchers use pre-processed, static datasets that hardly ever go over memory limits. For instance, consider a rough example of creating a diagnostic algorithm by examining 5,000 medical photos kept in a single folder.
Production. Systems must validate and transform dynamic data streams in real time as they are continuously ingested. A good example of this is the process of managing corrupted files, different formats, and urgent priority cases while processing 50,000 medical scans per day from hospital networks.
Performance criteria for experimentation and production are also widely different:
Experimentation. Accuracy investigation and hypothesis testing take precedence over speed optimization. For instance, an experiment could present training a recommendation engine for three hours to increase its precision by 2% on a test dataset.
Production. Strict latency requirements and sub-second reaction times become required. An example of a production machine learning pipeline could deliver product recommendations in less than 100 milliseconds to stop customers from leaving the checkout page.
Demands for the infrastructure throw in some additional differences:
Experimentation. Single workstation settings with manual resource allocation and minimal computational requirements are typically the ones used for experimentation. For instance, a researcher might consider using a laptop with 32GB of RAM to run climate prediction models, like this conversation suggests.
Production. Clusters of distributed computers with load balancing, geographic redundancy, and automatic scaling. As an illustration, consider weather forecasting systems that automatically spin up extra servers during storm seasons and operate across several data centers.
The scope of monitoring adds another layer of understanding in what way experimentation and production pipelines are different:
Experimentation. During the stages of developing a model, basic accuracy metrics are usually periodically monitored. Such an ML pipeline example would be this - while creating a spam detection classifier, weekly checks of confusion matrices are made.
Production. For production machine learning pipelines, a 360-degree observability is needed, encompassing regulatory compliance, data drift detection, system health, and business metrics. As an example to give, credit scoring systems tend to continuously check millions of loan applications for business impact, compliance with international standards, prediction accuracy, and demographic bias.
Tolerance for failure.All the failed cases in the repository have been production ones - this perfectly shows the dramatic nature of this differentiation point:
Experimentation. When algorithms run into unexpected situations, manual intervention is acceptable, for example, when sentiment analysis fails on social media posts with a lot of emojis, a researcher manually restarts the analysis.
Production. Complete fallback mechanisms and graceful degradation are necessary due to the zero-tolerance policy for unhandled exceptions - it would be evident in a situation when real-time competitor data is unavailable, and e-commerce pricing algorithms have to automatically fall back on historical averages (this process is called pricing collusion in this article).
Now that we can see the difference in practice, let’s figure out what efforts and resources it takes to deploy an AI data pipeline for an ML model.
Requirements for deploying ML models at scale
Even with well-known frameworks such as CRISP-DM that define deployment phases, ML industrialization still faces numerous obstacles and consistently low success rates (Heymann et al., 2022). Scalability, system integration, and ongoing monitoring must be given top priority to successfully implement large-scale ML pipelines, apart from some other important requirements:
Management and quality of data.
In production settings, the accuracy and dependability of ML models are directly influenced by robust data quality. Standardized data collection, cleaning, and validation processes are necessary for manufacturing applications in order to guarantee the best possible AI performance results. Prior to the start of model training, early exploratory analysis assists in identifying data problems (Lones, 2021).
Testing and validation of models.
Thorough validation guarantees that models continue to function when they move from development to real-world production settings. By evaluating the model's efficacy in actual settings, deployment acts as a crucial validation (Heymann et al., 2022). Testing procedures must be carefully considered in order to prevent data leakage during validation (according to Kapoor & Narayanan).
Accountability and transparency.
Especially in the cases where accuracy is an absolute must, AI systems need transparent decision-making procedures and accountability frameworks. To preserve operational integrity, processes of manufacturing should stick to impartial, equitable ML model architecture. Interpretable models are especially useful in high-stakes decision contexts (Rudin; 2021).
Constant inspection and upkeep
For deployed models to identify performance deterioration and evolving data trends over time, continuous monitoring is required. Effective management of small datasets and system behavior monitoring is made possible by cross-validation techniques (Lones, 2021). System updates and frequent model retraining must be factored into resource planning.
Preventing bias and reducing risk.
Bias is a major problem for manufacturing applications, especially when it comes to demand forecasting and inventory management. It is possible for models to pick up false correlations that work well in training but not in real-world situations. To guarantee dependable performance, class imbalance problems need to be handled with specific methods (by Haixiang et al.).
