A Comprehensive Guide to Machine Learning Pipelines and Orchest (2026)

Machine learning (ML) has rapidly evolved from a niche research discipline to a core enabler of digital transformation across every industry. According to a 2023 IBM survey, 87% of organizations are now using or planning to use machine learning and AI in their business workflows. ML is being deployed for an incredible range of use cases, from predictive maintenance in manufacturing to fraud detection in financial services to drug discovery in life sciences.

However, while the potential impact of ML has never been greater, the process of actually building and deploying ML solutions into production remains extremely challenging. A 2022 study by Gartner found that over 50% of ML projects still fail to make it to production due to challenges with the end-to-end development lifecycle. Data scientists can build powerful models on their laptops, but ultimately fail to translate these into reliable, scalable, and maintained production systems.

This is where machine learning pipelines come in – and tools like Orchest that make building them dramatically easier. In this deep dive expert guide, we‘ll unpack what exactly ML pipelines are, why you absolutely need them to scale ML in the real world, and how Orchest can help you implement them. We‘ll walk through concrete examples, share development best practices, and give an opinionated perspective on the future of this exciting space.

Why Machine Learning Pipelines are Critical

Fundamentally, a machine learning pipeline refers to the end-to-end workflow for developing and deploying an ML model. This covers everything from the initial data collection and preparation to model building and tuning to deploying the model in production to continuously monitoring and updating it over time. The diagram below shows a typical ML pipeline:

Image Source: Author

Historically, data scientists have tended to focus primarily on the "fun" parts of this flow – analyzing data, building models, and experimenting in notebooks. But in the real world, these steps amount to only about 20% of the overall work required to deploy a model successfully. The other 80% goes into the less glamorous but utterly essential work of data engineering, deployment automation, monitoring, and lifecycle management.

Failing to treat ML holistically as a pipeline causes significant issues downstream:

• Reproducibility issues – Without a clearly versioned pipeline, it becomes extremely difficult to trace or recreate how exactly a model was developed, leading to inconsistencies.

• Collaboration friction – If each data scientist has their own ad hoc workflow, it‘s very hard for teams to effectively collaborate or share work.

• Slow iterations – When every model change or update requires tedious manual steps to operationalize, development velocity grinds to a crawl.

• Lack of visibility – With a disjointed process, stakeholders have no ability to understand the status of projects or the provenance of deployed models.

• Regulatory risk – Many industries have strict requirements around data/model lineage, bias and drift monitoring, etc. that a pipeline-less process can‘t satisfy.

The ML pioneers at tech giants like Google and Facebook realized early on that a robust and automated pipeline was essential to scaling ML across their organizations. Google‘s seminal paper on "Machine Learning: The High Interest Credit Card of Technical Debt" drove home how easy it was for haphazard ML deployments to cause massive maintenance headaches down the line.

In contrast, by investing in repeatable, monitored, and versioned ML pipelines, organizations can realize game-changing benefits:

• 10x experimentation – With a streamlined pipeline, data scientists can test ideas faster and try many more model variations. One Orchest user ramped from a few models per quarter to over 50!

• Higher deployment rate – At Capital One, adoption of ML pipelines drove model deployment rate from 20% to 85% while reducing deployment time from months to weeks.

• Easier compliance – Pipelines give a traceable record of every model‘s journey from training data to production, making audits and regulatory checks far smoother.

• Team productivity – Data from Gartner shows that organizations with collaborative, pipelined ML processes see 30-50% gains in data science efficiency.

Ultimately, in a world of soaring ML complexity and rising stakes for "getting it right", pipelines have gone from a nice-to-have to an absolute must for any serious data science endeavor. Teams that don‘t adopt them will simply be outpaced by ones that do.

Introducing Orchest

While the need for ML pipelines is clear, actually implementing them from scratch requires significant engineering time, resources and expertise that many data science teams lack. Re-inventing the wheel on pipelining slows teams down and distracts from the actual modeling work.

This is where Orchest comes in. Orchest is an open-source, web-based platform that dramatically simplifies the process of building ML pipelines. It provides an intuitive visual interface for defining pipeline steps and dependencies, handles the underlying compute and data management, and makes iterative development a breeze.

