Azure Machine Learning (AML) is a cloud-based machine learning service for data scientists and ML engineers. You can use AML to manage the machine learning lifecycle—train, develop, and test models, but also run MLOps processes with speed, efficiency, and quality.
For organizations that want to scale ML operations and unlock the potential of AI, tools like AML are important. An all-in-one solution like AML simplifies the MLOps process. Creating machine learning solutions that drive business growth becomes much easier.
But what if you don’t need a comprehensive MLOps solution like AML? Maybe you want to build your own stack, and need specific tools for tasks like tracking, deployment, or for managing other key parts of MLOps? You’re in luck – in this article, we’re reviewing multiple alternatives to Azure Machine Learning (AML) for MLOps.
What does Azure ML really do?
To find alternatives for AML, first, we need to analyze exactly what AML does:
- Experiment Tracking
- Model Management
- Model Deployment
- Model Lineage
- Model Monitoring
- Data Labelling
1. Experiment tracking
Experiment tracking documents every piece of information that you care about during your ML experiments. Machine learning is an iterative process, so this is really important.
Azure ML provides experimental tracking for all metrics in the machine learning environment. Setting the AML workspace to pull the required data makes it easier to replicate experiments and compare results.
2. Model Management
Model Management in machine learning is about managing every process in the ML lifecycle from model creation to model deployment. It’s a fundamental part of any ML pipeline. AML is used to manage models in the machine learning project cycle. Effective model management uses log entries and model/data versioning to create a better environment for experiments.
With Azure ML, you can control experiments and enhance collaboration among scientists. This tool records the parameters associated with your experiments in a central location, where you can see each model and reproduce it if you want.
3. Model Deployment
Model deployment makes your models usable for people. Model deployment in machine learning operations often gets complicated and challenging because of the computing power and resources it requires. In this area, Azure ML is particularly helpful.
With the Azure ML Studio, you can deploy your ML models to the cloud, transform them into a web service, and expose them to testing. Additionally, it assists with packaging and debugging your app before sending it to the cloud, and improves the success rate of your deployment.
4. Model and Data Lineage
A big issue for data scientists and ML engineers is feeding accurate data to their models. Azure ML is useful for handling models and data lineage in the machine learning process. Azure ML monitors model history and tracks changes in the data it uses.
You can see how a model—and the data that fuels it—-evolve as they pass through phases in the ML production cycle. Model and data lineage is important because it helps you improve the quality of data used by your production models. This reduces the chances of incorrect predictions, making the model more valuable.
5. Model Monitoring
Efficient model monitoring makes it easier to find problems before your model reaches production. A robust MLOps platform like Azure ML can help you monitor drifts in datasets, historical model performance changes, fairness and bias, and other key indicators.
Production teams are often faced with monitoring the predictive performance of models, as errors could lead to adverse economic impact, a decline in user trust, and increased exposure to risk. The Azure Monitor makes life easier for professions by logging data metrics and activity records. This way, you can closely track your models and know when their predictive performance starts to drop.
6. Data Labeling
Data labeling is the process of tagging raw data for better training. Labeling in AML is good when it comes to creating, managing, and monitoring data labeling projects. It works with images and text data. You can set up labels for image classification, object detection (bounding box), or instance segmentation (polygon).
- Coordinate data, labels, and team members to efficiently manage labeling tasks.
- Tracks progress and maintain the queue of incomplete labeling tasks.
- Start and stop the project and control the labeling progress.
- Review the labeled data and export as an Azure Machine Learning dataset.
Azure Machine Learning is a great suite, but it can be overwhelming if you have very specific needs, or don’t need the options that Azure provides.
In G2 Crowd reviews of the Azure ML, users have mentioned that :
- Azure ML doesn’t meet the requirement for their specific use case; it’s not so flexible and customization can be hard.
- It’s difficult to integrate the data for creating the model with Azure.
- It’s expensive compared to other platforms.
- Its execution speed is quite slow.
- It’s only available online, and you need a strong internet connection to work effectively with Azure Machine Learning Studio.
So, here are multiple alternative tools that you should like!
Tools for experiment tracking
Neptune is an MLOps tool that provides metadata store for machine learning experiments. Beyond logging ML metadata, Neptune is an excellent tool for tracking experiments for improvement. It can help you monitor experiments in progress and debug them to improve performance. Even better, you can share your work with others and get valuable feedback to make your model better.
- Neptune allows data scientists and ML engineers to organize, monitor, and share experiments from a central location.
- With Neptune, you can log any essential data relevant to your ML experiment runs — data versions, parameters, and metrics.
- The beauty of Neptune lies in its flexibility — you can organize and compare multiple ML runs from the same interface.
- It’s programmed to work with popular frameworks, including PyTorch, Pandas, and Python. That means you can integrate Neptune into your daily workflow without any trouble.
