What Is a Machine Learning Platform?

 

Machine Learning (ML) and Artificial Intelligence (AI) applications are gaining in popularity and are being adopted in all kinds of interesting ways. AI applications analyze huge amounts of data to provide a wide variety of discoveries including new drugs, out-performing financial products, how to make IoTs smarter, and recommendations such as how to increase business process efficiencies, lower operational costsimprove marketing campaign effectiveness , reduce customer churnimprove customer satisfaction, or even how to better fight the rise of on-demand streaming services.

However, AI applications are not just about choosing a sophisticated algorithm and feeding it data to discover new insights. For an AI application to properly predict actionable outcomes, it needs to be properly set-up and deployed with the help of a robust AI platform. Choosing the right AI platform can help you onboard your AI application in just a few weeks and immediately reap its benefits, as opposed to taking months or even years with all the risks and hidden costs involved. In fact, one could convincingly argue that the platform on which your AI application is conceived and deployed is more important that the AI algorithms themselves.

A platform can loosely be defined as a Cloud architecture or software framework used to build and run applications, such as in our case, AI applications. But an AI platform is much more than that. It needs to be an end-to-end Cloud-Native platform, accessible to all team members working on your AI application, and it needs to take away the complexity of creating, training, evaluating and deploying several AI applications while making sure your data is well managed and processes are automated. To better understand how important an AI platform is, let’s take a look at the features that make a perfect AI platform.

ML Models

Let’s start with the models you will use in your ML/AI application to analyze your data and find the patterns and insights that will help you make appropriate business decisions. These can be Machine Learning  and Deep Learning algorithms, or even simpler statistical models. A good AI platform should allow you to choose from a list of popular models, import your preferred models, or even build your own – and compare the different models. You should not need to know all the different intricacies of the models you’re using to properly learn from your data. The platform should guide you through how to set-up your models, train and test them, score them, and chose the best one to deploy.

Data Extraction

The AI platform you should consider must allow you to easily set-up and manage your complete data-cycle from end-to-end, along the complete AI Pipeline: It should offer automated features to extract your data and store it, manage your learning models, deploy them and offer an application layer to visualize the outcomes. The platform should enable your models to continuously learn with every new data input, and adjust themselves to the new information.

AI Pipeline

The AI platform you should consider must allow you to easily set-up and manage your complete data-cycle from end-to-end, along the complete AI Pipeline: It should offer automated features to extract your data and store it, manage your learning models, deploy them and offer an application layer to visualize the outcomes. The platform should enable your models to continuously learn with every new data input, and adjust themselves to the new information.

Ecosystem

An important factor in choosing the right AI platform is its ecosystem – a sort of free market place or toolshed, where you can pick and choose the different tools you will use throughout the AI Pipeline. You should be able to choose the Cloud infrastructure you will run your project on, pick the connectors and functions to retrieve and transform your data, pick your AI models, create your end-user application from a list of visual tools from within the AI Platform. These tools should be ready-to-use, popular third-party tools, templates and functions. You should also be able to add your own specialty tools and create and share new ones.

Powerful Workbench

The AI platform you choose, should offer an easy-to-use, yet power workbench, from which you can create your AI infrastructure and AI application and manage your complete AI Pipeline – from data collection to consumption, within just one interface. All the functions you need should be in one place. You should not have to use different applications for different tasks, make uncertain technological decisions or bend over backwards to make your tools work together. The workbench should allow you to set-up automated processes with ease, including alarms and triggers.

Visual Interface

Your AI platform’s workbench should have an intuitive interface, that doesn’t require special skills to use. It should provide a drag-&-drop, and point-&-click interface with a high-level abstraction that hides all back-end complexities. It should also provide feedback to the users through cockpits and graphical interfaces that show which jobs are running, how many resources are used, and how well your models are performing.

End-User Application Builder

The AI platform you settle for, should allow you to easily create sophisticated and visually attractive end-user interfaces for your AI application – to share the results of your predictions to your business users. These can be in the form of input controls and fields, navigation components, icons, images, informational components such as graphs and tables, and more sophisticated UI elements that allow users to change certain parameters on the fly, and obtain the insights they’re looking for. The application builder should be easy-to-use and intuitive and not require special UI interface skills to use.

Cloud-Native

Your AI/ML platform should be Cloud-Native, and allow you to fully exploit the benefits of a Cloud infrastructure. The platform should be a Platform-as-a-Service (PaaS) to be highly reliable, redundant, scalable and secure, allowing your AI application to thrive on any public, private or multi-cloud. Your users should focus on creating value instead of wasting time on managing the infrastructure (software and hardware set-up and maintenance), or deal with security challenges. Your platform should provide the scalability you need, when you need it. It should allow your AI application to easily grow, by automatically accessing the resources your project needs as it needs them, and you should only pay for the resources you use.

Collaborative

As you can probably tell from the list above, many different people will be working on your AI application. You should be able to invite different skills
and encourage your teammates to collaborate and work together: from data engineers and data scientists, to UI experts, system and security administrators and even your finance department. It will prevent duplication of effort, avoid communication breakdowns and forgotten tasks. All your team members should be interacting with the same processes, the same datasets through the same workbench.

Blueprint driven

To further help collaboration, and improve project effectiveness and speed, your AI platform should allow you to capture your business processes, best practices and industry rules and save them as reusable blueprints. You should be able to share them with your team, your partners and your customers to reap full benefits of your AI application. These blueprints should be automated once in production, to further reduce costs, save time and avoid errors.

Ready for enterprise requirements

Finally, you should not need to sacrifice your enterprise requirements, such as scalability, security, visibility, operational processes, or automation. Data governance should be at the center of your AI platform, ensuring that the best data and business practices are put in place and tracked by the different members of your team.

Conclusion 

If your AI platform offers all the features mentioned above, including a compressive ecosystem, a collaborative workbench, automated business processes, and a sophisticated application builder, you will be able to create and deploy robust and scalable Cloud-Native AI applications in weeks, not years. Companies using this type of AI Platform have said that they were able to accelerate their development cycles by 10x, compared to other offerings.