Some of Orchest‘s key differentiating features include:

• Notebooks as steps – In Orchest, every pipeline step is authored as a Jupyter notebook. This allows data scientists to work in a familiar, interactive environment while seamlessly plugging into a production pipeline. No more "throwing code over the wall"! Here‘s an example step:

# Load data from previous step
import orchest
data = orchest.get_inputs()["cleaned_data"]

# Do feature engineering 
features = engineer_features(data)

# Train model
model = DecisionTree()
model.fit(features)

# Log results
orchest.output(model, name="model")

• Data versioning – Orchest versions every intermediate data artifact in the pipeline, allowing you to trace the evolution of your models and easily roll back to previous versions if needed. This built-in data lineage is a game-changer for compliance.

• Polyglot – Pipeline steps can be written in any language, including Python, R, SQL, Scala, and more. Different steps can even use different languages seamlessly. This allows teams to leverage their existing skillsets and libraries.

• Caching – Orchest caches the output of every step by default. When iterating on a pipeline, only changed steps are re-run, massively speeding up development loops. No more waiting hours for models to retrain unnecessarily!

• Schedulers – Pipelines can be set to automatically run on a schedule (e.g. weekly) or be triggered programmatically via API/SDK. This allows models to stay fresh by retraining on the latest data.

• GitOps Integration – Orchest pipelines can be imported from and exported to git repositories. This allows pipelines to plug into CI/CD flows and be versioned like any other code artifact.

This feature set makes Orchest uniquely suited to the real-world needs of ML teams looking to scale model velocity without overloading their engineers or sacrificing reproducibility.

Example Orchest Pipeline

To make the power of Orchest more concrete, let‘s walk through a realistic example – building a pipeline to train and deploy a model for fraud detection on bank transaction data. The pipeline will have the following steps:

1. Ingest transaction data from a database and validate it
2. Join transaction data with customer data
3. Engineer features (rolling window aggregations, categorical encoding, etc.)
4. Train an XGBoost tree model to identify fraudulent transactions
5. Evaluate model performance on a held-out test set
6. Re-fit model on full data and persist model artifact
7. Deploy model as a REST API for real-time scoring

Here‘s a simplified view of what this might look like in the Orchest visual editor:

Image Source: Author

Each step would be its own notebook. For example, step 3 (feature engineering) could look something like:

import orchest
import pandas as pd

# Fetch joined data from previous step
raw_data = orchest.get_inputs()["joined_data"]

# Feature 1: 24-hour rolling average transaction amount 
raw_data[‘amt_rolling_24h‘] = raw_data.groupby(‘user_id‘)[‘amt‘].rolling(24).mean()

# Feature 2: One-hot encode merchant category
raw_data = pd.get_dummies(raw_data, columns=[‘merchant_category‘])

# Output to next step
engineered_data = raw_data[[‘amt_rolling_24h‘, ‘merchant_category_amazon‘, ...]]
orchest.output(engineered_data, name=‘model_inputs‘)

The outputs of each step (validation stats, feature importances, trained model object, etc.) would be versioned and cached by Orchest. Once the whole pipeline is built, the data scientist could run it end-to-end with a single button click to train and deploy the model.

More importantly, they could then iterate rapidly by tweaking the model or features and re-running, without having to manually track input data or model versions. Orchest would handle all that book-keeping automatically.

When new transaction data arrives the next day, Orchest could automatically trigger a re-run of the pipeline to re-train the model and update the deployed API. No manual intervention needed!

Getting Started

If the above sounds compelling, the good news is it‘s very easy to get started with Orchest. Simply install the open-source version on your local machine or spin up a managed Orchest instance in the cloud. From there you can walk through one of the many great tutorials and example galleries to get a feel for the platform.

When you‘re ready to implement your own pipeline, I recommend starting simple:

1. Sketch out the steps in your workflow and their dependencies
2. Create a new pipeline in Orchest and add a notebook for each step
3. Incrementally add the logic for each step, testing and running as you go
4. Plug the steps together by passing data between them
5. Add in monitoring, experiment tracking, and any other bells and whistles
6. Schedule the pipeline to run periodically or expose as an API

The key is to start with a small, end-to-end skeleton and flesh it out from there, rather than trying to build each step to perfection in isolation. Constant feedback and iteration is the name of the game.

