Building a Scalable MLOps Platform from Scratch on OpenMetal

For many companies, Machine Learning Operations (MLOps) is at a bit of a crossroads. Everyone’s excited about what AI can do for business, but getting there often means hitting operational roadblocks. Teams struggle with model training that takes forever, slowing down how fast they can try new things. Getting models into production can turn into a complicated mess, causing slowdowns and wasted effort. The infrastructure often feels like a jumble of services instead of a solid, scalable setup. And on top of it all, there’s the worry about cloud costs shooting up unexpectedly, which can kill new ideas. These aren’t just theories, they’re what Machine Learning Engineers, Data Scientists, MLOps pros, and CTOs deal with every day.

As companies try to get better at MLOps, a question arises: is it better to go with an off-the-shelf MLOps solution for convenience, or to take charge and build a platform that’s perfectly designed around what they need now and in the future? Pre-built tools might look like a quick fix, but they often mean giving up flexibility + control, and can cost more in the long run.

This post will argue for building your own. We’ll look at why putting together an MLOps platform “from scratch,” especially on a solid and clear infrastructure like OpenMetal, can turn these common headaches into real, long-lasting benefits. It’s a smart move for any company wanting to get the most out of their machine learning work.

Why MLOps is a Big Deal Now and Why “From Scratch” Makes Sense

Deciding whether to build your own MLOps platform or buy one off the shelf is a big deal. It affects how well your company can innovate and grow its machine learning work for years to come. It’s important to understand what you’re getting into with each option.

The Temptation and Traps of Off-the-Shelf MLOps Tools

Off-the-shelf MLOps solutions and managed services from big cloud providers look pretty good at first. They often say they’re “end-to-end” platforms that promise you can get started quickly, cut down on development work, and enjoy the ease of having someone else manage updates and maintenance. If your company is looking for a fast way into MLOps or doesn’t have a ton of in-house experts in areas like data engineering or running infrastructure, these options can seem like the perfect shortcut. The idea of skipping the hassle of building and maintaining an MLOps setup is definitely tempting.

Finding, or Likely Frankensteining, the Perfect Solution

But the truth about “buying” an MLOps platform is usually more complicated than it seems. One of the main problems is that finding a single, ready-to-go platform that perfectly fits everything your company needs is often like chasing a unicorn. In reality, buying usually means getting and, more importantly, piecing together several different products, even if one claims to do it all. This integration work can be a lot, wiping out some of the speed benefits you were hoping for. Just getting the tools can be slow, bureaucratic, and technically demanding to test, making things take even longer.

Vendor Lock-In

Beyond just putting things together, getting locked into one vendor is a real risk. Relying too much on one company’s set of tools can limit your options, make it hard to use new or better tools from elsewhere, and ultimately slow down how fast you can deliver. While commercial platforms have a lot of built-in features, you might not be able to customize them much, forcing your teams to change how they work to fit the tool, instead of the other way around. This can be a big issue for companies with unusual types of data or very specific rules they need to follow.

Creeping Costs

Cost is another huge thing to think about. While buying a solution might seem cheaper upfront than building one, the long-term costs can really add up. Licensing fees, subscription costs that go up as you add more users or models, and charges for better support can lead to unpredictable and often very large bills over time. Not having precise control over how much you’re using in some managed services can also lead to going over budget. Buying third-party MLOps tools is rarely as simple as plug-and-play; it takes a lot of checking, integration work, and figuring out vendor systems, which makes it less quick and easy than you’d think.  

The Upsides of Building Your Own MLOps Platform

Building your own MLOps platform takes more time and resources at the start, but it offers some long-term advantages.

Full Customization

The biggest plus is total customization. You can build a platform that’s perfectly designed for your company’s specific needs, unique data types (like non-tabular data, which many off-the-shelf tools aren’t great with), complex workflows, and tough security or compliance rules. This means you get a perfect match with your business needs, not a compromise based on what a vendor offers.

Efficiency

This customization leads straight to better efficiency. When you design workflows and integrations that fit perfectly with how your team already works, you can make development and deployment much smoother. Teams don’t have to bend over backward to fit a rigid, pre-set tool.

Also, a custom platform allows for tight integration with your current tech setup. This means everything connects smoothly with your current systems, data lakes, and business apps, getting the most value out of what you already have and preventing data from getting stuck in silos.

Control and Flexibility

Maybe one of the most important benefits is full control and flexibility. Building your own platform means you’re completely in charge of its development, how it changes, and, crucially, the infrastructure it runs on. This freedom is important for adapting to new machine learning methods, new open source tools, and changing business needs without being stuck with a vendor’s schedule or pricing. This kind of control is essential for growing long-term and encouraging new ideas.

Long-Term Savings

From a money standpoint, a custom platform can be cheaper in the long run. Even though it costs more to develop at first, companies can skip ongoing vendor licensing fees, use resources very precisely, and avoid the kind of vendor lock-in that often leads to rising subscription costs.

Internal Knowledge

Finally, the process of designing and building an MLOps platform in-house builds up priceless internal know-how. The team gets a deep understanding of MLOps principles, tools, and best practices, creating a lasting capability that becomes a major asset for the company.  

