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Best MLOps Tools & Platforms 2022
MLOps (Machine Learning Operations) has become a trend in the current world of Artificial Intelligence. There are several operations that are part of any machine learning process. This includes data versioning, feature engineering, model monitoring, experiment tracking, model serving, model deployment, etc. There are several tools and platforms in the market that could help you with these processes and get your work done efficiently with proper risk analysis. However, before you choose a product or platform for your project, you should thoroughly research it. Furthermore, you must ensure that the tools are compatible with the rest of your stack. So, here is the list of popular tools and platforms that you can consider for your project based on the requirements.
MLfLow is a free, open-source platform for automating the machine learning process. MLflow Tracking, MLflow Models, MLflow Projects, and Model Registry are the four main components. MLflow facilitates machine learning development by allowing users to track their experiments, conduct reproducible tests using packaged code, and share and deploy their models. MLflow is compatible with any language, machine learning library, and existing code. It has the flexibility and scalability to suit both individual users and large enterprises. With Apache Spark, it can scale to massive data. It also has a large user community that backs it up.
Kubeflow is a free and open-source platform for developing and deploying machine learning models. It was designed by Google as a machine learning toolkit for Kubernetes, and the Kubeflow community now maintains it. It allows you to quickly deploy models in a variety of formats, including Jupyter notebooks, Docker images, and TensorFlow models. You can run them on your computer, in the cloud, or a Kubernetes cluster. Kubeflow is built to be scalable and versatile. It works with a wide range of programming languages, data processing frameworks, and cloud providers, including AWS, Azure, Google Cloud, Canonical, IBM Cloud, and others.
Metaflow is an open-source MLOps platform for creating and managing large-scale data science initiatives at the business level. This platform allows data scientists to design and deploy machine learning models from start to finish. It works with all prominent data science libraries, including TensorFlow and scikit-learn, so you may continue to use your preferred tool. It automatically versions and tracks all of your tests. You won't lose anything vital, and you'll be able to look through all of the tests' outcomes in notebooks.
Prefect is a modern workflow management application that allows you to construct, execute, and monitor data pipelines to orchestrate data stacks. It is an open-source project management solution that is powered by the Prefect Core workflow engine. It's a full-featured workflow automation platform with a strong data engineering methodology. It outperforms competing for workflow management applications by providing greater test coverage, more unit tests, and high-quality documentation. Prefect comes with a consistent, real-time interface that allows you to keep track of state updates and logs, start new runs, and capture vital data whenever needed.
Comet is a cloud-based meta machine learning platform that lets data scientists and teams track, explain, refine, and compare experiments and models. Code, hyperparameters, metrics, forecasts, dependencies, machine metrics, and more are all supported for project evaluation and comparison. With dedicated modules for vision, audio, written material, and tabular data, it enables sample visualization. It's compatible with today's machine learning libraries.
MLReef is an MLOps platform that allows teams to cooperate and share machine learning experiment results. Projects are built on reusable machine learning modules created by you or the community. Encouraging concurrency speeds up development and improves workflow efficiency. Every developer gets access to script repositories that are containerized and versioned and can be used in machine learning workflows. You can use MLReef to keep track of your experiments throughout iterations of your project.
BentoML is a complete solution for serving machine learning models. It enables Data Science teams to create production-ready models serving endpoints while following DevOps best practices and optimizing performance at each level. It provides a versatile and fast platform for serving, managing, and deploying machine learning models in production. It works with a variety of machine learning frameworks, including PyTorch, Tensorflow, Keras, XGboost, and others. It's a useful model serving tool that allows you to provide high-performance online APIs and offline batches. With the help of a high-performance model server, it offers a flexible workflow.
It is the ultimate model monitoring platform for your MLOps tool stack, as it not only lets you monitor models but also explains their decisions and develops trust in your AI. You may keep an eye on models for drifts, data changes, and performance measures, and notify the model owner when something goes wrong. It can track and measure up to 12 different metrics, including Precision, Recall, Specificity, Sensitivity, and others. It does a root cause analysis to determine what, how, and where all of the model modifications occurred. This tool makes monitoring the pipeline, analyzing difficulties, and explaining models a breeze.
KFServing makes it easier to serve production models with popular machine learning frameworks like XGBoost, TensorFlow, scikit-learn, PyTorch, and ONNX. To deploy ML models on Kubernetes, the tool provides a uniform and user-friendly interface. It uses a Kubernetes Custom Resource Definition (CRD) to provide machine learning models across a wide range of frameworks. The complexity of autoscaling, health checks, networking, and server configuration is abstracted away. This Kubernetes-based technology aids in the administration of serverless workloads.
DVC (Data Version Control)
DVC is one of the useful tools for your data science initiatives. It allows ML models to be replicated and results to be shared across the team. It aids in the management of ML pipeline complexity, allowing you to train the same model several times. It enables teams to keep track of version files for fast reference ML models and their outcomes. It has complete control over Git branches. When datasets are poorly named according to the convention, team members can become confused; DVC can help label datasets accurately. If users require extra RAM, they can use PCs, laptops with GPUs, or cloud resources.
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