Workflow optimization and integration.
Coordinated knowledge across several domains is necessary for successful deployment in order to guarantee efficient, transparent AI processes. Production reliability is increased through improved data management and modeling techniques that take uncertainty into account (Heymann et al.). Simplified model comparison and deployment procedures are made possible by contemporary machine learning frameworks. This demand is even stricter for deploying specialized AI models for vertical industry-specific data that require tuned algorithms to comply with definite standards.
These requirements are demanded by the nature of machine learning architecture. To find out more about it, let’s now understand the principles of automating and monitoring ML models.
Considering automation, monitoring, and scalability when building ML pipelines
By using automated procedures and standardized workflows, the contemporary MLOps approach offers a thorough framework for managing the entire machine learning lifecycle. The ML pipeline is made up of five interrelated steps that smoothly transition from experimentation to production deployment, as shown in the logical machine learning architecture diagram suggested by Microsoft Learning. By using a methodical approach, conventional manual machine learning procedures are converted into automated, repeatable workflows that maximize operational efficiency and scalability while minimizing human error.
The deployment phase manages model transitions from development to production environments by combining synchronous and asynchronous MLOps patterns with release pipelines. There are several approaches to automation defined by Aliev & Baimuratov:
The TPOT (Tree-based pipeline optimization tool) solution is a scikit-learn wrapper that treats pipeline operators as GP primitives and uses evolutionary computing techniques to construct decision trees. It uses genetic programming for automated machine learning pipeline optimization.
The ML-Plan System uses heuristic best-first search algorithms to implement Hierarchical Task Networks (HTN), offering specific overfitting prevention mechanisms and using random algorithm substitution to find the best pipelines.
Using deep reinforcement learning, neural networks for performance prediction, and Monte-Carlo Tree Search, the AlphaD3M Framework reduces pipeline synthesis to single-player game mechanics, cutting runtime from hours to minutes.
The SemFE Solution eliminates the knowledge asymmetry between domain experts and machine learning practitioners by addressing issues with transparency, data preparation, and model generalizability through ontology-based feature engineering.
Teams can retrain or update models before performance impacts become crucial to business operations, thanks to the monitoring infrastructure's proactive identification of model degradation. A closed-loop system for model improvement is created by this continuous monitoring technique, which records real-world behavior and feeds performance data back into the ML data pipeline development cycle. Here are the MLTRACE components of the observability framework for monitoring (according to Shankar & Parameswaran, 2021):
Logging systems. Record component inputs, outputs, and metadata in real time; facilitate silent failure detection by monitoring intermediate components; and integrate with minimal user input.
Monitoring metrics. Use plug-and-play metric libraries with automated alert systems to track ML-specific performance indicators such as F1 scores, t-test statistics, and real-time model performance without the need for labeled data.
Query functionalities. Enable component run-level, component history, cross-component, and cross-component history debugging patterns by supporting slice-based lineage tracking across heterogeneous tool stacks. Abstractions of Components: Give pipeline stages declarative interfaces with beforeRun and afterRun trigger methods, automatic dependency resolution based on input values, and freshness indicators to detect staleness.
Storage architecture. Provides organizational database access capabilities, ComponentRun logs for execution history, IOPointers for artifact management, and metrics tracking across successive component executions.
Infrastructure as code (IaC) techniques and cloud-based orchestration tools are combined by MLOps frameworks to achieve scalability of the ML pipeline architecture. The Model Factory method supports both batch and online model endpoints and optimizes operating costs by using Bicep or Terraform for automated infrastructure provisioning. Capabilities for Infrastructure Scaling (Nalam et al., 2025):
Data collection systems. Implement client-side buffering for 500 events and handle 85,000–100,000 events per second during peak loads with 99.95% accuracy across distributed nodes.
Event processing infrastructure. Maintain sub-150 ms latencies while supporting a continuous throughput of 80,000 events per second per node with horizontal scaling up to 32 production nodes.
Multi-tiered storage. Use hot/warm/cold storage designs to keep access latencies under 75 ms and cut costs by 35–40%.
The results achieved by following these methods are different as well. While hybrid architectures demonstrate scalability trade-offs by delivering 75 ms response times with 40 GB memory usage, basic machine learning pipelines achieve 150 ms response times with 16 GB memory usage.
