Choosing the right machine learning platform can help you onboard your data applications in a few weeks and immediately reap the benefits.
Machine Learning (ML) and Artificial Intelligence (AI) applications are gaining popularity and being adopted in all kinds of exciting ways. AI applications analyze vast 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 costs, improve customer satisfaction, or even how to fight the rise of on-demand streaming services better.
So what is a machine Learning platform? AI/ML applications are not just about choosing a sophisticated algorithm and feeding it data to discover new insights. For an AI application to correctly predict actionable outcomes, it needs to be perfectly set up and deployed with the help of a robust AI platform. Choosing the right machine learning platform can help you onboard your AI application in just a few weeks and immediately reap its benefits instead of taking months or even years with all the risks and hidden costs involved. One could convincingly argue that the platform on which your AI application is conceived and deployed is more critical than the AI algorithms themselves.
A machine Learning 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 a machine learning 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/ML and analytics applications. It needs to take away the complexity of creating, training, evaluating, and deploying several AI applications while ensuring your data is well managed, and processes are automated. To better understand how vital a ML platform is, let’s look at the features that make a perfect AI platform.
Setting-up your machine learning 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 to help you make appropriate business decisions. These can be Machine Learning, Artificial Intelligence and Deep Learning algorithms or even simpler statistical models. A good ML 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 various intricacies of the models you’re using to learn from your data correctly. The platform should guide you by setting up your models, training and testing them, scoring them, and choosing the best one to deploy.
Extracting your data for machine learning
The machine learning platform you should consider must allow you to easily set up and manage your complete data cycle from end-to-end, along with the full: 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 further information.
Definition of a machine learning pipeline
The machine learning platform you should consider must allow you to easily set up and manage your complete data cycle from end-to-end, along with the full: 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 further information.
What is the importance of an ecosystem?
An important factor in choosing the right machine learning platform is its ecosystem – a free marketplace or toolshed, where you can pick and choose the different tools you will use throughout the AI Pipeline. You should select the Cloud infrastructure you will run your project on, like 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 within the AI Platform. These tools should be ready-to-use, popular third-party tools, templates, and functions. You should also add your specialty tools and create and share new ones.
How to benefit from a machine learning powerful workbench
The machine learning platform you choose should offer an easy-to-use, yet powerful 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 backward to make your tools work together. The workbench should allow you to set up automated processes, including alarms and triggers, easily.
Don’t forget the visual Interface
Your machine learning 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 give 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.
How to build an end-user application?
The machine learning 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 specific 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.
How to be cloud-native from birth
Your machine learning platform should allow you to exploit a Cloud infrastructure’s benefits fully. 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 managing the infrastructure (software and hardware set-up and maintenance) or dealing with security challenges. Your platform should provide the scalability you need when you need it. It should allow your AI application to quickly grow by automatically accessing the resources your project needs as it needs them, and you should only pay for the help you use.
The importance of a collaborative process for machine learning
As you can probably tell from the list above, many different people will be working on your machine learning application. You should be able to invite other 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 interact with the same processes and datasets through the same workbench.
How to benefit from machine learning blueprint driven processes
To further help collaborate and improve project effectiveness and speed, your machine learning 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, partners, and customers to reap the full benefits of your AI/ML application. These blueprints should be automated once in production to reduce costs, save time and avoid errors.
Let’s get ready for your 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 machine learning platform, ensuring that the best data and business practices are put in place and tracked by the different members of your team.
Conclusion
Now that we know what is machine Learning platform, suppose your ML platform offers all the features mentioned above, including a compressive ecosystem, a collaborative workbench, automated business processes, and a sophisticated application builder. In that case, you will be able to create and deploy robust and scalable Cloud-Native machine learning applications in weeks, not years. Companies using this type of machine learning platform have said that they could accelerate their development cycles by 10x, compared to other offerings.
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