End-to-end MLOps tools are a mixed bag. Some of us engineers like a tool that connects every resource, plugin, integration, ML operation, and automates it all. Others not so much.
ClearML is a popular end-to-end platform that connects all data science tools in a unified environment. It’s an open-source suite of tools to automate preparing, executing, and analyzing machine learning experiments. Experiment management tools keep track of parameters, jobs, artifacts, metrics, debug data, metadata, and log it all in one clear interface. It’s a heavyweight tool that has it all.
The tech industry heavyweights like Microsoft, Facebook, Intel, Samsung, Amazon, Sony, and Nvidia use ClearML to run their machine learning and data science operations. But does that make ClearML the best choice?
In this article, we’ll explore why engineers at big companies like ClearML, and analyze 10 tools that provide different levels of functionality for the demanding ML engineer or data scientist.
What is ClearML?
ClearML eliminates the time-consuming and error-prone tasks associated with the full machine learning lifecycle. ML developers and data scientists can focus on data and training only.
It tracks everything we need to document our work, visualize results, reproduce, tune, and compare experiments. As a result, we can implement automated workflows, like hyperparameter optimization and other pipelines.
The Python-based package of ClearML supports:
- frameworks like TensorFlow/TensorBoard, PyTorch, Keras, Fastai, Scikit-learn.
- libraries (Pandas, Plotly, AutoKeras).
- visualization tools (Matplotlib, Seaborn).
- storage (file systems, S3, Google Cloud Storage, and Azure Storage).
We can use the hosted service and deploy a self-hosted ClearML server. It improves ML development with classes for experiments, explicit reporting, workflow automation, optimization (Optuna, HpBandster, random, grid, and custom search strategies), models, and storage.
The three most prominent features of ClearML are:
- ClearML Orchestration,
- ClearML Feature Store,
- and ClearML Deploy.
ClearML Orchestrate gives data science teams autonomy and control over computing resources in one, simple dashboard. It manages resources and cluster allocation without dedicated MLOps team members. The Orchestration tool is designed to scale to your needs, it can scale dynamically to accommodate thousands of GPUs. It abstracts the workload from the infrastructure. You only do the initial set-up, and then all processes are controlled by the tool automatically. You can self-serve our own resource scheduling through a simple interface. You get complete control of the costs and AI / ML workloads, with a tool that’s part of the complete ClearML suite. It keeps the utilization rates high, and saves money on-prem or in the cloud. Not to mention, it manages cloud bursting to provide compute power when we need it, all while controlling costs.
ClearML Feature Store
ClearML’s Feature Store, plus the automated pipelines, enable super speed for ML operations. You can build pipelines and experiments for structured and unstructured data in a matter of minutes. Simply plug in the ClearML Feature Store and add a simple way to manage and iterate on features for ML experiments. The store can be integrated with just a few lines of code. The result can organize both structured and unstructured DL and ML operations. You can ingest data of any type – images and videos, LiDAR, audio, IoT sensors, FLIR, and more. Integrate your structured data sources with sensor-based data for more comprehensive AI modeling.
ClearML Feature Store handles all of the work around ingesting structure and unstructured data from any source into the feature store. The flexibility is massive. ClearML Orchestrate & ClearML Experiment combine with Feature Store to create an integrated data feedback loop to re-train deployed models based on real-world data. Features can be served and deployed to the production environment easily. You can also monitor breakage and drift, which positively impacts accurate training data sets using time travel/data versioning.
ClearML deploys models to any environment, and also offers complete control over them. The Deploy module connects with Experiment and Orchestrate for a complete workflow that anyone can use. It offers a wide range of tools to deploy models. The target environment to deploy can be modified with a single click. It handles all deployment operations, so you can concentrate on training accurate models. With ClearML, you deploy easily across cloud, on-premise, and burstable environments, with your preferred set of tools and configurations. Managing the infrastructure is too cool for us when we can run live tests. We can run and deploy critical ML applications simply and repeatedly. The module offers complete visibility of model consumption, call details, and server use for any size deployment. Integrated with CI/CD, ClearML can scale flexibly and dynamically.
And that’s ClearML for you. But not all of us work at FAANGs or use ClearML. And even if you do, sometimes you still might need other tools.
So, here are 10 tools that can serve as a great alternative to the different parts of ClearML.
10 lightweight tool alternatives to ClearML functionalities
Algorithmia is an enterprise MLOps platform. You can use it to deliver models quickly and securely, so that data scientists can focus on machine learning processes. Algorithmia quickly migrates all the requirements and processes for production, and does it in a cost-effective way.
