Entering the world of Machine Learning
What’s your favorite Snapchat filter? Is it the hair-color-changing filter or the one that changes your gender? The former taught me purple hair doesn't suit me. But that’s not the reason why we’re having this discussion. Instead, after reading this article you will have a clear understanding of how businesses are using ML as a service to keep users engaged.
You might already be aware that ML as a service is constantly present in your daily life. Here’s another example: Twitter uses Machine Learning to show you more relevant content. And Instagram uses the same to provide you with a personalized feed. But, while these tech giants can afford to make their own models, you don’t have to worry if you don’t have such deep pockets, because you can outsource the service to MLaaS providers.
MLaaS - why even bother?
One of the main benefits of MLaaS is that its providers have made Machine Learning integrations accessible to users of all budgets and sizes. Using an MLaaS provider means doing the work that goes into building, training, and deploying ML models outside your company. In such a case, you only have to pay for the ML services you use and data storage in the cloud (if you can’t handle it on your servers).
With MLaaS software, you can analyze online reviews, respond to them, manage your customer emails, and listen to what customers say about you... and much more.
After reading this post, you won't only know beyond the basics of MLaaS, but you will also have a clear understanding of the main MLaaS providers out there and the comparative benefits each of them may have for you according to your business's particular needs.
Keep in mind: choosing an MLaaS provider is like choosing any other service provider – you’ve to understand your needs and if the platform can meet those needs. If you're feeling a bit lost in terms of what you need to know, here is some guidance in terms of what steps you need to take to integrate automation into your processes. At the end of the day, whatever choice you make, MLaaS providers make it cost-efficient to employ Machine Learning for your business.
Intrigued? There's more. Besides covering all the questions you had about the benefits of MLaaS, we will also give you an MLaaS vendor map covering major players in the market.
Machine Learning as a Service: definition
With Snapchat’s example, you’d now probably have a clearer idea of what Machine Learning is. Machine Learning (ML) is a subset of Artificial Intelligence (AI) that allows software applications to produce more accurate predictions without being explicitly programmed to do so. Machine Learning algorithms use historical data as input to predict new output values. Naturally, when you buy it as a service, it becomes MLaaS.
By way of a formal definition, Machine Learning as a Service is an umbrella term for several cloud-based platforms that offer tools such as Natural Language Processing, face recognition, data visualization, predictive analysis, data modeling APIs, and Deep Learning.
Essentially, ML comprises three steps that explain its working:
Description - Data is gathered and then described and delivered in charts and graphs.
Prediction - From the data gathered, patterns are traced, and predictions are made based on what’s learned.
Prescription - With analyzed gathered data, you decide what to do with the information.
Just like SaaS (Software as a Service), BaaS (Backend as a Service), or AIaaS (Artificial Intelligence as a Service), MLaaS entails outsourcing the processes involved in integrating Machine Learning into your business to third-party experts and vendors, rather than creating your own.
MLaaS vs AIaaS: What’s the difference?
Before moving any further, know that Machine Learning is a subset of, yet different from, artificial intelligence. In the same way, don’t confuse MLaaS with AIaaS.
Similar to MLaaS, Artificial Intelligence as a Service (AIaaS) is another cloud-based, third-party service. It lets users employ Artificial Intelligence for different purposes.
So how do they differ? While AIaaS can include a service for any task that should be done "intelligently", it often includes rule-based process automation that only mimics human behavior. An AIaaS can only be considered an MLaaS once the process relies on learning how processes are managed.
This way, AIaaS would be focused on solving complex problems through simulation of human behavior, one of the main priorities being the maximization of chances of success. However, MLaaS would operate by using data to train the program in question to recognize certain patterns and, through these learnings, perform particular tasks with maximized accuracy.
Machine Learning has endless possibilities: it has become the latest cutting-edge technology. Think of it as the technical equivalent of sparkling wine - it can only be called Champagne once it meets certain criteria.
What are the benefits of MLaaS for SMEs?
Although building your own ML model can deliver fantastic results, the process takes a lot of time and money. Not to mention, DIY-ing Machine Learning takes teams of data scientists. And, of course, those come at a price tag.
