As we've seen a Cambrian explosion of machine learning model technologies rapidly developed and released by the likes of Google, Meta, and various research collectives, many teams are diligently working to build the necessary infrastructure and processes to make the most of the newly available machine learning advancements.
A crucial step in constructing infrastructure that facilitates swift adoption, experimentation, and utilization of the latest machine learning (ML) models emerging weekly is the capability to quickly deploy custom or refined ML models to production endpoints for live traffic validation. Typically, this process involves integrating trained, pre-trained, or fine-tuned ML models into production environments for inference based on real-world data.
The ability to deploy machine learning models across several platforms, from cloud-based systems to edge devices, has increased the adoption and integration of ML into many company's products. As a company and team building an MLOps platform, we at Modelbit have been lucky to have a front row seat to witness the advancements that have either improved existing workflows for deploying models or provided new entirely ones.
So, what trends are we seeing with model deployment? We gathered insights from users and engaged in community conversations about how they are deploying ML models (trends). The result? This blog post!
In this article, you will:
The article also covers the necessary regulatory and ethical aspects of deploying machine learning models. Here’s the TL; DR:
Let’s jump in! 🚀
The landscape of ML deployment is constantly changing, shaped by ongoing research and the data-centric needs and model-centric demands of modern-day businesses. The following trends show the current trajectory in ML deployment, highlighting how these tools, frameworks, practices, multi-functional team structures, workflows, and production requirements shape how teams deploy ML solutions.
Here are the trends you will explore in this article:
This includes every stage of the lifecycle, from data preparation and model training to evaluation, deployment, monitoring, governance, and management, using tools like Modelbit, MLflow, Databricks, Kubeflow, and TensorFlow Extended (TFX), among others.
If done right, MLOps simplifies the monitoring, governance, and maintenance of ML models in production. This results in more reliable ML applications that drive organizational value.
Based on our conversations with users, here are some specific areas that you should be aware of within this trend:
Many teams are increasingly deploying more models that solve different use cases with varying production requirements. As a result, they constantly try to implement tools and practices that make it easy to move models to production. In the DevOps world, that is CI/CD— and it is almost no different when it comes to MLOps. Continuous integration and deployment, or delivery (CI/CD) practices, are gaining momentum for MLOps.
They use tools such as Travis CI, GitLab CI, GitHub Actions, and Jenkins to facilitate continuous integration in ML by automating the process of checking out code, completing tasks, validating the codebase, performing unit tests, and merging code. These steps ensure they constantly test and validate code, data, schemas, models, and other components.
The continuous delivery (or deployment, depending on the team’s workflow) step takes this process further and ensures that the way they deploy machine learning models is consistent and automated. It automates building, testing, and releasing models into production environments.
In ML, continuous delivery is not just about delivering changes like new features, bug fixes, configuration tweaks, or experiments. It means setting up a system where the users train models and then deliver a prediction service.
Another aspect unique to ML is continuous training (CT), where they monitor the models in production, measure their performance, retrain them if performance degrades, and re-deploy them to maintain accuracy and effectiveness over time.
Most ML teams we have spoken to have become adept at tracking and managing the evolution of their models and projects with experiment tracking. Companies building tools for ML experiment management, such as neptune.ai and Weights and Biases, are being rapidly adopted by teams working on machine learning projects, making ML deployment more accessible.
These tools provide a platform to help them track, visualize, and manage different aspects of their ML experiments. Machine learning practitioners use these experiment tracking tools to organize ML projects, track the model training process, compare results, reproduce the experiments, and make informed decisions.
Ultimately, this contributes to more successful deployments, especially when their experiment management tools integrate model registry and deployment platforms like Modelbit, for example. This results in users iterating on new models and shipping them quickly for smoother model transitions across staging and production environments.
As part of the current trend in ML pipelines, most teams primarily use Git for version control to manage and track changes to the project code, model, datasets, and other vital components of ML pipelines.
This not only ensures consistency across different stages of their model development but also provides a traceable history of changes made throughout the entire process.
Version control allows these teams to track changes, revert to previous versions, and understand the impact on model performance. This reduces errors and improves collaboration, which contributes to the success of their ML projects.
As ML pipelines evolve, there's a clear shift towards production-ready frameworks that are designed explicitly for ML workflows, such as Kubeflow Pipelines, Metaflow, Vertex AI Pipelines, SageMaker Pipelines, Kedro Pipelines, ZenML, and Flyte. These tools provide a more tailored approach to handling the unique challenges of ML projects compared to traditional tools like Jenkins.
Integrating Git alongside ML-specific pipeline tools optimizes their deployment process, operations, and regular maintenance. This approach enables them to automate workflows, manage complex models, and curate large datasets (data pipelines).