Learning from the Pros: Ideas to Guide Your Build

When you’re building an MLOps platform from scratch, it helps to listen to what the leading experts in the field have to say. Their ideas can give you a sort of compass for making architectural and operational choices.

Chip Huyen: Build in Stages and Start Strong

Chip Huyen, a well-known voice in MLOps, often talks about how machine learning systems change over time and why it’s so important to have a solid, scalable base to build on. As she says, ML-powered products aren’t static; they keep evolving as input data changes, business goals shift, and new modeling techniques come along. A custom platform, built from the ground up, allows for what Huyen calls “safe incremental updates” and helps create a “tighter iteration loop.” This is because the team has full control over the setup and can design it to be modular and easy to change.  

Building from scratch, especially on something like bare metal infrastructure, makes you really dig into ML + engineering fundamentals. This thorough approach fits with Huyen’s support for strong engineering practices, leading to systems that not only perform well but are also tough and ready for future changes. A custom platform gives you the ultimate control over the automation needed to quickly and reliably develop, test, and deploy these changing ML systems.

Matei Zaharia: Using Big Data Ideas in MLOps

Matei Zaharia’s groundbreaking work creating Apache Spark changed how we handle large-scale data processing, and the ideas behind Spark are very relevant to MLOps architecture. Concepts like distributed computing, handling failures, working with data efficiently, and in-memory processing are key for dealing with the huge datasets in modern machine learning. His work on MLflow also shows how important good lifecycle management is for ML models.

MLOps is all about data. From preparing features and training models to versioning data and monitoring in production, every step creates and uses a lot of data. Building an MLOps platform from scratch lets architects design the data part based on these proven principles from large-scale data systems. This ensures the platform can efficiently handle growing amounts of data, complex changes, and the heavy input/output needs of ML work.

As Zaharia talks about, it’s important to come up with an MLOps process that automates as much of this as possible so that these teams can actually innovate. A custom platform built with scalability in mind from the data layer up offers the best chance for this kind of automation and innovation. While not directly from Zaharia, MLOps architectural ideas that focus on solid data handling, validation, and performance metrics really fit the spirit of his work.  

Demetrios Brinkmann: Real-World Tips from the Community

Demetrios Brinkmann, through his involvement with the MLOps Community, shares the practical, everyday challenges and new best practices that people in the field face. His work brings up common problems like how hard it is to figure out the costs of ML projects, the difficulties of managing different data processing methods (batch, streaming, real-time), and the organizational roadblocks when trying to scale ML operations.

Building an MLOps platform from scratch gives you a unique chance to design solutions for these known challenges right from the start. For example, if demystifying the cost side of the ROI equation is a common stumbling block, a custom platform can include detailed cost tracking and resource tagging from day one. If juggling different data processing strategies is tricky, the platform’s architecture can be designed to be flexible enough for various data sources and processing methods.

Brinkmann’s community insights often highlight the need to test things out, because “nobody knows what’s going to work for your task…you have to build like…an evaluation”. A custom platform lets you create specific evaluation setups tailored to your model types and business goals. The advice to look beyond just the first use case and make educated guesses about future needs is a direct thumbs-up for the thoughtful, forward-looking design that building from scratch allows.

What these experts are saying boils down to this: building a custom MLOps platform isn’t just a tech project, it’s a smart move towards getting better at ML. It means moving away from doing things on the fly and relying on third-party tools that might hold you back, and towards a systematic, scalable, and solid way of operating. This change leads to being more agile, in control, and efficient in the future, especially for companies where machine learning is a key part of their business.

How Building From Scratch on the Right Infrastructure Solves Your Problems

When you build an MLOps platform from scratch on the right kind of infrastructure, it can directly fix the common problems ML teams face:

Slow ML Model Training Times

By using dedicated, high-performance bare metal infrastructure, like what OpenMetal offers, teams can get rid of the slowdowns and resource fights often found in shared public cloud setups. Direct access to optimized hardware (CPUs, powerful GPUs, fast storage) means you can use all the hardware’s power, making model training much faster.

Complexities in Deploying and Managing ML Models at Scale

A custom platform, designed with your company’s specific ways of working and preferred tools, makes it simpler to get models from development to production. Your team decides how automation and integrations work, instead of having to follow a vendor’s rules, which leads to smoother and more natural deployment.

Lack of a Unified and Scalable Infrastructure

The act of building from scratch is how you create that unified and scalable infrastructure. It lets you carefully design a system where all parts – data pipelines, experiment tracking, model serving, monitoring – work together on purpose, instead of trying to patch together different cloud services that might not play well with each other.

High and Unpredictable Costs Associated with Cloud-Native MLOps Platforms

Choosing an infrastructure provider like OpenMetal, with our clear and predictable pricing (like fixed costs for hardware and straightforward bandwidth policies), and combining this with open source MLOps tools, gives companies tight control over their spending. This approach helps avoid expensive license fees for proprietary MLOps software and the confusing, usage-based bills that often lead to budget surprises with major cloud providers.

Deciding to build an MLOps platform from scratch, guided by good engineering principles and expert advice, is a big step in a company’s ML journey. It’s about taking control and committing to building something that’s truly right for the job.