Hence, the chief benefits of MLaaS for businesses are:
- Accessibility - One of the main benefits of MLaaS is that these providers have brought accessibility to the world of ML. MLaaS and AIaaS can be quite expensive without a provider, that’s why it would make more sense for big companies such as Facebook or Twitter to build their own Machine Learning tools and programs.
- Budget-friendly - MLaaS is budget-friendly in comparison with starting things from scratch. Not every company has Google’s or Microsoft’s budget for developing their ML models. For SMBs the ‘pay for what you use’ or ‘pay as you grow’ system offered by most MLaaS providers makes it both budget-friendly and time-effective to integrate Machine Learning in your business while not requiring a team with specific technical capabilities.
- Time - MLaaS can also help you get started quickly. With it, you don’t have to get stuck with putting together the right team of developers and scientists, software installation, and other tedious tasks involved.
Read more about the value chain of Machine Learning.
When not to go for MLaaS in your business
If you are debating whether your business would benefit more from building or buying Machine Learning tools and processes, you must first assess your capabilities in terms of budget, time, and your team’s technical proficiency. According to what these are, you would then be able to choose whichever option best adapts to your needs.
The MLaaS world offers many specific solutions to any complex problems you may want to address – independently of the obstacle, your business may be facing you will find an MLaaS solution that fits like a glove.
However, sometimes it could be more fitting for your needs to develop your own systems in-house rather than outsourcing Machine Learning. It may be a sign that MLaaS isn't the right solution for you if:
- You find that your use case is too specific to outsource Machine Learning services. (E.g.: any use case that requires extracting and analyzing data of a different kind or source each, to then generate one unique prediction)
- You have too much data to import onto an external platform.
- Your data requires a level of security that external sources can't provide.
- You already have in-house software you would not be able to integrate with external software.
- You need a certain extent of data mobility only in-house software would provide.
If you find yourself in either of these situations and you end up deciding to DIY your own Machine Learning software, your main priority should be having all the right resources. After assessing all of your needs with much detail, your main priority should be to hire the right team and skillset, for which you can never have too much information. Otherwise, building your own in-house software system could be tricky.
MLaaS vendor map: the who's who of the MLaaS market
Now you know that regardless of your SMB’s size or budget, there is an MLaaS provider out there that would perfectly fit your needs. The question is: which one? It is easy to feel overwhelmed when trying to choose one - there are many options when choosing an MLaaS provider, but we are here to help you choose the right one for you and your business.
Let’s now make an MLaaS comparison of the most prominent service providers.
Amazon Machine Learning Services
AWS Machine Learning Services let you create ML models without having to dabble with the algorithm. This makes it one of the most automated players in the MLaaS market and a service that’s a good fit for deadline-centered businesses.
Talking about its automated nature further, AWS Machine Learning takes care of all preprocessing operations. For example:
- ML identifies fields that are numerical and categorical.
- It doesn’t require you to select a method for data preprocessing.
- It also selects the ML models – no user interference is needed.
This means that Machine Learning automatically trains and tests a lot of complex models, without you needing to be involved with any complex processes of the workflow or any code, then automatically selecting the most accurate one according to the program’s parameters to use in the final evaluation.
As for data sources, you are at liberty to get it on-demand or upload your data from various sources, including CSV files, Amazon Redshift, Amazon RDS, and more.
Room for improvement
Despite being a great MLaaS option for most users, AWS Machine Learning does have some limitations that are worth keeping in mind:
- The service doesn’t completely support unsupervised learning. This means that despite reducing the hours invested in performing certain tasks, Amazon Machine Learning Services’ ML processes would still require some time invested from your side.
- There are only three options regarding prediction capabilities: binary classification, regression, and multiclass classification.
- If your dataset’s model is too specific, there is a risk that AWS Machine Learning will provide an inaccurate prediction – so it might take some trial and error to get your model right and obtain an accurate prediction.
- Also worth a mention, Amazon’s focus has shifted from the current service to SageMaker, another powerful MLaaS platform. This probably explains why AWS’s documentation hasn’t been updated since 2016.