Steps to build a Machine Learning pipeline
A Deloitte study found that 67% of companies are already using machine learning for some processes. But there is a great difference between using and succeeding, and to succeed, you should follow a structured process.
Step 1. Establishing goals and limits.
Every machine learning pipeline project starts with defined goals and responsible boundaries long before you start applying any code to your project. According to Shankar from Stanford University, even the first stages of developing your pipeline start with establishing observability:
Goals and anticipated results. Specify the precise issue you're trying to resolve and how success will be determined. For example, an insurance company may use automated document classification to cut claim approval times by 25%.
Aligning stakeholders. To guarantee that priorities are in line, involve compliance officers, technical teams, and business leaders as soon as possible.
Risk and governance assessment. Consider possible negative effects, like skewed forecasts or legal exposure. One well-known example of how historical bias in training data can produce discriminatory outcomes is the now-defunct hiring algorithm used by Amazon (Langenkamp et al., 2020).
Regulatory requirements. GDPR (GDPR Local) mandates that processing personal data from EU citizens be done with their express consent or another legal basis, that data use be transparent, and that the principles of data minimization and purpose limitation be followed.
Ignoring basic data protection requirements during the initial planning for the ML pipelines can have disastrous legal and financial repercussions that threaten the entire viability of an AI system. The Clearview AI fine serves as an example of why it is imperative to establish appropriate regulatory compliance and data governance frameworks from the beginning of any ML project. Their failure to obtain consent and provide transparency resulted in a €30.5 million penalty and ongoing legal battles across multiple jurisdictions.
Step 2. Gathering, examining, and preparing data
After establishing the goals and data limitations, the focus is distributed to the quality of data. In fact, most respondents of Deloitte’s survey said that model development, data transformation, and model management and monitoring are the aspects of AI that need the greatest work. Here are the essential steps to work with the data:
Gathering and choosing. Collect pertinent datasets from reliable sources, such as past consumer transactions, IoT sensor results, or demographic information from third parties, depending on the source you have and the use case you work on.
Validation and exploration. Examine the data to be fed to your AI pipeline for timeliness, completeness, and problem-related relevance.
Cleaning and transformation. Eliminate erroneous data, standardize formats (e.g., convert all temperatures to Celsius), and label information as necessary.
Feature engineering. Determine or produce characteristics that are most pertinent to forecasts, such as concentrating on a retail recommender's past six months' worth of purchases, if you are creating a product suggestion engine pipeline.
Bias and compliance checks. Inadequately balanced datasets can yield discriminatory results, as demonstrated in Hiring Fairly in the Age of Algorithms. A compliance component is added by GDPR, which prohibits repurposing without consent and only requires the collection of personal data that is necessary.
The orange "Data Preparation" section in Barrak's machine learning pipeline diagram, which includes the "Data Processing & Wrangling" and "Feature Extraction & Engineering" components, is exactly what this data preparation phase is. The "Re-iterate till satisfactory model performance" feedback loop, which guarantees the quality of your data before you begin any training efforts, shows its iterative nature.
Step 3. Design, training, and assessment of the model
After the data is ready, attention turns to the model, which is the pipeline's central component. Here is the optimal structured workflow for data training and model assessment:
Model selection. Use pre-built models from open-source libraries or vendors, or create your own architecture. This method has a big influence on LLM integration, where your team can use vendor-pretrained models like GPT or BERT, optimize open-source substitutes like Llama, or create unique transformer ML pipeline architectures suited to particular domain needs.
Model validation. Before training with production data, perform an initial validation on a small, standardized dataset to make sure the model's fundamental logic functions.
Tuning and training your model. Provide the model with the prepared training set so that it can "learn" patterns from labeled examples. Then, modify the hyperparameters to maximize performance.
Evaluation. Assess the model's capacity for generalization by testing it against unknown data and monitoring metrics like precision, recall, and F1-score.
Governance alignment. Make sure the model's choices can be clearly explained in regulated contexts to meet the accountability requirements of the GDPR and internal governance.
This diagram shows a basic machine learning process from beginning to end. Step 1 starts with an Untrained Model that is given a Training Set made from unprocessed data. The untrained model is transformed into a trained model (Step 3) through Model Training (Step 2). After training, the model creates a prediction by analyzing new data. This machine learning pipeline example represents the fundamental learning loop: data → training → model → inference.
