Many companies and organizations around the world use cloud services and other similar tools to improve their workflow as well as to create new products. MLOps play an important role here as they are very useful in complex macroscopic learning environments.
MLOps refers to a set of practices for deploying and maintaining ML models efficiently and reliably. In today’s article, we focus on the five best MLOps tools that may help you improve the deployment of your machine learning projects. Discover how MLOps consulting can help you grow your business!
MLOps – What is it?
MLOps (machine learning operations) is a strategy that automates various tasks related to the deployment of machine learning models. MLOps ensures the entire ML development process is carefully documented and managed for optimal results. Moreover, it is based on DevOps and practices that increase the efficiency of workflows. MlOps applies these principles to the machine learning process to:
- Faster experimentation and model development
- Improving the quality and safety of ML models
- Deploy models faster to production
- Document practices that will make machine learning creation more scalable
- Quality control and end-to-end tracking of trace data
The best MLops tools
MLFLOW
MLflow is an MLOps open-source platform that supports the ML lifecycle. Therefore, it has several components to control your ML model during training and running. Moreover, MLflow supports the deployment of models, reproducibility, and experimentation. This platform integrates with other MLOps tools, e.g., Python, Algorihmia, Google Cloud, and many others. Key features:
- Is compatible with dozens of machine learning libraries, languages, and codes
- Regardless of the cloud, MLFlow runs in the same way
- Both individuals and large organizations can leverage this platform
- Offers the following four components:
MLflow Tracking provides an extensive registering framework around your machine learning code. You can also compare the output to previous runs through its API and UI.
The MLflow Project is a reusable and reproducible method for packaging data science code. MLflow Model is a standardized setup that allows you to save a model in various formats that are recognized by downstream tools.
Model Registry is a component that lets you centrally control your models and their lifecycle through API and UI. This feature provides model versioning, model lineage, stage transitions, as well as annotations.
KUBEFLOW
Kubeflow is an MLOps open-source, free platform that controls the deployment of end-to-end ML workflows on Kubernetes. Engineers can build ML models and analyze their performance, type hyper-parameters, or manage computer power with this tool. Additionally, Kubeflow is a great tool for deploying machine learning systems in a variety of environments to test, develop, and implement production-level services. Kubeflow supports TensorFlow Service, Seldon Core, Triton Inference Server as well as MLRun Serving. Kubeflow components:
- A user interfaces to manage jobs, experiments, and runs.
- Notebooks to interact with the system.
- An SDK to describe and run pipelines and components.
- A multistage ML workflow engine.
PACHYDERM
Pachyderm is an MLOps tool that incorporates end-to-end pipelines with data lineage on Kubernetes. Its goal is to simplify the process of managing ML pipelines, models, and data sets. Pachyderm integrates with a wide variety of solutions, such as Azure, Seldon, Label Studio, or Superb AI.
- Automated Data Versioning: Pachyderm creates and stores different versions of data. Additionally, you can reproduce previous versions of data.
- Data-Drive Pipelines: It enables packaging code, rapid script execution, and deployment of workflows in production.
- Immutable Data Lineage: It ensures a permanent record of the machine learning lifecycle
- Console: It offers aids in reproducibility as well as an intuitive visualization of DAG.
BENTOML
BentoML is an MLOps tool that allows deploying machine learning models and ensures extensive architecture to turn the trained models into a production environment. It supports the following ML frameworks PyTorch, H2O, Spacy, TensorFlow, and many more. Features:
- Online and offline serving that’s possible on any platform
- It works with DevOps and infrastructure tools such as Apache Airflow, Cloud Run, Kubeflow, etc.
- The advanced micro-batching mechanism for the highest online serving performance
DATAROBOT
DataRobot is a unified analytics platform that enables enterprises to build predictive models and grow faster. Analyzing a number of machine learning algorithms, DataRobot produces, employs, and builds personalized, predictive models for every situation you face. This platform has been designed not only for data scientists but also for people who are not related to the field but want to maintain AI. The great convenience is that you can access it from anywhere and from any device. It supports open-source frameworks like SparkML, TensorFlow, or H2O. Some features:
- Hadoop cluster plug and play
- Numerous database certifications
- Automated machine learning
- Preparation of the data
Conclusion
Machine learning operations are becoming a part of almost every piece of technology that everyone uses today. MLOps tools facilitate integration and collaboration and allow data analysts to solve problems and create more effective ML models. Do you want to know more? See MLOps consulting services.
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