When it comes to model deployment, it supports up to 3900+ frameworks and tooling. It works with existing tools and tech stacks for ML development. You can deploy your model wherever you want – in the cloud, on-premise, or in a hybrid environment.
It covers all the stages of the ML lifecycle, and automates deployment and proper versioning. It also facilitates a metrics pipeline that supports performance monitoring needs, and integrates with monitoring, reporting, and alerting tools.
If you’re looking for an alternative that provides end-to-end machine learning pipelines and automates model building and deployment with optimum scalability, then Cnvrg is the right platform for you.
Cnvrg scales machine learning development from research to production. It has all the tools that you need for the machine learning process in one place, so that the environment is more productive for data scientists.
It has a unified platform UI for science and engineering to collaborate and create an efficient machine learning development environment. The automation of model management takes away the pain of technical complexity, so you can focus more on data science tasks.
The container-based infrastructure simplifies complex tasks like tracking, monitoring, configuration, resource management, serving infrastructure, feature extraction, and model deployment. You can leverage the cloud and on-premise resources to build enterprise-ready machine learning models. It accelerates AI development with modularity, rapid experimentation, and production-ready infrastructure with native Kubernetes cluster orchestration and meta-scheduler.
Valohai is an MLOps tool that automates everything – data extraction, preparation, all the way to model deployment in production. You can train, evaluate and deploy models efficiently and conveniently, without any extra manual work – and then you can repeat the process automatically.
Valohai facilitates an end-to-end machine learning workflow that stores models, experiments, and artifacts automatically. It also manages the monitoring of deployed models in the Kubernetes cluster.
It has a super stable MLOps environment, framework and tooling adaptation, hyperparameter sweeps, team collaboration environment, secure environment with firewall, and auditing of experiments and models.
Developers and scientists can focus on custom model development instead of infrastructure and manual tracking. Valohai speeds up your work, and also supports automatic versioning of data, models, and experiments.
Polyaxon can be a perfect alternative to Clear.ml. It automates machine learning operations and infrastructure to reproduce, automate, and scale data science workflows with production-grade MLOps tools. It has an accelerated, iterative approach to research and model creation. Experimentation is more convenient without the manual infrastructure work, and you have an unlimited choice of frameworks and tools. There’s also an interactive workspace with notebooks, visualization, and dashboards.
You get effective user management with collaboration space, resource allocation, versioning and reproducibility, data autonomy, hyperparameter search & optimization, maximum resource utilization, powerful interface, scalable infrastructure, modularity, scalability, cost efficiency, and more. It’s a pretty rich package.
Pachyderm is a dynamic data science platform that leverages data lineage with end-to-end ML pipelines on Kubernetes. It’s available as open-source with minimum available features, and there’s a premium enterprise plan with maximum features.
You can build and deploy ML workflows using any framework and tool. You get state-of-the-art enterprise-grade support, a rich UI dashboard, advanced statistics, TLS, user access management, and custom deployments. It focuses on data; providing data scientists a scalable and flexible approach to leverage resources and work on research and experimentation.
The whole platform framework is based on containers, which makes data environments portable and easy to migrate to different cloud providers. The tool is built on top of Kubernetes, which gives you a path to production with constant ML process automation and monitoring.
👉 Check the best Pachyderm alternatives
Neptune is a centralized platform to manage all model building metadata, in other words, we can call it a metadata store. This tool logs, stores, displays, organizes, compares, and queries all your MLOps metadata. It’s a state-of-the-art platform for tracking machine learning experiments, logging metrics, analyzing performance charts, and much more.
You get a centralized, collaborative hub to organize teamwork in an organic way. You can customize the UI, and manage users in cloud or on-premise environments. Control user permissions and access quickly and with ease. For more productivity, Neptune monitors hardware resources to optimize the code, so that your resources can be utilized to the fullest potential. Neptune is highly scalable, and integrates with your favorite frameworks and tools.
In Neptune, you can log and study any machine learning metadata that you need, and organize experiments and model training in a unified hub. Compare models and experiments easily, monitor ML experiments in real-time while they’re executing. Produced models are traceable and reproducible. The UI is super rich with team collaboration, dashboard customization, result visualization, sharing, workspace, and a project structuring mechanism. All in all, it’s a really cool tool.
H2O is an open-source platform to make state-of-the-art models and machine learning applications. It’s an end-to-end platform that democratizes artificial intelligence, empowering everyone with sophisticated AI technology and easy-to-use AI applications. The AI hybrid cloud facilitates a unified platform for thousands of use-cases. The models you build are scalable from any on-premise infrastructure to cloud infrastructure.
