Review: AWS SageMaker vs. Azure ML: Which MLOps Platform Works Best for Businesses?

In an era where organizations are focusing on automating processes to maximize efficiency and output, the value of MLOps in automating various stages of a machine learning model hasn’t escaped the attention of data scientists. Let’s look at how two famous MLOps platforms – AWS SageMaker and Azure ML- differ in cloud compatibility, ease of use, and various other factors.

What is MLOps?

MLOps combines “machine learning” and continuous software development operations and helps data scientists maintain and deploy ML models efficiently and responsibly. It’s a set of processes that automates ML lifecycle algorithms in production. Using MLOps, data scientists, DevOps, and ML engineers deploy a ready-to-launch algorithm to production adhered to business and regulatory compliance.

MLOps platforms offer core functionality like machine learning pipeline management ranging from model training to deployment. Apart from that, specific tools are used exclusively for data collection and labeling.

The ML Process 

Source: https://medium.com/analytics-vidhya/an-introduction-to-automl-8356b6ceb091

Let’s have a detailed look at two MLOps platforms — AWS SageMaker and Azure ML, and pick a winner based on their respective strengths.

AWS SageMaker

Amazon SageMaker is a managed service in

 (AWS) public cloud that simplifies building and sustaining machine learning (ML) models. It automates data preparation, model training, validation, deployment, and monitoring to let data scientists develop ML products. Users of SageMaker can use AWS to build and deploy ML models at scale.

A glance at AWS SageMaker

Azure ML

The 

 Machine Learning service is a cloud-hosted service that enables data scientists and developers to build predictive analytics applications based on many algorithms and datasets. It allows you to quickly build predictive models using historical and real-time data through various methods. However, the service is primarily used to create, train, and deploy predictive models in the cloud, on-premises, or other systems.

A glance at Microsoft Azure ML

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Comparative Overview of Features Offered by AWS SageMaker and Azure ML

	AWS SageMaker	Azure ML
Product Focus	AutoML	AutoML
Supervised and Unsupervised learning	Supports both	Supports both
Data Preparation (Access, manipulation, visualization, engineering, exploration, cleaning)	SageMaker Ground Truth for labellingSageMaker Data Wrangler  for feature engineering SageMaker Processing SageMaker Feature Store SageMaker Clarify	Data labelingIngestion pipelines Azure Synapse for prep & wrangling
Python & R	Supports Python and requires R studio license	Supports both Python & R
SQL and metadata	It is possible to run SQL queries from your SageMaker notebooks using Amazon Athena	Requires Designer
Spark and Scala	Use EMR with Amazon SageMaker	Use Azure Synapse
Model Training	Yes	Yes
Model Registry	Yes	Yes
Model Deployment	Deployment is done with pre-built or custom containers.	Deployment is done with  pre-built or custom containers as managed endpoints Real-time or batch
Data Governance – lineage, audit, permissions	SageMaker has lineage tracking and various encryption options.	Azure has data lineage tracking options available with encryption
Model Performance and Basic Monitoring	Uses invocation metrics, CloudWatch & custom monitoring schedules	Monitoring for latency,  built-in hardware resource
Data and Insights Monitoring	Relies on capture, monitoring model quality & feature attribution	Payload logging for analysis. Drift detection
Model Governance	Uses Model Registry with versions, groups, and associations to train metadata.	Uses a per-workspace registry and is shareable across workspaces
Continuous Delivery	CI/CD templates for SageMaker	Pipeline, CI, and git integrationPayload logging
Self-install Support	Can train models in k8s with kubeflow.	Not present
Scheduling	Possible	Possible but requires more effort
Collaboration Features – “workspaces”, permissions	It has to be configured as is not used by default	Workspaces are available with permissions and security.
Publish endpoint	Internal only	Yes
Feature Store	Yes	Yes
Cloud/SaaS Support	Yes	Yes
Experiments	Yes	Yes
Notebook co-working	Yes	Code only,no shared compute
Pricing	AWS SageMaker prices are typically metered. Read here for more: https://aws.amazon.com/sagemaker/pricing/	Inexpensive licensing cost. Less than $50 a monthRead here for more: https://azure.microsoft.com/en-gb/pricing/details/machine-learning/

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Key Similarities Between AWS SageMaker & Azure ML

Estimators

Model training and prediction refer to the process that uses estimators to predict what will happen. Trainers need a device on which to run – such as VMs or actual hardware devices like GPUs; but in each case, the device must host a Docker container. To pre-process data before its usage, we can alter a little bit of Python code, but for the most part, it’d be pretty easy to swap out an estimator service with one from another vendor if needed.