Step 4. Integration and model deployment
At this stage, the model moves from development to the production environment, where it can be used to make predictions in the real world, following successful training and evaluation. Here are the best practices for establishing a secure machine learning deployment pipeline:
Setting up the infrastructure. Depending on latency needs and data sensitivity, deploy the trained model to an appropriate production environment, such as on-premises servers, cloud-based platforms (AWS SageMaker, Google Cloud AI Platform), or edge devices (hardware that controls data flow at the boundary between two networks, as stated by TechTarget.
Development of APIs. To enable smooth integration with current business applications, such as customer relationship management systems or automated decision-making workflows, create RESTful APIs or microservices that let other systems communicate with your model. Aim for consistent performance during periods of high usage without over-provisioning resources during periods of low usage by implementing load balancing and auto-scaling mechanisms to handle fluctuating prediction volumes.
Implementing security. Set up authorization, encryption, and authentication procedures to safeguard model endpoints and sensitive data transfers. This is especially important when managing personal data in accordance with GDPR regulations.
Rollback and version control features. Keep up model versioning systems that enable fast reverts to earlier stable versions in the event that production problems or performance deteriorate.
This step typically entails orchestration with Kubernetes and containerization with Docker, as mentioned in Rehmanabdul's Medium example of machine learning pipeline architecture. Monitoring dashboards are also implemented to track model performance metrics in real-time production environments (like the one suggested in Jeremy Jordan’s article).
Step 5. Monitoring, maintenance, and governance
Following deployment, ongoing supervision guarantees that the model upholds performance and compliance requirements for the duration of its operational lifecycle. Let us suggest a workflow for maintaining a stable model performance:
Performance monitoring. Create automated systems to monitor important parameters of your machine learning pipeline functioning, such as response times, prediction accuracy, and system resource usage. You can also set up alerts to notify you when performance falls below acceptable limits.
Data drift detection. Track incoming data distributions to spot instances where training data and new data diverge substantially, as this may suggest that the model needs to be recalibrated or retrained. For instance, when user query patterns change from training data (slang, new technical terminology), AI large language models must be updated to preserve response relevance and accuracy.
Pipelines for retraining models. Establish automated processes that can retrain models on new data at predetermined intervals or when performance deterioration is noticed, preserving prediction quality over time.
Explainability and audit trails. To comply with regulatory requirements and business transparency needs, keep thorough logs of model decisions and use explainable AI techniques. This is especially crucial for GDPR's "right to explanation."
Integration of the feedback loop. Provide systems for gathering user input and ground truth results in order to continuously evaluate the performance of the real-world model and pinpoint areas in need of development.
To ensure consistent quality standards, this step entails developing systematic evaluation frameworks that automatically assess model performance across various use cases, much like in the research by Yakunin et al. (2020). Results of binary classification (possibly positive or negative topics or sentiments) contain the typical metrics for classification, each class's sample count (8,133 for Class -1, 33,924 for Class 1), and the precision definition (80% as the overall model accuracy), and sample-weighted averages.
Given that Class 1 (positive sentiment) was significantly more common than Class -1, the table indicates that their topic modeling approach performed well on a dataset with unequal classes.
Model preparation and training process
A complete framework for creating end-to-end machine learning pipelines in cloud environments is offered by Microsoft Azure Machine Learning. Best practices for developing reproducible, scalable machine learning systems are illustrated in their official documentation.
Using the Azure ML pipeline workflow, here is what Microsoft recommends to execute:
Configuring the environment. Create the Azure ML workspace and set up the CPU/GPU clusters. Using conda specification files, define Python environments with the necessary dependencies.
Building a data pipeline. Use OutputFileDatasetConfig to create automated pipelines that transform raw data into datasets that are ready for training. Make datasets that are registered and versioned so they can be shared and used again in different experiments.
Implementation of preprocessing. Write modular Python scripts for feature engineering and data transformation as distinct pipeline stages. When inputs or source code haven't changed, use allow_reuse=True to cache steps.
Training environment specification. Use Environment classes with precise package versions to define runtime environments. This guarantees reproducibility among team members and various computing environments.
