The application of machine learning (ML) is no longer exclusive to large multinational corporations. With the rise of ML platforms, businesses of all sizes can now benefit from the power of data-driven insights.
However, with so many machine learning platforms available on the market, it can be difficult to know which one is right for your business. This article will provide a brief overview of what to look for in a machine learning platform and compare the top 10 ML platforms in 2022.
Historically, artificial intelligence models were developed from scratch by expert data scientists. They would spend months building a model before finally deploying it into production. This is because every step in the machine learning lifecycle, from data integration and pre-processing to model development, training, and evaluation required custom coding. Deep learning, or multi-layer neural networks fueled by big data, have especially onerous requirements.
The rise of machine learning platforms has changed all that. A machine learning platform is a tool that enables you to develop and deploy ML models quickly and easily, with little or no coding required. Beyond building machine learning solutions, these apps may also have features for data analytics, data engineering, data visualization, and other related use cases.
There are many benefits to using a machine learning platform. Firstly, it saves you a lot of time. You no longer have to spend months building a model from scratch. Instead, you can use a platform to quickly create and deploy a model.
Secondly, machine learning platforms are easy to use. They are designed for non-experts, which means you don’t need a background in data science to use them. This makes them ideal for businesses that want to use machine learning but don’t have the resources to hire data scientists.
Thirdly, machine learning platforms are versatile. They can be used for a wide range of tasks, including making predictions, analyzing and understanding your customers’ needs, segmenting customers, detecting fraud, and more. This makes them valuable for businesses across a range of industries, from finance to marketing to sales.
For instance, suppose a finance team is facing surging loan defaults. They store historical loan data in a MySQL database. With Akkio’s machine learning platform, the finance team can connect that data in a few clicks, then select the column they want to predict (default) and let the platform do the rest.
In a few minutes, they have a trained machine learning model that can be deployed into production to automatically predict which loans are likely to default. The finance team can then take action to prevent those defaults from occurring.
Prior to the advent of machine learning platforms, such a process would have taken months and required the expertise of data scientists. For example, even building the data pipeline – connecting to the database, extracting the data, and pre-processing it – would have been a significant undertaking. Then, building a robust, scalable machine learning model from that data would have been even harder.
There are many different machine learning platforms on the market, so it can be hard to know which one to choose. Here is a brief overview of some of the top platforms, to help you make a decision.
Keep in mind that we are discussing largely cloud-based machine learning tools, with simple user interfaces to build machine learning algorithms. This is in contrast to machine learning frameworks like PyTorch and Scikit-learn, which are still geared towards data scientists.
Akkio is a completely no-code machine learning platform. This means that you don’t need any technical expertise to use it, and team features make it easy to collaborate between data scientists and non-technical users.
In a world where million-dollar machine learning contracts are being signed, it’s important to have a platform that is affordable. Akkio offers multiple tiers, making it affordable even for small teams and even individuals to get started.
And despite its startup-friendly pricing, Akkio offers enterprise-grade features, including unlimited scalability, direct integrations with big data tools like BigQuery and Snowflake, and support for on-premise deployments.
Google Cloud AI Platform is one of the first machine learning platforms to hit the market. It offers a broad range of features, including data labeling, model training, and prediction serving.
Google Cloud AI Platform is designed for developers and data scientists, so it's not as business user friendly as some of the other platforms on this list.
Large, deep-pocketed organizations that already have in-house expertise with Google Cloud technologies may find this platform fits nicely into their existing tech stack. However, in a head-to-head comparison with Akkio, Akkio comes out on top in terms of model training time, ease of use, and affordability.
Azure is another giant in the world of cloud computing, and its machine learning platform offers similar features to Google Cloud AI Platform.
However, Azure has a more traditional licensing model, which can be expensive for small businesses. And like Google Cloud AI Platform, it's not as user-friendly as some of the other platforms on this list.
In a benchmark study, Azure AutoML was found to have slower training times and higher costs than Akkio, as well. In fact, compared to Akkio, Amazon, and Google, Microsoft was found to have the highest hourly training costs (by far), as well as the highest average training time, without higher accuracy to show for it.
Amazon SageMaker is part of a massive ecosystem of Amazon products and services. This can be both a benefit and a drawback. On the one hand, it's easy to get started with SageMaker if you're already using other Amazon products. On the other hand, it can be difficult to switch to SageMaker if you're using products from other providers.
The traditional SageMaker platform is designed for developers and data scientists. However, Amazon recently released a new product called SageMaker AutoPilot. This product is designed to be more user-friendly and accessible to business users.