Strategic Considerations for Building vs. Buying Your MLOps Platform

OpenMetal: The Foundation for Scalable MLOps

Picking the right infrastructure is key to making a custom-built MLOps platform work. It affects performance, scalability, costs, and how much control your teams have. OpenMetal stands out as a strong alternative to traditional public clouds, especially for tough MLOps jobs.

What is OpenMetal? More Than Just Another Cloud

OpenMetal offers on-demand, hosted private clouds built on dedicated bare metal hardware. Users can mix and match our bare metal servers with OpenStack private clouds and Ceph storage clusters to design their perfect architecture. This setup gives you the privacy, control, and steady performance of a private cloud, but with the quick provisioning you usually get from public cloud services.

For MLOps, which often means heavy-duty tasks, big datasets, and needing precise control over your environment, OpenMetal is a great option compared to the virtualized, shared environments of typical public clouds. It’s designed for users who want top performance, full control, and predictable costs without having to manage their own physical data centers.

Why OpenMetal’s Bare Metal Cloud is Great for MLOps

OpenMetal’s bare metal cloud has some unique features that really help organizations building MLOps platforms from scratch.

Full-Speed Performance: Bare Metal Power for Faster Training and Data Work

A big part of what OpenMetal offers is direct access to dedicated hardware resources that aren’t shared with anyone else. This includes powerful CPUs, top NVIDIA GPUs (like the A100 and H100 models that are great for deep learning), plenty of RAM, and super-fast NVMe storage. By getting rid of the virtualization layer and the “noisy neighbor” problem you often find in shared public clouds, OpenMetal makes sure your MLOps tasks get the full, unthrottled power of the hardware.

This direct hardware access makes a huge difference for MLOps. Processing large datasets, training complex models, and serving real-time predictions can all run much faster. Having fully dedicated NVIDIA GPUs and full root access to bare metal performance means no compromises on performance. This directly solves the major headache of slow ML model training. Faster training means more experiments, quicker improvements to models, and ultimately, getting valuable ML-driven insights and apps to market faster.

Clear and Predictable Costs: No More Confusing Cloud Bills

OpenMetal’s pricing is very different from the often complicated and unpredictable bills you get from major public cloud providers. The main idea is fixed monthly costs for dedicated hardware that makes up your private cloud cluster. This makes cloud budgeting predictable again, letting tech leaders plan expenses much more accurately. Instead of variable, usage-based charges that can swing wildly, organizations pay a flat rate for their hardware, kind of like leasing hardware but with OpenMetal handling the operational side.

A big part of this cost predictability is how OpenMetal handles bandwidth charges. A very generous amount of data transfer is included, and if you go over that, we bill based on a 95th percentile method, not per-gigabyte fees for data going out. This can mean egress costs that are up to 80% lower than what typical public clouds charge, which often feels like a “data tax”. For example, Convesio cut their cloud costs by over 50% after moving from Google Cloud to OpenMetal, mostly because of better bandwidth pricing and getting rid of unpredictable usage charges. This clear and fair hardware-based pricing directly tackles the problem of high and unpredictable MLOps costs, freeing up money that can be put back into new ideas.

Total Control and Transparency: You’re in Charge of Your MLOps World

OpenMetal gives customers full admin (root) access to their OpenStack control plane and, importantly, to the bare metal servers themselves (e.g., through IPMI/SSH). This level of control is fantastic for MLOps. Teams can customize the operating system, tweak kernel settings, install specific drivers (like for GPUs), and manage the entire software stack without limits from a managed service provider. Users control who uses what, resource allocation, OpenStack project setups, and all software running on their virtual machines or bare metal instances.

This total control directly solves the challenge of not having a unified, scalable infrastructure by letting teams build exactly what they need. It also makes the infrastructure less of a “black box” than some PaaS offerings, giving full transparency into how resources are being used. For MLOps, this means you can fine-tune every part of the stack for your specific machine learning tools and workloads.

Open Source Friendly: Freedom to Build Your Ideal MLOps Platform

The infrastructure OpenMetal provides is built on open source technologies like OpenStack for cloud management and Ceph for distributed storage. This open foundation naturally works well with the huge and fast-growing world of open source MLOps tools. By choosing OpenMetal, teams aren’t locked into a specific vendor’s proprietary MLOps tools or their licensing costs. Instead, they have the freedom to pick the best open source tools for each part of the machine learning lifecycle, from getting and versioning data to tracking experiments, serving models, and monitoring.

This flexibility is key for building a truly custom and well-suited MLOps platform. Using open technology helps minimize (or remove) licensing fees and avoid the vendor lock-in that can kill innovation and drive up costs. This lets teams pick their MLOps tools based on their specific needs and preferences, making sure the platform stays adaptable and ready for the future.

These advantages – full-speed performance, predictable costs, total control, and open source friendliness – work together beautifully. Faster model training from bare metal performance means more iterations and quicker innovation. Predictable costs mean teams can confidently assign resources to bigger ML problems without worrying about blowing the budget. Full control and open source support let you create perfectly tuned workflows using the best tools out there. These benefits aren’t separate; they combine to create an environment where MLOps can truly thrive, solving the main problems faced by ML practitioners and tech leaders in a complete way. This is a supportive ecosystem designed for MLOps success.