Model training configuration. To monitor metrics and model artifacts, put in place training scripts that interface with Azure ML's logging systems. Outputs are automatically captured and versioned, and they are saved to specified directories connected to your AI pipeline.
Pipeline execution. Construct a single Pipeline object that automatically handles dependencies by combining all of the steps. Azure ML maximizes parallelization opportunities while calculating execution order.
A thorough end-to-end machine learning pipeline using Scikit-Learn that goes beyond simple model training is demonstrated in Rehmanabdul's Medium article. In order to handle numerical and categorical features independently, he developed complex preprocessing using ColumnTransformer.
His method connected model training and preprocessing steps into a single, cohesive sequence using Scikit-Learn's Pipeline class, enabling the workflow to be completed with a single.fit() call. used GridSearchCV to test various C values and solvers for logistic regression (PDF) and adjust hyperparameters. The trained pipeline was saved as 'trained_pipeline.pkl' for deployment - the next step that we will discuss.
Model deployment and retraining pipeline
Research shows that concept drift and shifting data distributions cause 91% of ML pipeline architectures and models to deteriorate over time. As illustrated by Julian Bright, Alessandro Cerè, Georgios Schinas, and Theiss Heilker in their AWS blog post "Automate model retraining with Amazon SageMaker Pipelines when drift is detected," AWS tackles this issue by integrating Amazon SageMaker with monitoring and event-driven architecture through automated retraining pipelines.
Pipeline infrastructure setup. Use CloudFormation templates to create SageMaker Studio projects that provision the whole MLOps infrastructure. The system incorporates EventBridge for event-driven triggers throughout the model lifecycle, CodeBuild for execution, and AWS CodePipeline for CI/CD.
Automated model registration and approval. Put in place a two-phase approval procedure where data scientists authorize models for staging deployment in the SageMaker Model Registry. Then, in accordance with various roles and governance needs, operations teams grant secondary approval for production deployment.
Multi-environment deployment. Apply authorized models to staging endpoints for validation before moving them to production with improved settings like multi-AZ deployment and auto-scaling. For monitoring purposes, data capture functionality is enabled in production deployments to log all requests and responses.
Constant model monitoring. Set up SageMaker Model Monitor to run hourly on a predetermined schedule and compare production data with baseline statistics created during training. By examining distribution changes in input features and prediction outputs, the system can identify concept drift.
Automated drift alerting and detection. Configure CloudWatch alarms with thresholds unique to your model to sound when monitoring metrics surpass permissible drift limits. EventBridge rules automatically start new training pipeline runs to retrain models with new data when alarms are triggered.
Feedback loop integration. Use Amazon A2I to create ongoing data collection methods that use explicit ratings, implicit user feedback, or human-in-the-loop workflows to record ground truth labels. This guarantees that retraining datasets are up to date and accurately reflect patterns of behavior in the real world.
Such a workflow allows for a scalable and secure deployment of your machine learning data pipeline. As you noticed, there are numerous resources used in this workflow. This leads us to give you some additional information on the available frameworks and tools to perform an end-to-end ML pipeline development.
Tools for building Machine Learning pipelines
Let’s shortly list some of the more widely used ML pipeline tools that you can include in your machine learning pipeline creation process.
Airflow from Apache employs Directed Acyclic Graphs (DAGs) in Python-based configuration for retry logic and task-level control. The tool lacks native model serving capabilities but integrates with Spark, Hive, Docker, and Kubernetes. It’s ideal for complex workflow management with close monitoring and custom orchestration.
MLflow supports REST-based model serving and all of the main machine learning libraries, such as scikit-learn, TensorFlow, and PyTorch. For production deployments, cloud-native design makes it simple to integrate with AWS, Azure, and GCP. The best use for it would be flexible deployment, experiment tracking, and full ML pipeline lifecycle management.
Using Python-based workflows with CI/CD support, ZenML features a plugin architecture that links Airflow, MLflow, and Kubernetes. It tracks artifacts and metadata while removing the complexity of the infrastructure to create pipelines that are easy to maintain. It’s best to use for MLOps-first teams that value modular pipeline design and reproducibility.
Complete pipeline components for data transformation, validation, training, and evaluation with TensorFlow Serving are included in TensorFlow Extended (TFX), which uses Kubeflow or Apache Beam for orchestration, taking advantage of Google's speed and stability optimization. A perfect use case for it is production-grade validation pipelines with an end-to-end TensorFlow focus.
