Building a Machine Learning Project: AWS vs Microsoft Azure
Discover all the differences between the two dominant players.
Machine learning (ML) is a collection of techniques and approaches that allow programs to “learn” from data without being explicitly programmed. Today the field is evolving rapidly. Examples of ML usage range from healthcare and law to retail and marketing. However, businesses that enter into machine learning development face the challenge of setting up the required infrastructure.
To create and train an ML model, you need special equipment and tools. Purchasing them is not always a good idea due to extremely high costs. For this reason, organizations more often opt for ML via cloud. It provides access to all necessary resources and allows for the faster development of projects. Below we are going to review the benefits of cloud-based machine learning and compare the tools of two leading providers – AWS and Azure.
Five benefits of machine learning in the cloud
Working in the cloud is like living in a rented apartment. You do not need to buy furniture and fittings, you choose the landlord yourself, and you can get in shortly after signing the contract. Rented facilities allow you to deploy a project of any complexity. Also, you can change the provider at any time.
At Erbis, we have been working with cloud technologies for a long time, and here are the benefits we get:
1. Cost savings
Renting cloud services is much cheaper than buying special hardware and software. In addition, you save on related expenses such as:
- Payment for electricity. The data center must have an uninterruptible power supply and work around the clock.
- Staff salary. You would need at least 2-3 people to support the ML infrastructure.
- Safety system. It is vital to set up reliable protection from physical access to the data center and hacker attacks on software.
2. Simple scalability
You can start with any tech set and then expand or, vice versa, reduce resources as your project develops. The big plus of machine learning in the cloud is that you only pay for the resources you actually use. Such an approach frees you from paying for high capacity in advance. Also, you do not need to worry about overpaying because the settlements work based on a pay-as-you-go system.
3. State-of-the-art tools
Machine learning modeling cannot be achieved using outdated equipment and archaic tools. It requires a cutting-edge tech base to build programs that keep pace with modern trends. Tracking the latest innovations, and investing in infrastructure updates, is not economical or efficient for a local company. The wiser move is to rent advanced tools from specialized providers, who constantly improve the cloud services and offer superior ML products to stay one step ahead of their competitors.
4. Easier recruiting
Cloud-based AI and ML tools allow you to reduce demands on data science developers. Considering the salaries of such experts may reach almost $100K per year, you save a vast amount of money and kill two birds with one stone.
First, you do not have to bother finding narrow-focused, expensive, and highly-rated specialists.
Second, you get top-quality software quickly and at a reasonable price.
5. Faster goals achievement
Cloud ML providers free you from inventing specific tools for creating a machine learning model. All you need is to study the available functionality and choose the most appropriate services for your project. The time saved can be spent on other business tasks and project-related matters. You can concentrate on development details and management techniques. The result is that you can release your product faster and reach your goals more quickly.
AWS or Microsoft Azure: which to choose? Pros and Cons
Currently, several providers operate internationally and offer a range of cloud machine learning services. According to Gartner, the most advanced organizations are AWS and Microsoft Azure. Let’s take a closer look at these companies and list their pros and cons.
Amazon Web Services
Today AWS is the largest cloud provider with a presence in almost all world regions, including the U.S., Japan, Europe, Asia, and Latin America. Amazon partners with the US federal government and other big names, which entrust AWS with their security and benefit from high-performance software.
AWS machine learning tools cover various services for big and small businesses. You can work with computer vision, languages, recommendations, and predictions. With AWS SageMaker, you can quickly build, train, and deploy scalable ML models, or create custom models with support for all popular open-source platforms.
Broad array of tools
At Amazon, you can find all the necessary tools for building machine learning models. You can use them both for development from scratch and for the shaping of individual functions.
Extensive server capacity
AWS has enormous space and power capacities, so you can safely deploy the most ambitious projects there. The pay-as-you-go system enables quick scaling up or down to respond to your business needs.
AWS guarantees reliable data protection in the cloud environment. It regularly undertakes security audits to confirm the robustness of its system.
Special skills required
It may be challenging to select the right Amazon services and set them up. You will need a specially trained person who knows how to deal with their instruments.
The pay-per-use system may be more complex than it appears to some users. If that is the case, you could outsource the project to AWS-certified partners, who will help you deal with billing details and choose the most economical tools.
High cost for unique services
Amazon’s machine learning Application Programming Interface (API) offers the broadest set of cloud services. Here you are likely to find tools for even the most complex project, but their price may be higher than you expect.
Azure is another cloud giant that operates globally and covers the American, European, Asian, and Australian regions. Azure provides high-level cloud computing tools and simplifies the creation of cloud apps. It has a well-built infrastructure to cover all machine learning steps from data gathering to model deployment.
Microsoft has put a lot of effort into improving its analytics for deeper insights and smarter predictions. It constantly invests in innovations and looks for new ways to enhance its services. Azure Machine Learning Studio is highly usable and user-friendly. With machine learning fundamentals and a portion of coding skills, you can create a fully-fledged program for your business.
Seamless integration with Microsoft products
While the Azure environment allows you to use various external tools, it has the most straightforward mechanism for linking with the Microsoft suite. If you plan to use it a lot, the Azure cloud should be your choice.
Azure cloud ML has a clear and easy-to-follow interface. You can quickly figure out what’s what and start building your self-learning model.
Flexible hybrid cloud
Microsoft is considered the leader in hybrid hosting. It allows you to host Azure cloud services on local servers with an open management portal, code, and APIs for easy integration.