Containerization is the most popular deployment trend we have seen from users. They use a container image to package an ML model, along with its dependencies, into a portable file that can run on any computing environment.
Technologies like Docker facilitate containerization of ML and non-ML services, making deployment simpler, environments consistent, and management more effortless. This way, users have the same development environment no matter where they work, which is important for reproducibility. In fact, we designed our platform such that every model deployed with Modelbit receives its own isolated container behind a REST API.
Microservices deployments are arguably the most popular non-ML deployments that ML engineers have to work with, especially when collaborating with others in their organization. It’s about decomposing an application into isolated services that operate independently.
Each service performs a specific task and communicates with others using clearly defined protocols, like APIs. This design allows each component to be deployed and updated independently for flexibility and isolated improvements.
For example, in a machine learning application that provides product recommendations, the service that generates the recommendations can be updated to use a new algorithm without affecting other components of the application, such as the user interface or database. This results in quicker updates and more reliable software.
When containerization and microservices work together, they build a strong base for modern machine learning systems that can scale as needed and integrate with existing applications.
The trend toward workflow scheduling and orchestration in ML deployment is increasingly shaping the field. Some ML teams are adopting tools like Apache Airflow and Argo Workflows, specifically designed to manage complex ML workflows.
Workflow scheduling is about planning and determining the order of execution for tasks to ensure they run in the correct order and at the right time. Orchestration, meanwhile, centers around coordinating and managing the resources for these tasks, especially in multi-step workflows. This ensures that they interact correctly and that potential failures are managed efficiently.
Data and ML teams are adopting orchestration tools to provide a scalable solution to handle workflows with large volumes of ML models and data. Using schedulers and orchestrators ensures that they use resources efficiently and that their workflows are reproducible.
When teams automate the tasks involved in data preparation, model training, evaluation, and deployment, it facilitates good collaboration among data scientists, DevOps, and IT operations teams.
These tools also offer a comprehensive understanding of their entire machine learning lifecycle, allowing them to visualize and understand the impact of their ML models across various use cases.
The move towards serverless architectures, hybrid, and multi-cloud deployments is transforming ML deployment. These architectures provide more flexible, scalable, and cost-effective solutions.
Serverless deployment with platforms such as AWS Lambda and Azure Functions eliminates the need for these users to manage infrastructure. Essentially, it provides sophisticated networking environments and servers that autoscale to their workloads. Some users agree it saves them costs; others say it’s more expensive than deploying on bare metal, but the common theme is that this trend boosts their operational efficiency. At Modelbit, we designed a proprietary serverless architecture that makes on-demand GPUs available for both model deployment and model training.
We are seeing a few ML and data teams with particularly niche production requirements deploy hybrid machine learning systems. Such systems must run on-premises and in the cloud, or in some cases, on an edge device.
In cases where the ML engineers have constraints on moving their training data entirely off the on-premises infrastructure, they combine the security of on-premises solutions with the scalability and flexibility cloud environments provide.
Ideally, these types of deployments should be:
A cross-platform solution like Kubeflow can enable deployment across different platforms because it can run anywhere the Kubernetes infrastructure is available. This is important for organizations with requirements to serve models to clients on multiple platforms, from mobile devices to edge computing environments.
In other cases, we discovered that some users run systems that generate data in one cloud or that an application in another consumes their model predictions. This is where they run multi-cloud deployments—to utilize the strengths of different cloud providers with tools like Google Anthos and VMware Tanzu. This improves the performance and reliability of their deployments.
While these deployments offer numerous benefits, they also present challenges, such as managing data and deployment across multiple environments that may need to comply with various regulations. However, the advantages of serverless, hybrid, and multi-cloud deployments make them a valuable option for ML teams.
Edge AI and on-device ML deployment bring computation and data storage closer to the source. This trend involves deploying ML models directly on edge devices, such as smartphones, IoT devices, and sensors for inference, especially for requirements where users are bottlenecked by connectivity issues or need to minimize data transmission to the cloud.
Users prefer to run inference on the edge because it not only ensures greater data and client privacy, reducing the risk of breaches, but also enhances the efficiency, reliability, and responsiveness of their machine learning applications.
Most ML engineers cited tools such as TensorFlow Lite, Core ML, and ONNX for simplifying the deployment of models on edge devices. These tools facilitate real-time data processing, optimize resource usage, significantly reduce inference latency, and notably improve application performance.
In summary, edge AI and on-device ML deployment are indispensable in today’s machine learning landscape, significantly benefiting organizations and their users.