Kubeflow is natively integrated with Kubernetes due to its deep connection with Katib for hyperparameter tuning that supports TensorFlow, PyTorch, and XGBoost. The solution uses KServe for model deployment and container orchestration for distributed scaling.
It’s one of the best machine learning pipeline tools for containerized workflows needed for Kubernetes-native deployments.
Metaflow has a Python-native syntax, one-line AWS integrations for S3, and integrated data versioning. However, it lacks native model serving but abstracts infrastructure complexity with built-in DAG support and retry logic. A perfect application for it would be massive-scale machine learning pipelines that help data scientists advance quickly.
With such a great variety of applications, frameworks, and tools for ML pipeline development, your team might face significant challenges in choosing, integrating, and managing all these scattered sources, and risk data drift or faulty workflows. With the artificial intelligence software solutions developed by COAX, your risk is close to null: we start with a deep dive into your data governance needs and nuances of your operations, and build workflows based on the most suitable ML architecture, using the latest, most stable tools, and set transparent monitoring and versioning processes to ensure your models function exactly as they should.
Cloud-based vs. on-premise ML pipeline tools: What’s the difference?
We paid attention to stating the deployment type of the listed tools and frameworks for building machine learning architecture. To avoid misconceptions and confusion, let’s detail the difference between cloud and on-premise options and their aspects of differences. The following are the main points of comparison between on-premises and cloud-based machine learning deployment strategies, based on the Medium article from LEARNMYCOURSE:
Scalability. Physical cluster capacity limits on-premise Kubeflow deployments, and manual node additions are necessary for scaling. Elastic scaling and automatic provisioning are features of cloud-based solutions (managed MLflow on AWS or Azure). For instance, on-premise Kubeflow necessitates pre-configured cluster resources, whereas cloud-hosted TFX can benefit from Google's infrastructure optimization.
The cost structurebreakdown. High upfront capital expenses are required for on-premise deployment using self-hosted MLflow and Apache Airflow. Pay-as-you-go models are used by cloud-based platforms that run Metaflow on AWS, while on-premise Apache Airflow has fixed infrastructure costs independent of ML pipeline activity, and cloud MLflow deployments are charged by the compute hour.
Control and security. Complete control over data privacy is possible with on-premise deployments that use Kubeflow and self-hosted MLflow, since data never leaves organizational infrastructure. Cloud-based solutions that rely on shared responsibility models and provider security measures include managed TFX and Metaflow on AWS.
Overhead for maintenance. On-premise (MLflow servers, Kubeflow clusters, and Apache Airflow infrastructure) updates must be manually managed. With provider-maintained systems, infrastructure management is handled by cloud-based services for Metaflow and TFX. For instance, on-premise Apache Airflow necessitates manual system updates, whereas cloud-hosted MLflow eliminates server maintenance.
Integration nuances. Because of their plugin architectures, on-premise tools (Apache Airflow and ZenML) provide flexibility in integrating with current internal systems. Cloud platforms offer smooth ecosystem integration; for example, TFX integrates with Google Cloud infrastructure, while Metaflow naturally connects with AWS services.
Timing of deployment. Infrastructure must be set up and configured for on-premise deployment, but meanwhile, cloud-based solutions allow for instant deployment. For example, on-premise Kubeflow necessitates container preparation beforehand, while cloud MLflow deploys models to REST endpoints in an instant.
We consider each of these aspects during our cloud development services at COAX. For instance, we develop hybrid solutions that use AWS SageMaker for scalable model training and on-premise MLflow for sensitive data. By using spot instances for batch jobs and automatically reducing non-production resources during off-peak hours, we also minimize expenses. Our approach is simple: we find the closest shortcut to achieve the most efficient machine learning pipeline, and follow each step methodically to consider each nuance of development.
Challenges in updating and managing ML models
After initial deployment, machine learning models encounter operational challenges that call for continuous management techniques to preserve performance. Researchers from a variety of industries have divided them into organizational, governance, and technical domains. Let’s review the most common ones and solutions to overcome them.