However, these limitations don’t mean that it's impossible to optimize your business processes through AWS Machine Learning Services. A clear example of this MLaaS’s effectiveness is Aramex launching a new customer service center through AWS Connect. This logistics company was faced with the challenge of centralizing its contact center and customer service operations from its office in Amman. Initially, it was expected that this process would take a minimum of three months, requiring a high level of technical expertise to be able to successfully meet their goal. However, through outsourcing MLaaS, Aramex managed to deploy AWS connect twenty times quicker than the initial estimated time while also optimizing the quality of their services.
In short, the AWS Machine Learning Services gives you a fully automated solution, but one that’s limited in a few ways. Even if implementing this MLaaS in your business will reduce the hours invested in these processes, you will still need to focus some hours on setting up your model and supervising the Machine Learning process.
This one’s another prominent name in the MLaaS vendor map – one that Amazon's current focus is on, as we touched upon above.
Amazon SageMaker is a reliable MLaaS vendor targeted toward data scientists and developers that want to quickly build and deploy ML models without having to code. SageMaker promises to remove the heavy lifting from each step of the Machine Learning process, offering an ‘autopilot’ option which is an automatic building model system that only requires your involvement in the data import stage. Through this ‘Autopilot’ mode, SageMaker addresses the issue of transparency. Through SageMaker’s version of AutoML, users are given complete visibility and control regarding the algorithms and models, this way being able to choose the process and the final model that best suits their needs.
Like Amazon’s other MLaaS platform, SageMaker is also fully automated. The automation extends to building, training, as well as tuning, and deploying models. Additionally, it supports most Deep Learning frameworks such as Apache, Glucon, Keras, TensorFlow, MXNet, and others. One of this MLaaS’s main benefits is that it adapts to what have become the standard tools for data professionals, offering more seamless integration.
SageMaker also allows you to compare the model performance of validation sets across different models, therefore making it easy for data scientists to track different ML experiments at the same time.
Room for improvement
It’s worth pointing out that the platform’s visual interface is somewhat clucky, but its visual nature makes it easy to use, allowing users to create models, manage experiments, and debug.
Another downside to knowing, however, includes SageMaker’s inability to let you schedule training jobs. On top of that, the platform doesn’t allow you to track metrics you log in during training sessions.
Amazon SageMaker requires a certain level of technological or coding background, so it wouldn't be the best option if you are a beginner in these areas. However, with the right team and the right knowledge, SageMaker can effectively optimize your business's processes. ADP, Inc. had an average timeline of two weeks to deploy ML models before using Amazon SageMaker. This meant that fields such as employee turnover and other recruiting matters had a reduced level of effectiveness when aiming to analyze patterns and reduce outcomes. After adopting SageMaker, this estimated timeline went down to one day and the processes involved in ADP’s recruiting department were highly optimized.
Microsoft Azure Machine Learning Studio
Microsoft Azure’s Machine Learning is suitable for both beginner users and pros and businesses of all sizes.
A good selling point is ML Studio’s variety of algorithms. It empowers users with over 100 methods that help with regression, classification, recommendation, Text Analysis, and anomaly detection.
Besides, Microsoft allows access to its Azure AI Gallery (previously Cortana Intelligence Gallery) to users. The gallery is a community-based site where users can research and learn solutions from experiments, tutorials, and training from Azure data.
As for the platform’s ease of use? That’s pretty easy owing to the drag-and-drop interface that doesn’t take a genius to use. With the interface, you don’t have to dig into coding.
Room for improvement
However, this MLaaS is limited to a certain extent when running more complex Machine Learning models, being less suited if you have complex or very large datasets. Its pre-set models are ideal for simple ML automation, but it would be more time-exhaustive and complex to create new models if your business’s needs go beyond these.
Further, the Studio offers only one clustering algorithm, K-means, so if that’s not what you’re looking for, this platform might not be of help.
Despite these limitations, this MLaaS could be ideal for optimizing your processes if your needs stay within its boundaries. A clear example of this is Carhartt’s use of Machine Learning to stay ahead of the industry’s growing competition. The company used highly accurate data insights gained through models and predictions deployed by Azure Machine Learning Studio to determine the locations of its three new stores. These stores exceeded their revenue plans by over 200%, a result which may not have been possible without the integration of ML in their processes.