By understanding these trends, we can better predict the future of ML deployment. This knowledge can help data and ML teams adapt their workflows and find new ways to gain a competitive advantage.
Deploying machine learning models will undergo transformative changes over the next few years, and it will happen very rapidly. The following predictions offer a glimpse into the next frontier of ML deployment and its potential impact on various industries.
In the future, we anticipate the emergence of standardized deployment protocols—a “canonical stack.” This will facilitate smoother integration of ML models into different systems for compatibility and interoperability.
Standardized protocols, like LAMP or MEAN stack in web development, will act as a guideline for best practices, streamlining the deployment process and reducing the complexity associated with integrating machine learning models into various platforms.
By establishing a standard set of practices and guidelines, these protocols will facilitate a more unified and cohesive approach to deploying ML models. This, in turn, will lead to more consistent and broadly used deployment tools and practices. In many ways, this should improve your chances for successful ML deployments that drive value for organizations and your users.
Additionally, by adopting standardized protocols, developers and data scientists from all over the world will be able to collaborate more effectively, sharing insights and best practices to advance the field of machine learning further.
As the intersection of machine learning and operations deepens, we expect a rise in integrating MLOps tools directly into developer environments. These integrations will provide a seamless experience covering every stage of the machine learning lifecycle, from data preparation and model training to deployment and monitoring. This will help organizations efficiently manage their machine learning workflows, ensuring consistency, reproducibility, and scalability.
As MLOps tools become more ingrained in developer environments, they will significantly streamline the machine learning deployment process. Developers will have all the necessary tools at their fingertips that make it easier to navigate the complex landscape of ML deployment.
The result? Quicker, more efficient deployments, and more high-performance models in production.
Many more users will use GPUs and TPUs because GPU inference deployment tools will improve, and more large language models will be made to work best with these kinds of accelerators. These tools will use the processing power of GPUs to accelerate the execution of ML models, which can lead to faster and more optimal inferencing, especially for large models.
Also, as the ecosystem of machine learning tools grows and changes, it will become more critical for GPU inference deployment tools to integrate with a wide range of tools and cloud-native environments. This will facilitate a seamless deployment process because users talk about configuring interfaces like NVIDIA’s CUDA akin to “doing gymnastics” on their infrastructure.
Platforms or tools that make deploying to GPU devices easy will continue to grow because they will continually enable cloud-native teams (or on-premises) to fully take advantage of GPU-accelerated inference.
Multimodal models, which process various data types like text, images, and audio, are expected to gain popularity. Deploying these models will require specialized tools, resources, and platforms to manage the diversity of modalities.
Deploying multimodal models will require robust infrastructure to support their increased computational demands. This includes the necessary hardware and software, data pipelines, and storage solutions to accommodate the complexity of data with multiple modalities. An example is users beginning to adopt or deploy Vision-Language Models (VLMs) for production use cases.
Organizations must invest in improving their infrastructure to successfully deploy multimodal models, maintain them, and fully maximize their benefits as these models become popular.
As teams increasingly deploy ML models for critical applications, there will be a heightened emphasis on security, privacy, and other ethical considerations related to the deployment process. This includes adopting stringent data protection measures to safeguard sensitive information during the deployment phase.
That should uphold transparency and fairness in deploying these models across various platforms and address potential biases that could arise during deployment. These measures will be crucial in building trust and ensuring the responsible and ethical use of machine learning models in production.
In addition to the measures mentioned, there will likely be an increased focus on AI regulations aimed explicitly at governing the deployment of ML models. These regulations will likely mandate specific standards for model transparency, accountability, and fairness during and after deployment, among other ethical considerations.
Ensuring ML engineers protect the integrity of models from adversarial attacks and other threats that could hurt their performance and dependability in real life will become an essential part of security measures.
It will also be important to monitor compliance with regulations and the ethical behavior of production machine learning models to ensure they keep working as planned and do not cause any harm or behave unintendedly. Especially now that many governments are beginning to provide stricter regulations on AI.
The ML deployment landscape is dynamic, with potential changes on the horizon in the coming months and years. This may bring challenges for some organizations and opportunities for others.
Currently, there are exciting MLOps trends, such as production-ready pipelines, containerization, and edge AI, which are reshaping machine learning model deployment and enhancing speed, reliability, and efficiency. Anticipate more changes, including standardized protocols, integrated MLOps tools, GPU inference improvements, and heightened security and ethics focus, soon.
It's an exciting time in this field, and we look forward to the evolution driven by research and industry demands. Being well-prepared ensures you stay at the forefront of your industry. With such trends and predictions on the horizon, you need a stack flexible enough to deploy projects with different requirements quickly and reliably!⚡