MLflow is an open-source platform for managing parts of machine learning workflows such as model building, training, testing, and deployment. It was designed to standardize and unify the machine learning process. It has four major components that help organize different parts of the machine learning lifecycle.
- MLFlow Tracking
- MLFlow Model
- MLFlow Model Registry
- MLFlow Project
MLflow tracking can be used for experiment tracking. MLflow excels at logging parameters, code versions, metrics, and artifacts associated with each run as an experiment tracking tool.
- MLflow tracking offers visualization to compare your results from different runs.
- It integrates well with Java, REST API, R, and Python, so you can easily log and query your experiments.
Weight and Biases (WandB) is a platform for ML developers to track, compare, evaluate, version, visualize and share their machine learning and deep learning experiments.
- It has built-in integrations with popular frameworks (TensorFlow, PyTorch, Keras) and tools (Kubeflow, SageMaker) that make it easy to integrate experiment tracking and data versioning into existing projects.
- Visualizing results with WandB is easier since the user interface is highly intuitive and easy to navigate. You can choose to view the data from your experiments on a dashboard or automatically create a report in Python using the WandB public API.
- WandB is beneficial for teams, thanks to its collaboration-enhancing features.
- WandB stores ML metadata stored in a central location
Experiment tracking tools comparison table
Experiment tracking tool comparison
Tools for model management
As we mentioned earlier, Neptune offers experiment tracking and versioning which is a part of its model management framework. This means you can have a birds-eye-view of your experiments and track the performance of models throughout various testing phases.
Neptune also allows you to log, store, display, organize, compare, and query all metadata generated during the machine learning lifecycle in a secured central repository.
- Neptune provides a model registry for your models;
- When you sign up to Neptune, you’re offered a free managed service option with 100 GB storage, unlimited experiments, private and public projects
- Neptune’s list of integrations is quite extensive and covers most of the popular frameworks.
Metaflow is a python-friendly framework for creating and managing data science workflows and lifecycles. It was developed by Netflix. Metaflow provides a unified API to the infrastructure stack that is required to execute data science projects, from prototype to production. Metaflow uses the dataflow paradigm which models a program as a directed graph of operations and it manages those operations.
It comes equipped with built-in features like:
- Metaflow helps you manage your compute resources(GPUs, etc)
- It manages external dependencies
- version, replay, and resume workflow runs
- Client API to inspect past runs suited for notebooks
Learn more about Metaflow by reading the Metaflow docs.
3. Vertex AI
Vertex AI is the new integrated Google Cloud AI Platform. It’s a unified AI platform for building and deploying models with pre-trained and custom tooling. It can manage your entire machine learning lifecycle. It includes tools that can handle every part of ML development and experiments.
Vertex AI allows you to manage your experiments, models, versions, etc., via the Google Cloud console. It has a GUI where you can access all the options that the Vertex AI provides such as storage management, logging, monitoring, and more.
If you’re a fan of the command line, Vertex AI also provides a command-line tool for performing ML tasks called gcloud command line tool.
Vertex AI Platform provides a REST API for managing your notebooks, workflows, data, model, versions, and hosted prediction models on Google Cloud.
On Vertex AI, you can create virtual machine instances with your notebooks that are pre-packaged with JupyterLab and support for TensorFlow and PyTorch frameworks. It also allows notebook integration with Github.
Vertex AI (Google Cloud AI) provides a set of deep learning virtual machine images that are optimized for machine learning and data science tasks.
Although the vertex AI Platform does more than model management, it’s a good model management tool for individuals, tools, and organizations.
Check out the Google Cloud AI documentation to see if it has your use case.
Model management tools comparison table
|TOOLS||MODEL REGISTRY||STORAGE MANAGEMENT||RESOURCE MANAGEMENT|
Model management tool comparison
Learn more about model management tools
Tools for deployment
Streamlit is an open-source python library for creating, managing, and deploying custom web apps for your Python data science projects and machine learning models.
It requires that your project is on a public GitHub repo, then you sign into streamlit, click the deploy app option, and then paste in the GitHub repo. And in minutes streamlit can convert your code into a web app.
With Streamlit you can create the frontend of the app with simple python code and it also gives templates for creating the frontend. Streamlit handles the backend for you.
Learn more about the Streamlit in the Streamlit docs.
TensorFlow Serving is a Google-backed platform for deploying machine learning models and testing their functionality in a real-life environment. Although TensorFlow has built-in integration with TensorFlow models, it can work with other models.
With TensorFlow Serving, you can train and serve multiple machine learning models using the same architecture and APIs. That streamlines your workflow and takes the pain out of model deployment and iteration.
TensorFlow key benefits:
- Manages model versioning and control
- Compatible with large models up to 2GB
- Used at top companies and corporations
- Flexible API improves integration with existing architectures
Check out the Tensorflow Serving documentation to learn more.