OpenMetal vs. Traditional Public Clouds for Demanding MLOps Workloads

Building Your MLOps Platform Step-by-Step on OpenMetal

Now that we know why building from scratch is a smart move and how OpenMetal offers a great foundation, let’s look at how to actually build the MLOps platform. This section gives you a step-by-step plan, suggesting open source tools and explaining how they fit into the OpenMetal bare metal cloud environment, showing how OpenMetal’s strengths can be used at each stage.

The Groundwork and Setting Up Your Infrastructure

The first step is to define, set up, and configure the basic infrastructure where all your MLOps parts will live. Automation and good orchestration are key to building a platform that can grow and be maintained easily.

Automating Your OpenMetal Cloud: Terraform and Ansible

The idea of Infrastructure as Code (IaC) is super important for making sure your MLOps platform’s base layer is reproducible, version-controlled, and scalable.

• Terraform: This open source tool is for “building, changing, and versioning infrastructure securely and effectively” by telling it what you want the end result to be. With OpenMetal, you can use Terraform to define and set up the OpenStack resources that OpenMetal provides – like compute instances, networks, subnets, and storage. Teams write down the desired state of their cloud infrastructure in configuration files, and Terraform makes the API calls to OpenStack to make it happen.
• Ansible: After Terraform sets up the basic infrastructure, Ansible takes over to manage the configuration. Ansible is an automation tool that doesn’t need agents and uses YAML playbooks to describe system setups. It’s great for things like installing common software and dependencies, setting up user accounts, tightening security, and preparing base images on your OpenMetal instances. Importantly, Ansible can set up and configure bare metal servers, which fits perfectly with what OpenMetal offers.

On OpenMetal, this combo is really powerful. Terraform scripts can create OpenMetal Cloud Cores, define your network layout, and spin up the first set of bare metal servers. Then, Ansible playbooks can target these servers to install what Kubernetes needs, set up distributed file systems, or get other basic services ready. The full control and root access from OpenMetal mean both Terraform and Ansible can work without any restrictions, automating the setup from the hardware all the way up. This layered approach – Terraform for the OpenStack layer and Ansible for the OS and software on the bare metal – creates a highly automated and customizable starting point.

Managing ML with Containers: Kubernetes on OpenMetal

Kubernetes (K8s) has become the go-to for managing containerized applications, and it’s becoming more and more central to MLOps. It gives you a solid platform for deploying, scaling, and managing the various small services that make up an ML system, from data processing jobs to model training environments and prediction endpoints.

There are many reasons to choose Kubernetes for MLOps: you can scale workloads up and down as needed, it’s portable across different environments, it manages resources (CPU, GPU, memory) efficiently, it can be extended with operators (for example, for TensorFlow or PyTorch) to handle specialized ML tasks, and it connects smoothly with CI/CD pipelines to automate the ML lifecycle.

Deploying Kubernetes on OpenMetal’s bare metal infrastructure has big advantages. OpenMetal provides the raw, high-performance hardware, and teams can then pick how they want to deploy K8s. Tools like Kubespray, which uses Ansible for automation, are great for deploying production-ready K8s clusters on bare metal and OpenStack environments. Alternatively, kubeadm gives you a more hands-on way to bootstrap a cluster. The OpenStack Ironic service can also help set up bare metal for Kubernetes nodes, showing how well they work together.

Running Kubernetes directly on OpenMetal’s bare metal means the K8s control plane and worker nodes get maximum performance and low latency. The predictable, high-bandwidth networking within an OpenMetal private cloud is also vital for good communication between nodes in the Kubernetes cluster, which is key for distributed ML work. The combination of IaC tools like Terraform and Ansible for deploying Kubernetes onto OpenMetal’s bare metal creates a highly automated, reproducible, and deeply customizable foundation. This end-to-end control, from the physical hardware (managed by OpenMetal) up to the application management layer (Kubernetes), is a key feature of a from scratch MLOps platform built for serious growth and reliability.

Data Management and Versioning: The Heart of ML

Data is the most important part of any machine learning system. Being able to manage, version, and process data reliably and reproducibly is critical for developing trustworthy models, fixing issues, and making sure the MLOps platform can be maintained in the long run.

Why Good Data Pipelines and Versioning Matter

ML models are very sensitive to the data they’re trained on. Changes in datasets, how data is preprocessed, or how features are engineered can greatly affect model performance. So, good data pipelines that automate getting data, checking it, transforming it, and creating features are essential. Just as important is data versioning – being able to track changes to datasets and link specific data versions to model versions and experiment results. This ensures you can reproduce results, allows for auditing, and makes it possible to go back to previous data states if needed.

Tools for Reproducibility with Kubernetes

Two well-known open source tools for data versioning and pipeline management in a Kubernetes-focused MLOps platform are DVC and Pachyderm.