- This browser-based service is fast, flexible, and scalable.
- Its straightforward drag-and-drop interface means that it is suited to users with all levels of coding expertise.
- The pre-set models Azure Machine Learning Studio offers make it simple and time-effective to train your Machine learning models.
- However, if your needs go beyond these pre-set models it may not be the best option for you as it would be more costly and complex to create a new model on this MLaaS platform.
Google Cloud AutoML
Like Amazon, Google’s ML service also comes on two levels: Google Cloud AutoML and Google Cloud Machine Learning. The former is best for beginner-level ML users.
You can upload your datasets, train models, and deploy them with Cloud AutoML’s graphical interface. Just as Azure with Microsoft, Google Cloud AutoML depends on being connected to other Google tools to a certain extent.
This MLaaS platform would be suited for you if your use case relates to:
- Churn analysis.
- Image and video processing services.
- Training models on structured data.
- Natural Language Processing and translation engine.
The process will be more time-effective and will need less human supervision when working with structured data than when importing unstructured data. However, Cloud AutoML deploys highly accurate models and predictions with either kind of dataset.
The best part? Google Cloud AutoML offers highly accurate deep Neural Networks for sifting through your data. This means that instead of having to start from the base to train your models, Cloud AutoML uses automatic Neural Architecture Search (NAS) and deep Transfer Learning for training models so that you work on pre-trained services.
Hence, the main benefit of this MLaaS platform is that you work with pre-trained services based on Google’s pre-existing labeled data and deep Neural Networks. This MLaaS provider is simple to use even if you don’t have any ML or coding expertise, achieving this without having to sacrifice customizability for accessibility.
Google Cloud AutoML is composed of a variety of category branches, each depending on the use case involved when adopting this MLaaS provider. This way, you get the Machine Learning models and processes that best suit your needs.
Room for improvement
Despite these offerings, there’s a common complaint that Cloud AutoML isn’t cost-effective when you scale. If you plan on high volumes of use, you might end up spending a lot on this vendor with its pay-per-user model.
Google Cloud Machine Learning Engine
This one’s Google’s MLaaS engine for experienced data scientists, helping them build superior learning models, known as Cloud MLE.
It supports multiple frameworks, including TensorFlow, Keras, scikit-learn, and XGBoost. And, it’s integrated with all Google services, including Google Cloud Speech and Google Photos.
As for what Cloud ML can help you with: it shines in training, prediction, and data classification.
Room for improvement
That said, Google Cloud Machine Learning Engine is in beta as of September 2018. Besides, all its frameworks are written in Python, which means you need to have some proficiency in Data Science to be able to use them.
You’ll need to learn Neural Networks, advanced statistics, linear algebra, gradient descent, regression, and more. This is why this MLaaS provision from Google is mainly for data scientists, not beginners.
IBM Watson Machine Learning Studio
Watson brings a fully automated ML service to the table, which reduces the learning curve and takes no prior training to use. This makes the ML Studio a good fit for beginners and experienced individuals for building, training, and deploying models.
With this service’s visual modeling nature, you can readily identify patterns in data, get valuable insights from it, and make decisions faster.
The good news is that experts can skip to using IBM Watson Machine Learning Studio manually too. With the automated engine, you can handle three main tasks:
- Multiclass classification
- Binary classification
The adoption of this MLaaS enabled KIST Europe to optimize the quality management processes at their factories. By implementing automated models and predictions to collect and analyze data collected from scales and other equipment, KIST Europe improved its production line and quality control efficiency without needing to involve coding experts. They saved weeks of development by not having to import and process data manually, with a 98% accuracy rate that meant their production performance became higher than ever.
For manual users, IBM Watson ML Studio offers two more model-building services. These are:
- SPSS Modeler - This is a software package that helps you get statistical data from raw data. Previously offered as a stand-alone service, SPSS Modeler is currently available as a product that lets you upload data, manipulate it using SQL statements, and train models.