H2O services and processes are easily customizable and extensible. It’s a top-quality free tool that gives you data manipulation, various algorithms, cross-validation, grid search for hyperparameter tuning, feature ranking, and model serialization. The driverless AI helps you work on machine learning projects faster and more efficiently by using automation.
It supports the most powerful and dynamic statistical & machine learning algorithms. The distributed processing on big data provides tremendous speed with fine-grain parallelism, enabling optimal efficiency. The graphical notebook called Flow lets us build models without even having to code, but we can also use languages like R and Python.
Datarobot is an AI platform that accelerates and automates the machine learning process, efficiently converting data to value. You can prepare the data, perform experiments, then build and validate machine learning models. Time-series models are also supported.
After deployment, Datarobot does monitoring-as-a-service. The overall model development process becomes simpler and more efficient, with rapid iterations of numerous models, data preparation steps, and parameters. The centralized, AI-powered UI platform drives better machine learning outcomes. Datarobot is extensible, we can use the cloud platform or on-premise environment with completely managed services.
The key features the platform provides are data preparation and exploration, automated model creation and explainable AI, seamless and automated machine learning operations, building business applications, and use-case tracking.
Dataiku is a powerful platform that democratizes access to data, enabling enterprises to create their own path to AI in a human-centric way. You get tools and services for data preparation, visualization, machine learning, data ops, MLOps, and more.
There’s also an analytics app for data and model analysis. The main focus is on enterprise-class collaboration, governance, explainability, and architecture. The visual flow service lets you build data pipelines with datasets, combine and transform datasets, and build predictive models.
Dataiku’s ML feature fills the missing values and converts non-numeric data into numerical values, automatically using well-established encoding techniques. There’s a centralized hub to start projects and collaborate across the development team. The permission control feature can be used to control user access to different services. The visual flow employs an advanced canvas for collaboration purposes.
The platform offers critical capabilities for explainable AI, including reports on feature importance, partial dependence plots, subpopulation analysis, and individual prediction explanations.
Iguazio is a data science platform with end-to-end automation of machine learning pipelines, converting AI solutions to real-world business solutions. State-of-the-art solutions accelerate and scale machine learning development, deployment, and management.
You can automate the entire ML pipeline, from data collection, preparation and training, to rapid deployment and ongoing monitoring in production. You can ingest data from any source, and build reusable solutions online and offline. Iguazio uses KubeFlow for workflow orchestration.
The platform UI executes experimentation over scalable serverless ML/DL runtimes, with automated tracking, data versioning, and continuous integration/delivery (CI/CD) support. The efficient architecture helps deploy models and APIs from a Jupyter notebook or IDE to production in just a few clicks, followed by continuously monitoring model performance and mitigating model drift.
Clear.ml is best-in-class among many data science tools, and it’s open-source as well. But, these tools mentioned above are no less they are just as useful in different use cases, and they can be the right alternative to clear.ml for you.
The automatic processes, scalability, unified design, global plugin integrations, and other key features make these tools very efficient and effective for driving machine learning projects to create accurate models to deploy. You can create quality datasets and develop state-of-the-art models that can be easily converted to business outcomes using the alternative and powerful MLOps tools.
15 Best Tools for Tracking Machine Learning Experiments
Pawel Kijko | Posted February 17, 2020
While working on a machine learning project, getting good results from a single model-training run is one thing, but keeping all of your machine learning experiments organized and having a process that lets you draw valid conclusions from them is quite another. That’s what machine learning experiment management helps with.
In this article, I will explain why you, as data scientists and machine learning engineers, need a tool for tracking machine learning experiments and what is the best software you can use for that.
Tools for tracking machine learning experiments – who needs them and why?
- Data Scientists: In many organizations, machine learning engineers and data scientists tend to work alone. That makes some people think that keeping track of their experimentation process is not that important as long as they can deliver that one last model. This is true to an extent, but when you want to come back to an idea, re-run a model from a couple of months ago or simply compare and visualize the differences between runs, the need for a system or tool for tracking ML experiments becomes (painfully) apparent.
- Teams of Data Scientists: A specialized tool for tracking ML experiments is even more useful for the whole team of data scientists. It allows them to see what others are doing, share the ideas and insights, store experiment metadata, retrieve it at any time and analyze it whenever they need to. It makes the teamwork much more efficient, prevents situations where several people work on the same task, and makes onboarding of new members way easier.
- Managers/Business people: tracking software creates an opportunity to involve other team members like managers or business stakeholder in your machine learning projects. Thanks to the possibility to prepare visualizations, add comments and share the work, managers and co-workers can easily track the progress and cooperate with the machine learning team.
Here is an in-depth article about experiment management for those of you who want to learn more.Continue reading ->