The right tech helps ML engineers focus on building powerful machine learning models instead of managing manual configurations
Organizations often invest heavily in AI and ML initiatives. Sadly, those initiatives often fail. And it’s not because of poor model performance. It’s because of flawed integration and orchestration within the existing workflows.
A recent MIT study shows that a staggering 95% of generative AI implementations in enterprises have no measurable impact on profit and loss.
And it all comes down to poor integration with established systems and processes.
Orchestration is obviously a big part of machine learning (ML) development and deployment.
But tackling orchestration manually is ineffective at best, and error-prone and costly at worst. That’s why ML orchestration tools work to automate and streamline that process. They make it easier for developers to do everything. This includes managing data pipelines and deploying fully trained models at scale.
In this guide, we’ll take a look at the six best ML orchestration tools on the market. These are the solutions that developers, engineers, and data scientists can use to best orchestrate machine learning pipelines.
Key Takeaways
- MIL orchestration eliminates the inefficiencies that so often come from manual efforts. Developers can focus on building better models by automating pipelines, testing, deployment, and monitoring. Orchestration tools manage infrastructure so developers don’t have to.
- The best tools balance flexibility and scalability. And platforms like DuploCloud, Airflow, and Kubeflow integrate with existing environments. They also scale effortlessly, all of which makes them suitable for both startups and enterprise teams.
- Security and compliance are critical in orchestration. This is why modern platforms like DuploCloud offer built-in compliance checks, encryption, and monitoring. This helps you make sure that your ML operations remain secure and ready for production.
What Is ML Orchestration?
ML orchestration refers to the practice of automating all aspects of machine learning models at scale.
Ideally, orchestration automates design, testing, deployment, monitoring, and iteration. And it catches every step in between. We know that manually managing all those development processes is incredibly costly and time-consuming. As such, ML orchestration tools help developers automate as much as possible. That way, they can focus their efforts on building ML models instead of orchestrating them. ML orchestration is just a better use of highly specialized development talent and organizational resources. This is especially true because today’s orchestration tools are only going to get better.
TThe 6 Best ML Orchestration Tools
#1: DuploCloud
DuploCloud streamlines AI and ML orchestration. It also improves security and empowers developers to scale almost effortlessly, all within the same environment. Our low-code/no-code platform makes it easy for you to implement Amazon Web Services (AWS) workflows or deploy large language models through Google Kubernetes Engine (GKE).
Extensive integrations with popular DevOps tools make it easy for teams to use DuploCloud to build ML models. And they don’t have to redesign their org charts or refactor their code base.
Here are some of the most exciting features DuploCloud offers ML developers and data scientists:
- Access-based security: DuploCloud offers end-to-end encryption, threat detection, and automated compliance checks. So developers can launch with strengthened ML security from day one.
- Integrated CI/CD pipelines: DuploCloud offers continuous integration and continuous deployment features. This helps you make sure that each release and update is faster and more reliable.
- Real-time monitoring: DuploCloud’s 24/7 security and around-the-clock monitoring systems provide you with real-time insights. Your developers can use these to identify risks, resolve issues, and improve performance.
- Infrastructure as code: If you decide to use IaC, standardizing and automating infrastructure setup with DuploCloud makes it easy to consistently:
- Manage
- Replicate
- Scale
#2: Airflow
Airflow was originally built at Airbnb. But the Apache Software Foundation has been running the open-source Python project since 2016. The company designed the platform to help developers. So they can design, schedule, and monitor complex workflows according to a strict program.
True, it isn’t used only for machine learning orchestration. Still, Airflow remains one of the most popular ML tools on the market today. It offers benefits like:
- Modular architecture for infinite scalability
- Dynamic pipeline generation defined in Python
- Extensible libraries for variable levels of abstraction
- Lean design and templated code consistency
Airflow is easy to use. This is because of its web application interface and its reliance on Python. It also integrates with AWS, Google Cloud Platform (GCS), and Microsoft Azure. So developers can extend existing infrastructures without any issues. And with an active community using and contributing to the open-source platform, Airflow offers a wealth of documentation and support.
#3: Kedro
Kedro is another Python-based open-source workflow orchestration framework. Engineers can use it to explore and transition production-ready code into their experiments. Kedro standardizes the code used in machine learning projects. This way, data science and engineering teams can collaborate seamlessly. The major features of Kedro’s data science development environment include:
- Kedro-Viz: Developers can visualize pipelines and workflows with clear blueprints. These make it easy to track experiments, trace data lineage, and collaborate with stakeholders.
- Data Catalog: Kedro’s lightweight data connectors make it easy to save, load, and integrate data. And they can do it all from a wide variety of file systems and file formats.