Microsoft docs do not fully describe how to work with Azure products. To understand specific points, you need to contact support or read forums.
It’s pretty tough to configure the firewall and restrict access to the virtual machine. You need to spend time figuring out all the nuances of configuration.
Few ML models
Compared to R libraries, Azure loses in terms of the number of prebuilt ML models. However, Azure ML Studio supports some R packages, and you can import them by using regular R syntax.
Amazon SageMaker vs. Microsoft Azure Machine Learning Studio
Now that we’ve outlined the pros and cons of the major cloud providers, let’s take a closer look at their AI and ML tools. Below, we’ll name the primary cloud services for creating, training, and deploying smart models and discuss which ones may suit you best.
These are the main services responsible for making the smart programs in the AWS and Azure environments, respectively. Both claim to provide a full tech stack to serve all machine learning phases.
At Erbis, we have worked with both tools and can confirm the accuracy of their claims. However, SageMaker and Azure Studio are as different as apples and oranges in most respects. They occupy different niches, target diverse users, and offer dissimilar means of development. Let’s review their differences and similarities.
1. Way of model building
To work with AWS AI tools, you need deep coding and data science skills. SageMaker gives total freedom and flexibility in creating ML models. You can realize any idea, but to take full advantage of AWS capabilities, you need to be well-versed in Jupiter Notebook and have an expert level in Python. Therefore SageMaker is ideal for experienced developers with deep coding knowledge and strong data engineering expertise.
On the contrary, Azure ML Studio mostly relies on the codeless experience. Its interface offers simple drag-and-drop elements to create a complete ML model with little to no programming skills. You are not required to code using Python or be an expert in some deep data science techniques. The service is geared towards data analysts who like the visual presentation of elements and a simple interface.
2. Logging and monitoring
SageMaker uses CloudWatch to log the model metrics and historical data. CloudWatch translates the received data into a readable format and keeps the records for 15 months. It helps track model behavior and make timely changes or updates.
Azure ML Studio utilizes MLFlow for data recording and monitoring. The overall process is highly intuitive, with visual presentation and graphical elements. For easy recording, you can set up automatic logging, which frees you from the need to log statements explicitly.
When comparing the two services, the Azure mechanism wins in terms of ease of use and the clean appearance of the data display.
3. Artifact logging
It is relatively easy to find artifacts and resources in SageMaker, as they are located in the same bucket and sorted into separate files.
In Azure, everything merges together. Artifacts related to the same model launch are often placed in different locations, so it isn’t easy to find and study them.
4. Customization opportunities
SageMaker is more about coding, and this is its strong point in this case. You can move in any direction while working on your ML creature. With a precise organization of data input, output, and tracking, you can easily assess the accuracy of your model and work on improving and simplifying ML predictions.
On the contrary, the Azure AI API tends to offer prepared templates for speedy development. You can quickly construct the needed model but have less room for creativity. So, the choice of AWS vs. Azure AI tools should derive from the project’s nature.
1. Model training
The training is organized using estimators which are, in fact, Docker containers. With both Amazon and Azure tools, you deploy them to a specific virtual machine or machine learning cloud computing in one or several instances. Such an approach gives high portability. So, if you decide to change your provider, it will be easy for you to migrate.
2. Model deployment
You can deploy the ML model in the API endpoint both with the help of Amazon ML tools or Azure Studio. It is helpful for projects that do not intend to develop a web or mobile interface but concentrate only on creating logic and algorithms that work behind the scenes. For example, you may write a model that determines the likelihood of illness for a person with given parameters. Different hospitals may create mobile clients that will reach your model through the API and display the results in their interface.
3. Workflow setup
SageMaker and ML Studio allow you to build the workflow from independent modules and further group them in a logical sequence of actions. Such a chain of activities is called a pipeline. Thanks to it, you can progress faster in ML model development and be more flexible in terms of scalability. The simplest example of grouping steps into a pipeline may look as follows:
1. Data engineering
2. Model training
3. Model registration
4. Model deployment
How to build an ML model in the cloud
Machine learning penetrates all areas of activity, and companies increasingly invest in AI projects. To develop smart models, you need special tools like Amazon SageMaker or Azure ML Studio. They help quickly develop a self-learning program, but that is only half the battle. Maintenance, support, and re-training require much more attention and expertise. Given this, ML engineers must have vast experience in the AI and ML field and be aware of possible behaviors and the scope of the most common mistakes. Fundamental knowledge is already required at the stage of dataset formation, and engineers should clearly understand the class of problems machine learning solves.
For example, classification tasks require that data be divided so that the numerical ratio of objects of different classes in the resulting set is the same as in the original general totality. On the other hand, regression analysis tasks want the same distribution of the target variable in the resulting sets used for training and quality control.
ML engineers must take into account all these points and many others. They should handle such processes as data cleaning, work with properties, generation, transformation, normalization, and discarding unnecessary variables to exclude multicollinearity of factors and reduce the dimensions of the model.
With machine learning as a service, they can do it faster and with better effect. Cloud providers like Amazon Web Services and Microsoft Azure offer robust capabilities and eliminate the need to set a technological environment locally. Using their tools, developers can create, train, and deploy ML models for different purposes and by various means. Developers may opt for one or another provider based on the project nature, as the features of each ML set may be viewed as advantages or disadvantages depending on the goals set. Don’t forget that AWS and Microsoft provide you only with the tools, and further maintenance and support of ML models requires a more sophisticated approach.