DVC (Data Version Control):

DVC is an open source tool made to bring version control to data and ML models, working alongside Git. While Git tracks code, DVC tracks pointers to large data files and models, which are usually stored somewhere else like S3-compatible object storage (which Ceph in your OpenMetal cloud can handle), local storage, or other cloud storage services. DVC is known for being lightweight and fitting smoothly into existing Git workflows. Data scientists can use DVC commands on their own machines to version datasets and define simple data processing pipelines.

However, a key thing to remember is that DVC’s pipeline execution for transformations usually runs locally on the user’s machine and isn’t automatically scaled to a cluster. While DVC is great at versioning files and linking them to code, it doesn’t inherently manage distributed data storage or scalable computation for pipelines. On OpenMetal/Kubernetes, data scientists can use DVC for local development, with the actual large data files living on Ceph storage within the OpenMetal cloud. CI/CD pipelines running in Kubernetes can then use DVC to pull the correct data versions for automated training jobs.

Pachyderm:

Pachyderm offers a more complete, Kubernetes-native solution for data versioning and automated data pipelines. It provides Git-like features for data, meaning data is stored in versioned repositories, and pipelines are automatically started based on changes to input data repositories. Pachyderm runs on Kubernetes and provides a copy-on-write filesystem on top of an object store (again, Ceph on OpenMetal is a good backend for this).

The main advantage of Pachyderm is its ability to use Kubernetes for scaling its data processing pipelines. Each step in a Pachyderm pipeline runs as a Kubernetes job, allowing for massive parallel processing and efficient handling of large datasets. While it’s a bit heavier than DVC, Pachyderm provides a powerful, integrated system for versioning data and managing complex, automated, and scalable data transformations directly within the Kubernetes environment.

The choice between DVC and Pachyderm often depends on how complex and large-scale you need your data pipelines to be. DVC is an excellent choice for teams that want a simple, Git-focused way to version data and models, especially if pipeline management and scaling are handled by other tools or are less critical. Pachyderm, on the other hand, is a more fitting solution for organizations building a highly automated, scalable MLOps platform on Kubernetes where data lineage and reproducible, scalable data processing are top priorities. OpenMetal’s flexible infrastructure, with its high-performance compute and scalable Ceph storage, can effectively support either tool, letting teams choose the one that best fits their current setup and technical needs.

Experiment Tracking and Model Registry

Machine learning development is all about experimenting. Data scientists and ML engineers try out many different ideas, model designs, hyperparameter settings, and feature sets. Systematically tracking these experiments and managing the models that result are essential for reproducibility, teamwork, and making good decisions.

Why Tracking Experiments is a Must-Do

Without a good system for tracking experiments, ML development can quickly become messy and inefficient. It becomes hard to remember which mix of code, data, and parameters produced a certain result, making it tough to reproduce successful experiments or figure out why others failed. Good experiment tracking means logging all important details for each experimental run, including:

• Source code versions (e.g., Git commit hashes)
• Data versions (e.g., DVC or Pachyderm commit hashes)
• Hyperparameters and configuration settings
• Evaluation metrics (accuracy, precision, recall, F1-score, loss, etc.)
• Generated files (trained model files, charts, log files)
• Environment details (library versions, hardware used)

This systematic logging lets teams compare different experiments, understand how changes affect results, and pick the best-performing models to move forward.

Organizing the Model Lifecycle: MLflow

MLflow is a popular open source platform designed to manage the entire machine learning lifecycle from start to finish. It has several key parts that are very useful for building an MLOps platform:

• MLflow Tracking: This part provides an API and UI for logging parameters, code versions, metrics, and output files when running machine learning code, and for looking at the results later.
• MLflow Projects: Offers a standard way to package reusable data science code.
• MLflow Models: Provides a common format for packaging machine learning models in different “flavors”, so they can be used in various downstream tools (e.g., for real-time serving or batch predictions).
• MLflow Model Registry: Offers a central place to store and collaboratively manage the full lifecycle of an MLflow Model, including model versioning, moving models through stages (e.g., from “staging” to “production”), and adding notes.

Running MLflow on an OpenMetal/Kubernetes setup is pretty straightforward. The MLflow Tracking server can be deployed as a service within your Kubernetes cluster. The backend storage for metadata (like a PostgreSQL database) and the artifact store for keeping model files and other large outputs (like Ceph object storage in your OpenMetal cloud) can also be hosted within your controlled OpenMetal environment.

MLflow can also deploy models registered in its Model Registry to Kubernetes for serving, often working with tools like KServe and MLServer. Comet ML is another popular option (though it’s proprietary, it has a free tier) that offers similar experiment tracking and model management features, and is often praised for its user interface and collaboration tools. However, for a completely open source setup, MLflow is a very strong choice.

The practice of carefully tracking experiments is key for continuous improvement in machine learning, something experts like Chip Huyen strongly support. Tools like MLflow directly help with this by providing the necessary setup to systematically record, compare, and learn from each experiment. Without such tracking, trying to improve can just become disorganized trial and error. So, investing in a solid experiment tracking and model registry setup from the beginning is critical for building a dynamic learning system, rather than just producing static models. OpenMetal provides the stable and high-performing backend – compute for the tracking server and scalable storage for artifacts – that these essential MLOps tools need.