In some cases, both Amazon and Azure tools are deployed to a specific virtual machine or machine learning cloud computing. Deploying them in this manner ensures greater portability. For instance, you can make changes to your software or services provider by simply migrating your tools and data independently as required.

Deployment

You can deploy the ML model in the API endpoint or some batch transform and scoring with the help of both Amazon AWS tools or Azure Studio. By leveraging Machine Learning and cognitive services, you can model your data and create smarter and more relevant experiences from it.

For example, you can quickly build a model capable of identifying the underlying causes – such as relationship issues or cancer – for behavioral changes in an individual. This way, instead of having ten different mobile clients for ten different hospitals, you can have just one mobile app that is efficient to project this kind of data.

Hyperparameter Tuning

Both the vendors provide hyperparameter tuning as a service. SageMaker provides Random Search and Bayesian Search. Azure Machine Learning provides Random Search, Grid Search, and Bayesian Sampling. While Random search does the job sometimes, both companies offer more sophisticated searching methods based on the current situation.

Additionally, since the release of version 2.1 of SageMaker, you can use an auto-tuning tool for automated search of the best hyper-parameters in the model you choose to deploy using SixSigma quality standards (for optimal parameters under your circumstances).

ML Pipelines

SageMaker and Azure ML enable the creation of ML pipelines from independent modules to club and group them in sequential tasks. Such chains of activities are called pipelines. For instance, the grouping steps in the pipeline can include the feature/data engineering step, model training step, model registration step, and model-deployment step. But how the two are implemented is quite different from each other.

Key Differences Between AWS SageMaker & Azure ML

Logging

SageMaker relies on the CloudWatch Logs feature to log your models’ metrics and historical data for up to 15 months. It gathers the data and converts it into an understandable format. CloudWatch keeps track of model behavior and gives easy solutions to identify any issues or problems related to your model so that you can address them promptly.

On the other hand, Azure ML Studio utilizes MLFlow for more detailed data recording and monitoring that also offers visual presentation and graphical elements — these all are helpful to ensure successful project completion. Azure ML also lets you set up automatic logging, thereby eliminating the need to perform detailed logging and saving time for other tasks.

Azure ML is indeed simpler, more visual, and easy to use between the two, making it the winner in this category.

Artifact Logging

It’s easier to find artifacts and resources in SageMaker as they are located in a bucket separate from other files.

With Azure ML, this becomes a little challenging to manage as it gathers all the artifacts required for a given project into one deployment. This implies that many artifacts could be involved even if they relate to the same part of a project. Clearly, it is not always easy to locate and study-specific and isolated artifacts in this structure.

When comparing the two, AWS SageMaker wins in this category as the artifacts are quickly traceable and easy to find.

Ease of use

SageMaker works more on a ‘do as you go’ approach as it is more about coding, so one can precisely move while working on their ML creation. Having a clear concept of where your data points are coming from, how they are related, and how their values affect each other is paramount to ensure that your machine learning model yields accurate predictions.

Azure ML is more like a drag-and-drop model-building tool that is simple to use and comes with immense productivity. One of the key benefits of using Azure ML over SageMaker is that it offers pre-made templates for speeding up the development process. As a result, you can quickly create a model and use the template to easily get up and running with a specific application or project. The downside, of course, is that there’s less room for creativity for developers and app-creators.

After experiencing both SageMaker in Azure ML, we strongly feel that SageMaker ticks off the boxes of ease of use, customization opportunities, and flexibility which is why SageMaker is the winner here.

Data Input

A SageMaker user must ensure that the data has been split into train, validation, and test datasets before running a training job to be enforced in the future. For some models/algorithm combinations, you can store the data on a local disk rather than using S3.

On the contrary, Azure Machine Learning can be created during a training script via several methods. Azure Machine Learning is a bit different that lets you avoid splitting your data inside the training script if you have small amounts of training data and significant memory resources (RAM).

On the data input front, Azure ML scores over AWS SageMaker, and hence our winner is AzureML.

Developers need to have a solid understanding of Python to use AWS. While many people enjoy the freedom that SageMaker offers, coding in Python isn’t so easy for everyone. You need experience working with it and in Jupyter Notebook and AWS before getting started. Overall, SageMaker is good for experienced developers with deep coding knowledge and data science skills.