Concept evolution and data drift
A major issue during ML model deployment is concept drift, which refers to modifications in the distribution of input data or patterns of target variables, and can impact even the most sophisticated machine learning platform architecture. Over time, concept drift can significantly impair model performance, as described by Data Science Wizards on Medium. Despite the availability of technical solutions, Baier et al. (2019) discovered that practitioners usually rely on manual checks and adjustments rather than automated drift detection systems.
There are two ways that organizations can address this issue, according to this research:
Using automated monitoring pipelines that continuously compare production data to baseline distributions.
Setting pre-established thresholds that initiate model retraining workflows when significant drift is detected.
Even if data drift is prevented, your team might face an issue of model versioning.
Rollback management and model versioning
Without versioning models correctly, debugging and recovery become very challenging and time-consuming because teams are unable to replicate particular training runs or rollback to stable versions when deployed models fail. Data scientists are unable to monitor the progress of experiments in chaotic collaboration environments caused by this lack of version control.
The importance of thorough documentation of all model versions (including training data sources, performance conditions, and simple rollback capabilities to earlier stable versions) is evident here. According to research from the European Conference on Information Systems, serving infrastructure that can manage several concurrent versions is necessary for effective model management.
Implementing ML pipeline tools with integrated version control systems, automated model registry services, and blue-green deployment techniques that allow for instant rollback are some solutions to eliminate this challenge.
Concerns about bias and transparency
Assumptions and biases ingrained by the people and organizations that created the models pose serious risks to the integration of AI pipelines, which can impact results in a variety of sectors (Lattimore et al. 2020; Khoei and Kaabouch 2023). With deep neural networks, technical transparency becomes especially difficult because practitioners frequently find it difficult to communicate model decisions to stakeholders, which lowers adoption and trust.
These issues can be lessened by using explainable AI frameworks, regular fairness metrics tracking, diverse review committees, and customer-specific metrics that convert technical performance into results that are relevant to business.
Integration and scalability of infrastructure
As AI automation grows, historical human control over data collection and processing is eroding, posing privacy concerns regarding its use (Baier et al., 2023). Data scientists must adjust solutions to different customer environments while navigating approval procedures for new infrastructure investments and standardization gaps in local solutions.
The solutions include implementing hybrid cloud architectures that strike a balance between security and scalability, adopting containerized deployment strategies using Docker and Kubernetes, standardizing MLOps templates, and developing automated data pipeline management systems that can effectively handle database and model lifecycle requirements.
With 15 years of experience, COAX uses multiple project-proven methodologies to build an efficient and error-proofed machine learning pipeline that eliminates expensive trial-and-error for your business. If you want to avoid such common mistakes and potential risks, we are the team for you to turn to.
FAQ
What is pipeline in machine learning?
Samaan and Jeiad (2025) define an ML pipeline as a sequence of interrelated steps that automate the creation and implementation of ML models. Through successive stages, which are classified as estimators or transformers, where each stage's output becomes the subsequent stage's input, it simplifies designing, fine-tuning, and performance evaluation.
What is a machine learning pipeline on a state-of-the-art level?
Advanced automation with sequential transformations is a feature of modern machine learning pipelines. These transformations include variance-based feature selection, vector assemblers for feature combination, imputers for missing values, standard scalers for normalization, and support for multiple algorithms (Decision Tree, Random Forest, and Logistic Regression) with cross-validation and hyperparameter tuning capabilities (Kansab, 2025).
Is a deep learning pipeline the same as a machine learning pipeline?
The difference between ML pipelines and deep learning pipelines is that the latter are specialized ML pipelines as just one of the types of a broader phenomenon. In contrast to conventional cloud-only methods, Ali, Yaseen, and Anjum (2018) show this by using edge-enhanced systems that distribute deep learning across edge/fog/cloud resources for video analytics, achieving 71% efficiency gains.
What are the examples of ML pipeline architecture patterns?
There are various patterns to follow. As stated by a well-known public Github repository, key patterns include:
Following these patterns enables you to create extensible, scalable ML systems.
How do you ensure a stable and scalable machine learning architecture at COAX?
We use automated pipelines with integrated preprocessing, data validation, and error handling steps to guarantee stability. Distributed processing frameworks, real-time streaming, cross-validation techniques, and modular design patterns that COAX engineering teams use also facilitate smooth model switching and deployment monitoring are used to achieve scalability.
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