TorchServe is a flexible and beginner-friendly tool for deploying PyTorch models. It’s a brainchild of the collaboration between the Pytorch teams and AWS.
If you’re creating your model using the PyTorch framework, then TorchServe is an excellent choice. It has out-of-the-box integration with PyTorch machine learning models and should sync nicely with your workflow.
However, problems may come up for teams or researchers working with other ML environments since TorchServe was designed to serve only PyTorch models. However, that may change with future updates to the tool.
The RESTful API functionality in TorchServe means you can deploy it across different devices. Plus, TorchServe lightens your workload since it comes out of the box with default libraries for executing tasks like object classification, image classification, object detection, and image segmentation.
TorchServe key benefits:
- Model versioning
- Logging metrics
- Promotes scalability
- Simple set up + plenty of helpful resources for new users
To learn more about Touchserve, check out the TorchServe documentation.
Check out more model deployment tools
Model deployment tools comparison table
|TOOLS||MOBILE||WEB||MULTI-MODEL SERVING||DEVICE MANAGEMENT||MODEL TYPE||ENVIRONMENT SUPPORTED||LATENCY|
|Streamlit||✖||✔||✖||Streamlit Cloud config||Python scripts, TF model||Python scripts, heroku||High|
|Tensorflow Serving||✔||✔||✔||Automated||TF models, keras,||Tensorflow||Low|
|Torchserve||✔||✔||✔||CUDA||Pytorch models only||AWS Sagemaker, Kubernetes, Amazon EC2||Low|
Model deployment tool comparison
Tools for model and data lineage
Pachyderm is a data platform that mixes data lineage with end-to-end pipelines on Kubernetes. It brings data-versioned controlled pipeline layers to data science projects and ML experiments. It does data scraping, ingestion, cleaning, munging, wrangling, processing, modeling, and analysis.
Learn more about Pachyderm here.
DVC is an open-source version control system for both models and data. DVC versions input data, configuration, and the code that was initially used to run an experiment. It practically versions every experimental run. It makes use of existing tools such as GIT.
DVC has a built-in way to connect ML steps into a DAG and run the full pipeline end-to-end. With the DVC data pipelines, you can see how models and other data artifacts are built, which promotes reproducibility.
DVC runs on top of any Git repository and is compatible with any standard Git server or provider (GitHub, GitLab, etc). It can integrate with Amazon S3, Microsoft Azure Blob Storage, Google Drive, Google Cloud Storage, Aliyun OSS, SSH/SFTP, HDFS, HTTP, network-attached storage, or disc to store data.
DVC handles caching of intermediate results and does not run a step again if input data or code are the same.
Neptune also tracks, stores metadata and every other parameter information created during the machine learning lifecycle. Neptune allows you to define what data to log before each run and get the information you need to improve your models.
The Neptune environment automatically generates a record of your data and provides insights into changes to your data. Moreover, it logs model checkpoints, which comes in handy for logging your model versions and using them later.
Neptune is easy to integrate with many third-party applications, which is ideal for versioning. It can also process large amounts of data.
Model versioning tool comparison table
|TOOLS||MODEL VERSIONING||DATA VERSIONING||LINEAGE TRACKING|
Model versioning tool comparison | Source: Author
Tools for model monitoring
Amazon SageMaker Model Monitor tracks the quality of production models round-the-clock. It can be programmed to detect changes in the quality of your model and can also track drifts in data quality and bias in model productions.
With Sagemaker Model Monitor, you can diagnose issues with your model early and implement corrective measures. That could be anything from retraining your model to inspecting other areas. You can program these checks to run automatically, which reduces the chances of missing out on a key indicator.
Sagemaker Model Monitor solves a problem in model monitoring: the difficulty of writing code for MM tools. You can implement model monitoring processes in a breeze without writing a line of code. At the same time, Model Monitor is fully customizable and allows you to add custom code for more detailed data collection.
Learn more about Sagemaker Model Monitor here.
Fiddler improves your ML workflow by providing high-performance monitoring for your models in production. This all-in-one toolset can help with the following model monitoring tasks:
- Tracking and analyzing model behavior
- Managing models and datasets
- Fixing inaccurate model predictions
- Monitor performance of machine learning models
Fiddler supercharges your model monitoring with a suite of practical tools. A scheduled alert system allows you to detect model quality issues and locate the problem areas. And you can organize and track these alerts from one interface.
If you want better insights into your model’s behavior, Fiddler has highly developed technology for breaking down model performance to the essential elements. This gives you more information so you can do better retraining.
You can pull data from literally any source with Fiddler, thanks to its multi-integration capability. It’s also possible to incorporate it into your current framework.