Azure ML is more suitable for budding data scientists who want to get started with their first tasks in machine learning. The interface works for non-coders who intend to get their data fed into predictive models with minimal fuss and user confusion. The service offers robust yet straightforward features that allow data scientists to quickly create a range of predictive models without much coding or complex data science knowledge!

Therefore, Azure ML stands out as a clear winner because of the flexibility and learning opportunities it offers to newbies.

Support

In general, we feel Azure ML has a better pipeline, dataset, documentation, framework, containers, feature updates, and ongoing product support over AWS SageMaker. Azure ML offers a full stack of documentation such as tutorials, quick starts, references, and many other resources that help you build, manage, deploy, and access machine learning solutions with ease.

On the other hand, Amazon SageMaker’s documentation is not in-depth and confusing. There seem to be two different APIs available- one high-level and another low-level. While the high-level API has a lot under its umbrella, it takes a certain amount of guesswork to determine how its operations translate to the lower-level API.

Azure ML is undoubtedly the winner when it comes to overall platform support.

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Which Platform Supports Your Use Cases the Best?

It is essential to keep in mind that many use cases for MLOps depend on the projects’ and platforms’ specific usage like performance, stability, scalability, flexibility, and advanced coordination. So it is essential to have well-defined requirements before you start looking at which MLOps solution is best suited for your company’s needs. Learn more about different use cases of AWS SageMaker and AzureMl here:

Use Cases for AWS SageMaker	Use Cases for Azure ML
Business analysis & predictions End-to-end machine learning solutions Speech recognition or speech to text Access control Forecasting demand Outlier Detection	Visual Search Sentiment Analysis Fraud Detection Demand forecasting Customer Churn Prediction Data analytics and model building ERP solutions

Here are some general guidelines you can follow:

AWS SageMaker:

• Use SageMaker if you have more engineers than analysts in your team and if you’re okay to work on a platform under upgrade. However, this means that you’ll likely get better results and get to work with plenty of new features before everyone else.
• Sagemaker is ideal for companies that build human-like conversation flows that require more complex intents and long dialogues.
• Use Sagemaker if you need an all-in-one platform to develop, train, deploy, and serve your models from a single place. If you need to take care of most machine learning-related tasks without overthinking the technical aspects, Sagemaker is the platform for you.

Azure ML:

• Use AzureML if you want an easy and flexible building interface that helps businesses to build, test, and generate advanced analytics based on the data.
• Use Azure ML if you wish to build artificial intelligence (AI) applications that intelligently process and act on data in real-time, which helps businesses achieve greater results through increased speed and efficiency.
• Use Azure ML if you need a wide range of algorithms, frameworks, and container support. Its presence of ML pipelines and no-code designs speeds up the process of workflow development and helps scale applications much quicker.

Special Mention – TensorFlow

When speaking of the MLOps platform, a robust ML production comes into play- TensorFlow. It is a machine learning platform extensively used by coders to help support the development of AI. With this open-source software, one can design their own artificially intelligent programs for various applications in which machine learning techniques are used. It’s a handy toolkit for both novice and experienced developers who want to build the most effective AIs possible, and it’s even available on GitHub at TensorFlow.

With all the buzz around machine learning, it’s clear that the future will be data-driven. Organizations can leverage machine learning capabilities to solve current business challenges and prepare for future business opportunities.

This article went through two popular platforms for machine learning and artificial intelligence development. We looked at their features, similarities, and differences to help make a more informed decision about the best MLOps platform for your requirements. Both are great options for developing and deploying machine learning models, but each has its strengths and weaknesses. AWS Sagemaker is a great platform for building simple models and deploying them in the cloud with minimal setup. However, Azure ML might be a more versatile choice for predictive analytics.

Overall Winner: Azure ML 

Which among AWS SageMaker and Azure ML would you prefer as an MLOps and data visualization tool for your organization? Comment below or let us know on LinkedIn, Twitter, or Facebook. We’d love to hear from you!

Disclaimer: Unless stated otherwise, any information provided in this review does not constitute a recommendation or endorsement for the products listed in the article. All information in this article is provided in good faith; however, we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability, or completeness of products that are reviewed. The viewpoints expressed within the content are solely the author’s and do not reflect the views of Spiceworks Ziff Davis or its affiliates.

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