What is MLOps and Various MLOps Tools (Part 2)
In comparison to general-purpose application programming, machine learning is a relatively recent field of study. Large machine learning initiatives are now supported by both hardware and software, allowing businesses to make smarter decisions, and machine learning tools and solutions have swept the technology landscape. MLOps is a new field that has emerged as a result of this.
In the first part of this article we discussed what MLOps is and now we will discuss various MLOps tools.
MLOps Tools
As you look for a solution that will fit your goals and assist you in implementing MLOps, you’ll see that there are a variety of possibilities. You’ll need to consider open-source vs. proprietary software, as well as SaaS vs. on-premise solutions.
Open-source vs proprietary MLOps tools — Open-source software users are free to read, modify, and distribute the source code for their own purposes. The source code for proprietary software is not available to the general public. Only the firms that generate this software have the ability to change it.
SaaS vs on-premise MLOps tools — Access to programs is provided through software as a service (SaaS). Through the web, users engage with a software interface. In-house hosting is used for on-premise software solutions. This is normally more secure, but the expenses of administering and maintaining the necessary infrastructure are higher.
MLOps have access to all of these possibilities. Your decision should be based on your individual objectives, internal knowledge, and financial constraints.
Top 6 MLOps Tools:
Below are the tools which are widely used to help machine learning projects:
Comet ML automates the tracking of datasets, code changes, experimental histories, and production models for data science teams, resulting in efficiency, transparency, and reproducibility. All of your experiments may be seen and compared in one spot. It works with any machine learning library and for any machine learning task, regardless of where you execute your code. Everyone who wishes to see experiments more easily and make it easier to work on and perform tests.
Features
→ A variety of features for sharing work in a team or group
→ Compatible with existing machine learning libraries
→ Deals with the management of users.
→ Compares experiments in terms of code, hyperparameters, metrics, forecasts, dependencies, system metrics, and more.
→ With distinct modules for vision, audio, text, and tabular data, you can display samples.
→ It comes with a number of integrations that make it simple to link it to other tools.
It is a machine learning platform that allows you to create, train, manage and deploy machine learning models in a production-ready setting. With purpose-built tools like labeling, data preparation, training, tuning, hosting monitoring, and more, it speeds up your research. It removes all of the roadblocks that developers have while attempting to use machine learning.
Amazon SageMaker has roughly 17 built-in machine learning services, with more on the way in the future years. Make sure you understand the fundamentals of AWS because you never know how much allocating those servers will cost you every hour.
Features:
→ It includes a number of machine learning algorithms for training your data (big datasets). This assists in the improvement of your model’s accuracy, scale, and speed.
→ Linear regression, XGBoost, Clustering, and customer segmentation are among the supervised and unsupervised machine learning techniques included in Sagemaker.
→ Modeling, labeling, and deployment are all sped up with the end-to-end ML platform. Based on your data, AutoML features will automatically generate, train, and tune the optimum ML Model.
→ It allows you to integrate APIs and SDKs, making setup simple, and allowing you to access machine learning algorithms from anywhere.
→ It includes over 150 pre-built solutions that you can use right now. This guide will assist you in getting started with Sagemaker.
Azure Machine Learning is a service provided by Microsoft. It is a cloud-based platform for training, deploying, automating, managing, and tracking machine learning models. It can be used for every type of machine learning, from classical to deep learning, and for both supervised and unsupervised learning.
Features:
→ Using automated machine learning, the Azure ML platform supports Python, R, Jupyter Lab, and R studios.
→ The drag-and-drop functionality creates a machine learning environment that is free of code, allowing data scientists to interact more readily.
→ You can train your model on your own computer or on the Azure machine learning cloud workspace.
→ Tensorflow, Scikit-learn, ONNX, and Pytorch are among the open-source tools supported by Azure machine learning. It has its own open-source MLOps platform (Microsoft MLOps).
→ Collaborative notebooks, AutoML, data labeling, MLOps, Hybrid, and multi-cloud support are just a few of the main features.
→ MLOps features that are robust allow for creation and deployment. It’s simple to keep track of and manage your machine learning experiments.