3. Seldon Core
Seldon Core is the companion to the Seldon Deploy platform used for deploying machine learning models in Kubernetes. Seldon Core is excellent for managing every aspect of your ML production pipeline and provides monitoring tools for your benefit.
Seldon Core comes out of the box with a dashboard for monitoring model performance, and you can easily track two models to get more insights. You can generate metrics on an as-needed basis, evaluate the quality of model productions, and compare model versions.
Seldon Core key benefits:
- Supports popular ML libraries
- Detect outliers for faster problem assessment
- Available model explainers to analyze model predictions
You can learn more about Seldon here.
Model monitoring tools comparison table
|Amazon Sagemaker Model Monitor||✔||✔||✔|
Model monitoring tool comparison
Tools for data labeling
Hasty is a data labeling/annotation tool for labeling image and video data for ground truth datasets in computer vision projects. It uses machine learning to perform the annotation.
With Hasty, the user has to manually annotate or label about 10 images, then the Hasty tool creates a model to train those images and then uses that model(active class) to automatically annotate subsequent images. This makes Hasty fast.
To get started with Hasty, check it here.
Labelbox is an end-to-end platform for creating and managing quality training datasets for machine learning training. It does image segmentation, image classification, and text classification. Labelbox offers error detection, customer personalization, safety monitoring, and quality assurance. It involves an analytical and automatic iterative process for training and labeling data and making predictions, along with active learning.
Labelbox has pre-made labeling interfaces that you can use. It also allows you to create your own pluggable interface to suit the needs of your data labeling task.
It facilitates human collaboration and management of multiple distributed labeling workforces, so teams across the world can collaborate on the data labeling process, making it an inclusive and diversified effort.
Learn more about Labelbox here.
AWS Sagemaker Ground Truth offers automatic data labeling to the dataset. It uses an active learning model that labels the data and routes any data it cannot accurately label the data scientist. It does data labeling accurately and efficiently.
Sagemaker Ground Truth does for labeling for text, images, video, and 3D cloud points. For text, it does text classification and named entity recognition. For images. It supports image classification, object detection, and semantic segmentation. For videos, it supports video object detection, video object tracking, and video clip classification. For 3D point cloud data, it supports object detection, objection tracking, and semantic segmentation.
SageMaker Ground Truth also gives you access to a workforce of over 500,000 independent data labeling contractors to whom you can send your labeling jobs. For confidential data or special skills, the tool also has pre-screened third-party vendors like iVision, CapeStart Inc., Cogito, and iMerit that can perform data labeling using special and confidential procedures.
Check out the AWS Sagemaker Ground Truth docs.
For more data labeling tools, check also
Data labeling tool comparison table
|TOOLS||TEXT LABELING||IMAGES LABELING||VIDEO LABELING|
|AWS Sagemaker Ground Truth||✔||✔||✔|
Data labeling tool comparison
Azure Machine Learning is still one of the best platforms for scaling MLOps and the drag-drop designer option makes it beginner-friendly. However, if you’re looking for more specific options to integrate into your ML workflow, there are a number of tools you can try out.
For experiment tracking, Neptune.ai, Mlflow, and WandB will serve you well. They all have collaboration compatibility. If you want to deploy a simple data/ML app, Streamlit is a good option and if you already use TensorFlow or PyTorch then Tensorflow Serving, TorchServe would be better tools to use.
For model management, Vertex AI, Neptune.ai, and Metaflow are really great tools for managing your machine learning workflow. For data labeling AWS Sagemaker Ground Truth, Hasty and Labelbox are good for teams looking to scale their data labeling process.
You can check out the MLOps Tools Landscape to see various tools and their specific use case and any of the tools mentioned in this article will work.
The Best MLOps Tools and How to Evaluate Them
12 mins read | Jakub Czakon | Updated August 25th, 2021
In one of our articles—The Best Tools, Libraries, Frameworks and Methodologies that Machine Learning Teams Actually Use – Things We Learned from 41 ML Startups—Jean-Christophe Petkovich, CTO at Acerta, explained how their ML team approaches MLOps.
According to him, there are several ingredients for a complete MLOps system:
- You need to be able to build model artifacts that contain all the information needed to preprocess your data and generate a result.
- Once you can build model artifacts, you have to be able to track the code that builds them, and the data they were trained and tested on.
- You need to keep track of how all three of these things, the models, their code, and their data, are related.
- Once you can track all these things, you can also mark them ready for staging, and production, and run them through a CI/CD process.
- Finally, to actually deploy them at the end of that process, you need some way to spin up a service based on that model artifact.
It’s a great high-level summary of how to successfully implement MLOps in a company. But understanding what is needed in high-level is just a part of the puzzle. The other one is adopting or creating proper tooling that gets things done.
That’s why we’ve compiled a list of the best MLOps tools. We’ve divided them into six categories so you can choose the right tools for your team and for your business. Let’s dig in!Continue reading ->