In our benchmark analysis, SageMaker's training time was around 70X slower than Akkio, and SageMaker's models had the lowest accuracy among all the platforms tested.
IBM isn't considered one of the "big three" cloud providers, but its Watson platform is a serious contender in the machine learning market.
Watson became well-known for its success in the TV show Jeopardy!, but these days it's being used for much more than that. Watson is being used for things like fraud detection, customer support, and drug discovery.
DataRobot is a fast-growing AI startup competing for the enterprise market. The platform offers both automated machine learning and manual model development for more experienced data scientists.
DataRobot isn't cheap, as it goes for $100,000 a year on the AWS marketplace.
RapidMiner is a popular open-source machine learning platform. It offers a wide range of features and plugins for data pre-processing, modeling, evaluation, and deployment.
According to TrustRadius, pricing ranges from $7,500 per user, per month (similar to DataRobot), all the way to $54,000 per user, per month for the enterprise version, or $648,000 per year. Clearly, that's a hard-sell for any business with a limited budget.
H2O is another open-source machine learning platform that's popular among developers. It costs $10 an hour for the software, plus another $12 an hour for infrastructure costs.
If we normalize this to a full-time equivalent price (2,080 hours a year), it comes out to $45,760 per user, per year, so it's cheaper than RapidMiner or DataRobot, but still not cheap. That pricing, too, is only an estimate of the basic software and infrastructure costs. An H2O pricing list by IBM shows that subscriptions are even pricier than marketplace estimates imply: $300,000 for 3 years, all the way to $850,000 for a 5-year subscription with GPU. This places H2O firmly outside SME affordability.
Dataiku isn't just a machine learning platform - it's a data science platform. This means that it offers a wide range of features for data preparation, modeling, deployment, and collaboration.
However, in order to make use of advanced machine learning features, like a prediction API for unlimited models, you'll need the enterprise version of the platform. The pricing for this isn't public, but we can see that their "Discover" tier, which is two steps below enterprise, is $80,000 a year, so the Enterprise tier is likely to be well into the six-figures or more.
Databricks is an Apache Spark-based platform that offers a wide range of data processing, warehousing, and machine learning features.
Apache Spark is a fast, in-memory data processing engine that's well-suited for big data. However, it can be challenging to use, which is why Databricks offers a managed platform that makes it easy to develop, deploy, and scale Spark-based applications.
Databricks' pricing model is designed for big data workloads, and it starts at $0.20 per Databricks unit per hour - and that's just for the compute resources, not including storage or any other add-ons. For a small business, this quickly becomes prohibitively expensive.
85% of machine learning projects fail, according to Gartner. In order to increase your chances of success, you need to choose the right machine learning platform for your organization.
The causes of failure vary, but they often boil down to a few common factors. One is overly-complex architectures that are difficult to maintain. If non-technical users can't understand how the platform works, or if it's difficult to make changes, then it's likely to fail. All stakeholders need to be able to understand how the system works and how changes will impact the business.
Another common cause of failure is exceeding IT budgets. Machine learning can be expensive, and if you're not careful, it's easy to blow through your budget. Make sure to carefully consider the cost of each platform before making a decision.
Finally, another cause of failure is running behind schedule. This ties in with the issue of complexity - if the platform is too complex, it will take longer to get up and running. Make sure to choose a platform that strikes the right balance between features and ease of use.
Akkio is the best machine learning platform because it's easy to use, scalable, and affordable. It's designed for businesses of all sizes, and its pay-as-you-go pricing model means that you only ever pay for what you use.
If you're looking for a machine learning platform, make sure to consider Akkio. In a recent case study, Sterling Strategies, a campaigning platform, saved 6 months of development time and achieved 5X annual revenue growth by using Akkio for intelligent and accurate donor targeting. Upon conducting a vendor comparison, Sterling Strategies noted that “Akkio had the best models, pricing structure, scalability, and usability.”
Any business can achieve better results, more quickly, by using a machine learning platform. If you’re not using one, you’re at a competitive disadvantage.
Several challenges stand in the way of successful machine learning initiatives, but by choosing the right platform, you can increase your chances of success.
It's important to consider the target audience when choosing a platform. If you're working with non-technical users, then you need a platform that is easy to use and understand.
It's also important to consider cost and timelines, as many platforms cost hundreds of thousands of dollars per year and take months to get up and running.
Akkio is the best machine learning platform because it's easy to use, scalable, and affordable. It's designed for businesses of all sizes, and its pay-as-you-go pricing model means that you only ever pay for what you use. Sign up for a free trial today to see how Akkio can help your business unlock its growth potential, or learn more about how to become a no-code machine learning expert.