CI/CD for ML (Continuous Integration/Continuous Delivery/Continuous Training): Automating the Road to Production

Taking the ideas of Continuous Integration and Continuous Delivery (CI/CD) from traditional software development and adapting them to the unique needs of machine learning is a key part of MLOps. CI/CD for ML, sometimes also including Continuous Training (CT), aims to automate the entire lifecycle of building, testing, validating, deploying, and retraining ML models.  

What CI/CD for ML Means and Its Key Parts

CI/CD for ML treats data, model, and code as one complete unit. Key parts of an ML CI/CD pipeline usually include:

• Automated Data Validation and Preprocessing: Making sure data is good quality and consistent, and turning raw data into features that are good for model training.
• Automated Model Training and Retraining: Starting training jobs automatically when new code, new data, or new configurations are introduced, or when model performance drops.
• Automated Model Testing and Validation: Checking model performance against set benchmarks, on new unseen data, and looking for fairness, bias, and robustness.
• Automated Model Packaging and Versioning: Packaging the trained model with its dependencies and metadata, and versioning it in a model registry.
• Automated Deployment: Moving validated models to staging or production environments using strategies like canary releases or A/B testing.
• Continuous Monitoring and Triggering: Watching deployed models for performance drops or changes in data/concept, and automatically starting retraining pipelines when needed.

Tools for the Job on Kubernetes

Several tools can be used to set up CI/CD for ML pipelines on a Kubernetes platform running on OpenMetal:

• Jenkins: A long-time, very extendable automation server with a huge number of plugins. Jenkins can be deployed on Kubernetes and set up to manage ML pipelines.
• GitLab CI/CD: Tightly connected with the GitLab source code management platform, GitLab CI/CD offers powerful pipeline definition features using YAML and has great support for Kubernetes deployments.
• Argo Workflows: This is a Kubernetes-native workflow engine specifically designed for managing parallel jobs on Kubernetes. Workflows in Argo are defined as Directed Acyclic Graphs (DAGs), where each step in the workflow is a container. This makes it exceptionally good for the multi-step, often compute-heavy nature of ML pipelines. Argo Workflows works on any Kubernetes cluster and is often praised for its usefulness in data preprocessing, multi-stage model training, and complex model evaluation.

For an MLOps platform built “from scratch” and heavily reliant on Kubernetes, as suggested in this guide, Argo Workflows has its advantages. While Jenkins and GitLab CI are solid general-purpose CI/CD tools, Argo Workflows’ built-in understanding of Kubernetes resources, container lifecycles, and parallel job execution is natural to it. This deep integration makes it simpler to define and manage complex ML pipelines where each stage (data preparation, feature engineering, training, validation, deployment) can be a separate containerized job.

On OpenMetal, Argo Workflows can manage these entire MLOps pipelines, using the bare metal performance of the Kubernetes cluster for each compute-intensive step. This Kubernetes-native approach often leads to smoother and more efficient CI/CD for ML compared to tools that need more extensive setup for Kubernetes integration.

Model Serving and Monitoring for Delivering and Maintaining Value

Getting a machine learning model into production is a big step, but it’s not the end of the MLOps road. Making sure the model keeps delivering value requires a solid serving setup and continuous monitoring for performance and potential issues.

Ways to Deploy Models as APIs

The most common way to make ML models available to applications is by deploying them as APIs, usually REST APIs for real-time predictions. This lets other services send input data to the model and get predictions back instantly. When serving models, you need to think about how fast you need responses, how many requests you expect, how big the data payloads are, and whether you need real-time predictions or if batch predictions will do.

Serving with Power: Seldon Core or KServe

For serving models on Kubernetes, Seldon Core and KServe (which used to be KFServing) are leading open source options. Both are Kubernetes-native platforms designed to make deploying, managing, and scaling ML models in production easier. They handle complex operational stuff like request routing, auto-scaling, model versioning, and advanced deployment strategies like canary releases and A/B testing.

• Seldon Core: Lets you deploy single machine learning models, custom data transformation logic, drift detectors, outlier detectors, and even complex inference graphs or pipelines. It supports a wide variety of model types (TensorFlow, PyTorch, scikit-learn, XGBoost, ONNX, custom Python models) because it works with inference servers like MLServer and NVIDIA Triton Inference Server. Seldon Core encourages tools to work together by using the Seldon V2 inference protocol, a standard also supported by KServe and Triton.
• KServe: Offers similar features to Seldon Core, being built on Kubernetes and designed for serverless inference. It focuses on providing a standardized, highly abstracted way to deploy models from common ML frameworks. KServe often works with Knative for serverless auto-scaling (scaling down to zero when not in use) and Istio for sophisticated traffic management and observability. MLflow, as we talked about earlier, can deploy models directly to KServe.

Both Seldon Core and KServe deploy models as custom resources within Kubernetes, managing the underlying pods that serve prediction requests. Running these on an OpenMetal Kubernetes cluster ensures that model serving benefits from high-performance networking for low-latency responses and direct access to compute resources (including GPUs for faster inference if needed).