- Project Templates: Developers can use Kedro to design standardized templates that keep configurations, code, tests, and docs consistent across every project.
Kedro also supports seamless integrations with popular development tools and deployment environments like:
- Amazon SageMaker
- Apache Airflow and Spark
- Argo
- Azure ML
- Databricks
- Docker
- Kubeflow
- Prefect
#4: Kubeflow
Kubeflow is a free open-source toolkit that puts Kubernetes to work specifically for ML pipeline orchestration. Its pre-configured containers are designed to support the entire ML operations lifecycle. This runs from training and testing to deployment.
Yes, Googlers were responsible for the origin of Kubernetes. But developers from major tech players, including Google, Cisco, IBM, and Red Hat, all came together to launch Kubeflow.
The toolkit combines containerized Kubernetes application development with easy integrations to popular ML workflow systems like Airflow. In that sense, Kubeflow is often part of a broader ML tech stack as opposed to standing alone as a one-stop-shop platform.
#5: Metaflow
Metaflow is a framework built to support ML and AI projects. Its workflow management features aim to help data scientists focus on the hard work of building models. So it doesn’t get bogged down in manual management tasks, machine learning operations, or MLOps orchestration tasks.
Metaflow helps engineers run experiments. They can develop, test, debug, and analyze their results locally. Metaflow also makes it easy to scale to the cloud by facilitating collaboration across multiple cores and instances.
And once engineers are satisfied with their experiments, they can deploy workflows to production. All the while, they can trust that they will automatically update in response to changing data.
Metaflow was originally developed at Netflix, so there’s no surprise that it’s an expensive tool. It integrates seamlessly with major cloud providers like AWS, GCP, and Microsoft Azure. It also integrates with various machine-learning-oriented programming languages.
#6: Prefect
Prefect is a modern workflow orchestration platform that promises increased flexibility and simplicity. It compares well to solutions like Airflow. Its focus on fully local building, debugging, and deployment sets it apart. It’s also easy to set up and has a quick pipeline deployment. There are three main products under the Prefect’s umbrella:
- Prefect Cloud: This fully managed workflow orchestration option allows machine learning developers to benefit from a fully hosted service.
- Prefect Open Source: This one is billed as an alternative to Airflow. The open-source option allows developers to select the tools and features that support their workflows.
- Marvin AI: This engineering framework is specifically designed to help developers build AI models, classifiers, functions, and full applications on natural language interfaces.
DuploCloud Makes ML Orchestration Easy
By aligning with a platform like DuploCloud, you don’t just get faster deployment. What really stands out is your ability to unlock flexibility, scalability, and cost efficiency.
Our multi-cloud compatibility means your teams can run workloads across AWS, Azure, and GCP. And you don’t even have to rewrite your pipelines or sacrifice your performance.
Automated compliance and built-in security cut your operational risks.
Real-time monitoring ensures continuous optimization.
If you’re looking to innovate quickly and keep your infrastructure streamlined, DuploCloud is the way to go.
It’s your fool-proof orchestration solution that evolves with your business.
Of course, finding the right ML orchestration tool requires a clear understanding of your organization’s needs. You also want to reflect on your team’s workflows. DuploCloud streamlines AI and ML orchestration. This helps you replace time-consuming and error-prone manual configurations with a fully automated low-code/no-code approach.
Our platform offers advanced features like:
- Continuous monitoring and reporting
- Automatic compliance checks
- Seamless platform integrations
These all allow MLOps and DevOps engineers to focus on highly specialized and valuable work.
DuploCloud even speeds up deployment times by 10x.
Contact us today to see how DuploCloud can help take your ML pipeline orchestration to the next level.
FAQs
Why do developers need ML orchestration tools instead of manual workflows?
Manual machine learning is time-consuming and prone to massive human errors. It’s also super costly. Orchestration tools automate repetitive tasks and improve your team’s collaboration. So it’s easier to scale models into production without having to sacrifice reliability.
Which ML orchestration tool is best for beginners?
Tools like Prefect and Kedro are highly recommended for newcomers because they’re super easy to set up. They also have clear visualization features and active open-source communities that offer a ton of support.
How does DuploCloud stand out from other ML orchestration tools?
At DuploCloud, we differentiate ourselves with our low-code/no-code automation, integrated security and compliance, and real-time monitoring. These features are all those that help you work with ML orchestration without needing deep DevOps expertise.
Can orchestration tools work across multiple cloud platforms?
Yes! Leading orchestration tools like DuploCloud, Airflow, Kubeflow, and Metaflow support integrations with AWS, GCP, and Azure. This makes it easy to extend your workflows across hybrid or multi-cloud environments.