- Neural Network Modeler - It offers Neural Network models that let you process visual and textual data. Using a flow editor, the neural modeler helps you train and deploy models and move data between datasets. But be warned: the initial setup can be challenging. The concern only aggravates with the lack of free resources to guide users on the Studio setup and use.
Room for improvement
This MLaaS is quite versatile, as it offers the option to train the system either locally or on the cloud. This means that in terms of data security it is easy to keep your information private when building and training your models. However, this becomes a bit more complicated when deploying your model on the cloud.
IBM Watson Machine Learning Studio offers a solution that is just as easy to use for beginners as for code experts. Its versatility, both in manual or automated model building and local or cloud-based usage, make it a viable solution for all kinds of users. However, some complain that it may be relatively complex for a beginner to use with no support. This MLaaS provider offers solutions for those facing these issues, however, some of them may be a bit costly and result in a less budget-effective process.
Lastly, Levity AI is a drag-and-drop MLaaS provider that’s very easy to use and doesn’t require any coding. On top of that, the clean UX and in-depth resources, including use cases on the site, make it easy to understand how to use it. Levity offers an easy-to-use interface with easy-to-access support, making it one of the best options if you have no previous coding experience.
That said, Levity is suitable for businesses of all sizes. Levity brings accessibility to the Machine Learning space, offering a personalized scalable payment plan that means you only pay for as much as you need to use.
Levity operates through a product-led approach, meaning that when booking your access call the particular needs and use case of your business will be assessed and analyzed to make sure that you are able to build and train your processes in the most personalized way possible. You will be able to integrate your workflows, as many others have already done, with the main data-processing tools out there, facing little to no limitations on this aspect.
Working with unstructured data, Levity helps with the following:
- Sentiment Analysis
- Image and data classification
- Natural Language Processing
Through training your own workflows through Levity AI, you would be able to dramatically reduce the hours spent on mindless work and optimize the effectiveness of your processes. For example, by importing accumulated data from your Gmail inbox, or any other source, to the platform and training your own custom model on it, you could reduce hundreds of hours that would otherwise be spent opening and analyzing each relevant piece of data. Audibene has already optimized its processes by integrating this MLaaS into its business without needing to involve technical workers in the process.
Levity’s Machine Learning platform will allow you to build end-to-end processes with complex logic without having to use a single line of code, making it the best option for non-developers in small and medium-sized businesses that want to automate processes in the most straightforward way. Levity offers more customizability than other platforms, as well as supporting processes that require highly complex logic. In terms of data transparency, while operating on the cloud, you can access Levity’s own API so you are always in the loop with how your data is being processed.
Room for improvement
No one's perfect, and neither are we. Levity currently focuses purely on classification problems - and creative problem-solving. It's not always easy to change your workflows to move towards a more automated infrastructure - but we are here to help you figure out how to bring you unlocked value! Just reach out to us on Twitter or shoot us a message in the chat widget - can't wait to hear from you!
With this rundown of what MLaaS is and an MLaaS comparison, here’s hoping you have a clear idea of how outsourcing the work can benefit you. If your Data Science knowledge isn’t extensive, you’ll want to pick a no-code provider that’s easy to use and integrates with your current systems too. This way, you can focus on what’s important to you – employing ML models to improve your business rather than taking a crash course in Data Science and coding.
The question now is: how does AI benefit businesses? The short answer is: AI helps increase revenue. How? In some of the following ways, the benefits of MLaaS do not end here either:
- Helping companies offer personalized services to their customers.
- Making predictions that can help companies plan their strategies.
- Helping improve products and offerings, among other things.
For example, Machine Learning can help salespeople close more deals by giving them a score for each prospect. Based on this lead score, sales reps can pursue deals that are more likely to close.
Instead of developing such complex Machine Learning processes from scratch, it helps to do things cost and time-effectively by outsourcing the service to MLaaS. Whichever issue you are trying to solve, there is an MLaaS provider out there that suits your needs. After reading this article you shouldn’t need much more than an access call with your provider of choice to find out the specific ways in which Machine Learning can help your business.