Centralizing knowledge means being able to reproduce, extrapolate, and tailor experiments. Learn how large scale companies like Uber share internal knowledge.
It is a fully handled end-to-end machine learning and data science platform. It has capabilities that make service management easier and smoother. Developers, scientists, and data engineers may all benefit from their machine learning methodology. The platform has a number of features that assist in the management of the machine learning lifecycle. It offers advanced machine learning services, including pre-trained models and a model generation service.
Features:
→ Cloud storage and big-query help you prepare and store your datasets. Then you can use a built-in feature to label your data.
→ You can perform your task without writing any code by using the Auto ML feature with an easy-to-use UI. You can use Google Colab where you can run your notebook for free.
→ Google Cloud Platform supports many open-source frameworks like KubeFlow, Google Colab notebooks, TensorFlow, VM images, trained models, and technical guides.
→ Deployment can be done with Auto ML features, and it can perform real-time actions on your model.
→ Manage and monitor your model and end-to-end workflow with pipelines. You can validate your model with AI explanation and What-if-tool, which helps you know your model outputs, its behavior, and ways to improve your model and data.
5. Metaflow
It is a Python module that assists data scientists and engineers in the development and management of real-world projects. It’s a workspace system dedicated to the management of machine learning lifecycles. It was created by Netflix to help scientists work more efficiently.
Features:
→Netflix and Amazon Web Services (AWS) released Metaflow in 2019, which can interface with SageMaker, Python, and deep learning-based libraries.
→ It provides a uniform API to stack, which is essential to execute data science projects from prototype to production.
→ A data warehouse, which might be a local file or a database, is used to access data.
→ Metaflow is a graphical user interface that assists you in creating a directed acyclic network for your work environment (D-A-G).
→ You keep track of all your experiments, versions, and data after they’ve been deployed in production.
6. Paperspace
The gradient is a machine learning platform developed by Paperspace that may be used for everything from research to production. It allows you to create, track, and collaborate on machine learning models. For handling all of your machine learning experiments, it offers a cloud-hosted design. Because the majority of the workflow is based on NVIDIA GRID, you can expect strong and faster results.
Features:
→ Almost every framework and library you’re using or planning to use is supported by Paperspace Gradient.
→ All of your experiments and resources may be trained, tracked, and monitored on a single platform.
→ GradientCI’s GitHub functionality, which is supported by jupyter notebooks, allows you to combine your machine learning project with a GitHub repo.
→ You will receive free powerful GPUs that you can start with a single click.
→ Create machine learning pipelines using contemporary deterministic techniques. Streamline versioning, tagging, and lifecycle management.
→ Your previous experiments can easily be converted into a deep learning platform.
→ They have NVIDIA M4000, which is a low-cost card, and NVIDIA P5000, which lets you optimize heavy and high-end machine learning workflows. They intend to integrate AMD to improve the machine learning workflow.
How to select the right tool for your project?
Your decision is based on your project’s needs, maturity, and deployment scale. Your project must be well-organized (Cookie Cutter is a good project structuring tool that will help you do that). Before choosing any MLOps solution, ask yourself these below questions:
- It should be user-friendly for data scientists, rather than forcing your data science teams to use specific tools and frameworks.
- It should be installed, configured, and personalized easily.
- It should be easy to integrate with your current platform.
- It should be able to repeat outcomes, whether you’re working with a team, debugging a production failure, or iterating an existing model, reproducibility is crucial.
- It should be scalable, select a platform that satisfies your current demands and can scale in the future for both real-time and batch workloads, providing high-throughput scenarios, expanding automatically with increased traffic, easy cost control, and safe deployment and release processes.
Conclusion
This article is merely a sketch of the MLOps concept, best MLOps tools, and their features. Although each team’s MLOps Pipeline may differ in terms of application situations and industries, the essential concepts remain the same: invest time and money on the knife’s edge.
Deploying machine learning models and putting MLOps processes in place can be difficult. However, in today’s data-driven world, avoiding these difficulties is impossible. More than just the commercial value provided by your current ML project, acquiring MLOps competence will pay off in multiples as your firm builds more and more models.
Remember, you can get started with Comet for free today!
Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. We’re committed to supporting and inspiring developers and engineers from all walks of life.
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