Monitoring Model Drift and Performance with Prometheus and Grafana

Deployed ML models aren’t set in stone; their ability to predict accurately can get worse over time. This can happen because of data drift (changes in the input data’s statistical properties compared to the training data) or concept drift (changes in the underlying relationships between input features and what you’re trying to predict). So, continuous monitoring is essential to catch these issues and keep models reliable.

• Prometheus: This is an open source systems monitoring and alerting toolkit that has become a standard in Kubernetes environments. Prometheus works by regularly collecting metrics from configured endpoints. Many ML serving tools, including Seldon Core and KServe, are designed to expose Prometheus-compatible metrics endpoints. For custom monitoring, applications running on Ray clusters can also expose metrics through a built-in exporter.
• Grafana: An open source platform for data analytics and visualization, Grafana connects to various data sources, including Prometheus, to create rich, interactive dashboards. These dashboards can be used to visualize key model performance indicators (KPIs) like prediction latency, request throughput, error rates, resource usage, and custom metrics designed to detect data or concept drift.

To set up this monitoring stack on an OpenMetal Kubernetes cluster:

1. Deploy the Prometheus Operator, which makes it easier to manage Prometheus instances within Kubernetes.
2. Configure ServiceMonitors or PodMonitors to tell Prometheus to collect metrics from your model serving endpoints (exposed by Seldon/KServe), data pipelines, and maybe even model training jobs.
3. Use Grafana to build dashboards that give you insights into model behavior and operational health.
4. Define alerting rules in Prometheus (often managed via Alertmanager, part of the Prometheus ecosystem) to notify the MLOps team of critical issues, like big drops in model accuracy, spikes in error rates, or detected drift.

Model serving and monitoring are two sides of the same production coin. Successfully deploying a model with Seldon Core or KServe is only the first step; making sure it continues to provide accurate and reliable predictions requires a solid monitoring strategy using tools like Prometheus and Grafana.

The MLOps platform design must inherently consider how serving tools will provide telemetry and how monitoring tools will consume and visualize it. Building this observability into the platform from the start, made easier by the control and transparency that OpenMetal provides over the Kubernetes environment, is crucial for maintaining the long-term value and trustworthiness of deployed machine learning models.  

Open Source Toolkit for Your MLOps Platform on OpenMetal

Growing and Adapting Your MLOps Setup

Building an MLOps platform from scratch on OpenMetal isn’t a one-and-done deal. You want to create a system that can grow and change. The setup we’ve talked about is designed with future growth and adaptation in mind, so the platform can handle increasing demands and take on new advances in the MLOps world.

How the OpenMetal Setup Handles Future Needs

Choosing OpenMetal’s on-demand bare metal private clouds from the start inherently supports growth. As MLOps workloads get bigger, needing more data processing, more frequent model training, or serving more prediction requests, organizations can easily add more dedicated bare metal servers to their private cloud cluster. This means more raw computing power, more storage through Ceph, and better network capacity.

On top of this expandable bare metal base, Kubernetes offers its own powerful ways to scale. The Kubernetes cluster itself can grow by adding more worker nodes (new OpenMetal servers). For MLOps services that don’t store state (like some model serving endpoints or data processing workers), Kubernetes’ Horizontal Pod Autoscaler (HPA) can automatically change the number of running pods based on CPU use or custom metrics. Stateful applications, common in data storage or some parts of the MLOps control plane, can be scaled using Kubernetes StatefulSets.

Also, the modular nature of the recommended open source tools helps with scalability. Each part – whether it’s MLflow, Pachyderm, Argo Workflows, or Seldon Core – is generally designed for distributed setups and can often be scaled on its own. For instance, you might scale out the number of workers for MLflow or increase how many Argo Workflow tasks run at once without necessarily needing to scale every other part of the platform in the same way. This precise scalability means you use resources efficiently.

Advanced Ways to Scale

Besides just adding more general-purpose compute nodes, there are several advanced ways to scale specific parts of the MLOps platform, especially for demanding machine learning jobs:

Using Distributed Training Frameworks

Training increasingly complex models, especially deep learning models, on huge datasets often needs more computing power than a single machine (even a powerful GPU server) can offer. Distributed training frameworks let you spread the training workload across multiple nodes and multiple GPUs.

• Ray: This open source framework is designed to make it easier to develop and scale distributed Python applications, making it exceptionally good for a wide range of ML workloads. Ray Core provides simple tools for parallelizing Python code. On top of this are libraries like Ray Tune for massively parallel hyperparameter searching, Ray Train (which includes features from the old Ray SGD) for distributed deep learning, and Ray Serve for scalable model serving. Ray can run efficiently on Kubernetes clusters deployed on OpenMetal, taking full advantage of the bare metal performance and high-speed, low-latency networking that are crucial for communication-heavy distributed tasks. Ray is also designed to handle failures, gracefully dealing with machine problems during long-running training jobs.
• Horovod: Developed by Uber, Horovod is a distributed deep learning training framework that works with popular libraries like TensorFlow, Keras, PyTorch, and Apache MXNet. It’s known for being easy to use, often needing only small code changes to existing single-GPU training scripts to enable data-parallel distributed training. Horovod works well with MPI (Message Passing Interface) and NVIDIA’s NCCL (NVIDIA Collective Communications Library) for high-performance communication between GPUs and nodes, which directly benefits from OpenMetal’s low-latency networking and direct GPU access.
• Other Frameworks (e.g., DeepSpeed, FSDP): For training truly massive models (like large language models with billions of parameters), more advanced libraries like Microsoft’s DeepSpeed or PyTorch’s Fully Sharded Data Parallel (FSDP) offer sophisticated techniques for memory saving and parallelism (model parallelism, pipeline parallelism, and advanced data parallelism like ZeRO). These tools are designed to push the limits of model size and can be deployed on multi-GPU, multi-node clusters on OpenMetal.  