Orchest in the MLOps Landscape

Of course, Orchest is not the only tool aimed at easing the creation of ML pipelines. Other notable players in the space include:

• Kubeflow – Kubernetes-native platform for building and deploying ML pipelines. Very powerful and scalable but has a steeper learning curve, especially for data scientists not familiar with Kubernetes.

• Airflow – General purpose workflow orchestration platform that can be used for ML pipelines. Highly flexible but requires more configuration and tooling integration compared to Orchest.

• Sagemaker Pipelines – AWS‘s managed service for ML pipelines. Offers good integration with other Sagemaker tools but limited to the AWS ecosystem.

• Azure ML – Microsoft‘s cloud ML platform which includes pipeline capabilities. Similar tradeoffs to Sagemaker.

Where Orchest shines is in its laser focus on the data scientist experience. By anchoring the pipeline development process in the familiar world of notebooks and providing a highly visual, interactive interface, Orchest meets data scientists where they are. At the same time, it still provides the robustness and reproducibility needed to scale ML in production.

This empowers data scientists to own more of the end-to-end ML lifecycle without having to become platform engineers overnight. One way to think about it: Orchest is to DevOps what Excel is to programming – a low-code, high-leverage way for non-specialists to accomplish powerful workflows.

Looking Ahead

As powerful as tools like Orchest are today, we‘re really just at the beginning of the journey to scalable, production-grade ML pipelines becoming the norm. Over the next few years, we can expect to see major strides in several areas:

• Automated pipeline generation – Advances in AutoML technology will allow more of the pipeline development process itself to be automated based on the nature of the data and problem. Less manual stitching together of steps.

• Real-time pipelines – As the demands for real-time ML grow, we‘ll see streaming pipelines (e.g. using Kafka, Spark, etc.) become a native part of tools like Orchest. This will enable continuously updated models.

• Adaptive pipelines – Pipelines won‘t just be static DAGs but will reactively re-configure themselves based on data and model drift. For example, re-triggering data prep and training on relevant slices of data as needed.

• Pipeline monitoring – Just as application performance management (APM) is critical for software, pipeline monitoring will become central to ML operations. Spotting bottlenecks, faults, and performance issues in the pipeline will be a dedicated discipline.

• Pipeline as a product – For many companies and use cases, the pipeline itself and its attendant monitoring, compliance, and orchestration features will become the actual ML product they deliver rather than one-off models.

As an industry, we‘re on a rapid trajectory from the wild west of custom, artisanal ML to a world of standardized, reliable, and highly automated ML pipelines. But this transition won‘t happen automatically – it will require organizations to invest in upskilling their data scientists on MLOps technologies, and building strong collaboration between data and platform teams.

At an individual level, data scientists who embrace pipelining tools like Orchest and level up their skills accordingly will be extremely well positioned for the future. Just as software engineers who learned DevOps early on became indispensable, "full-stack" data scientists fluent in end-to-end pipelines will be the new kingmakers.

Conclusion

ML pipelines are the key to organizations realizing the full transformative potential of machine learning and AI. By providing a reproducible, scalable, and collaborative framework for the end-to-end model development lifecycle, pipelines enable data scientists to move beyond the experimentation phase and deliver real production impact.

As we‘ve seen, Orchest is a powerful and intuitive platform for building these pipelines that meets data scientists where they are while still enforcing engineering best practices. By adopting tools like Orchest and following the best practices outlined in this guide, data science teams can drastically accelerate their model velocity and time-to-value.

Of course, both the technology and practice of ML pipelines will continue to evolve rapidly in the coming years, with major breakthroughs ahead. The organizations and individuals that stay on the cutting edge of this space will be the ones that define the future of applied ML.

The most important thing is simply to get started – identify a use case, carve out a minimally viable pipeline for it, and iterate relentlessly. Every pipeline is a foundation to build on.

Embracing pipelines is not just about adopting a new tool – it‘s about fundamentally leveling up the discipline of data science to adapt with software engineering rigor, while still preserving the magic of human-in-the-loop model development. It‘s a challenging but incredibly exciting road ahead for practitioners.

So fire up Orchest, pipeline that first model, and get building! The future is bright for data science – one pipeline at a time.