Specialized Hardware for AI/ML

The need for specialized hardware, especially GPUs, is a defining feature of modern ML. OpenMetal’s offering of high-performance NVIDIA GPUs, including the A100 and H100 models, is critical for scaling deep learning training and inference workloads. The ability to set up private GPU servers and customize GPU clusters and specifying GPU counts, CPU-to-GPU ratios, RAM amounts, and storage sizes allows organizations to tailor their hardware infrastructure precisely to their scaling needs. This avoids overspending and ensures that compute resources are perfectly matched to the job.

Being able to scale isn’t just about adding more resources, but about smartly applying the right kind of scaling to the right part of the MLOps lifecycle. A from scratch platform built on flexible infrastructure like OpenMetal allows for these different scaling strategies: scaling data processing with tools like Pachyderm or Ray Data, scaling model training with Ray or Horovod on GPU clusters, and scaling model serving with KServe or Seldon Core using Kubernetes’ own scaling features. This targeted approach leads to more efficient resource use and better overall performance compared to a one-size-fits-all scaling model that might be forced on you by a single vendor solution.

Evolving Your Platform: Always Improving and Adapting

The MLOps world changes incredibly fast. New tools, better algorithms, new techniques, and changing best practices pop up all the time. A key advantage of a custom-built MLOps platform is that it’s flexible enough to adapt and bring in these new ideas. Unlike proprietary platforms that are tied to a vendor’s development schedule and plans, a “from scratch” system built with open source parts gives your organization the agility to check out and integrate new technologies as they become useful.

This evolution should be guided by feedback. Continuously monitoring deployed models gives you data on performance, drift, and resource use. Feedback from data science teams on their development experience, and from business stakeholders on the impact of ML applications, should all help in the ongoing refinement and improvement of both the MLOps platform and the models it supports. This fits perfectly with the iterative idea championed by experts like Chip Huyen, where the system is always learning and getting better.

The modular, component-based setup advocated in this guide also makes evolution easier. Because the platform is made up of distinct open source tools for different MLOps stages, individual components can often be upgraded, reconfigured, or even swapped out for better alternatives if they come along, without needing to redo the entire system. This architectural choice makes your investment future-proof.

The Strategic Payoff of Building from Scratch

Starting the journey to build a complete MLOps platform from scratch is definitely a big job. It needs careful planning, dedicated resources, and a commitment to growing in-house expertise. However, it’s an investment that pays off in big and lasting ways.

These payoffs show up as better performance tailored to your specific workloads, predictable and often much lower long-term costs, complete control over the entire machine learning lifecycle, freedom from the limits and expenses of vendor lock-in, and the development of an invaluable internal skill set in MLOps. For organizations where machine learning is, or aims to be, a core competitive advantage and driver of business value, this approach is a powerful strategic move. It allows teams to innovate faster, operate more efficiently, and ultimately, take full command of their MLOps journey.

Wrapping Up: Get Started Building Your Perfect MLOps Platform

The road to a mature and effective MLOps practice is full of important decisions. This guide has hopefully highlighted the strong strategic reasons for choosing to build a custom MLOps platform from scratch, especially when it’s built on the powerful and transparent bare metal cloud infrastructure offered by OpenMetal. This approach directly tackles and overcomes the common frustrations of slow model training, complex deployments, fragmented systems, and unpredictable costs that affect many ML projects.

By using OpenMetal’s full-speed performance, predictable and cost-effective pricing, complete hardware control, and natural fit with open source tools, organizations can build an MLOps environment perfectly suited to their needs. The blueprint we’ve detailed from automated infrastructure setup with Terraform and Ansible, to Kubernetes orchestration, solid data management with DVC or Pachyderm, systematic experiment tracking with MLflow, Kubernetes-native CI/CD with Argo Workflows, scalable model serving with Seldon Core or KServe, and careful monitoring with Prometheus and Grafana provides a clear roadmap using open source technologies.

This journey is far more than just an infrastructure project; it’s about creating an environment where new ideas can thrive. It’s about giving ML engineers, data scientists, and MLOps specialists the tools and control they need to move from idea to production efficiently and reliably. While the initial investment is significant, the long-term benefits in terms of performance, cost savings, flexibility, and in-house expertise are huge.

For organizations serious about unlocking the full potential of machine learning and making it a sustainable, scalable, and strategic asset, taking control of the MLOps platform is essential. The power to build the MLOps future that meets your precise needs and goals is not only possible but is increasingly becoming a sign of leading AI-driven companies.

If you’re excited about all the possibilities after reading this and ready to chat with us about designing your custom MLOps platform, get in touch!