Top 10 Must-Haves in an Enterprise Machine Learning Platform

Finally, it takes lots of testing and analysing to create a ML platform that rolls out excellent artificial intelligence solutions. The right platform should give any business the freedom to continue testing state-of-the-art predictive analytics solutions and business intelligence tools for their data. 

The Azure Machine Learning Studio, for instance, comes to data scientists from Microsoft, offering the peace of mind that comes with a solution from a well-established company. Their tool is ideal for scientists to test and execute a range of AI offerings in a flexible, ever-growing environment. 

Machine learning platforms rely on access to your data to function. However, no business, no matter the size can afford to take risks when it comes to their data. When using an ML platform to build AI initiatives, the company you're working with must offer you security and privacy.

For example, the Databricks platform - an Apache Spark solution for machine learning - provides a range of proprietary features for real-time enablement, operations, security, and reliability through Amazon Web Services. Access to AWS also ensures that you're getting the safety and security of the Amazon cloud.

Automation is a must-have for any business that wants to reap the full benefits of artificial intelligence and machine learning. The option to automate manual processes in your machine learning platform ensures that you can save time and resources. It also allows you to engage your staff in actions that require human insight. The right automated system allows you to make impressive progress with your machine learning strategy. This is often much faster than you could without automatic tools. 

San Francisco-based machine learning platform company, Domino, offers plenty of automation features that allow data scientists to spend more of their time on creative work like preparing, exploring, and also visualising data.  

There are plenty of providers of machine learning platforms that are just emerging in the marketplace for the first time. Many of these offerings are highly engaging and intuitive. However, for most data scientists, it's hard to beat the benefits of a highly-established platform with an active community. 

When you have access to a high-level community, it's easier to find solutions to problems, explore existing use cases. The mature user base of TIBCO software helps companies lay the foundations for their machine learning strategy with advanced prototyping and plenty of community support. That may be why TIBCO was named a leader by the Forrester Wave report. 

The demand for machine learning and artificial intelligence is continuously expanding. Therefore, businesses seeking an ML platform need something consistently up-to-date and ready to take advantage of cutting-edge technology. There's no time to wait around when new frameworks and best practices appear in the marketplace. That's one of the reasons why company like Mathworks are staying ahead of the game. They always keep up to date with market developments. 

On the Mathworks MATLAB platform for machine learning, you can access point-and-click apps for training and comparing models, advanced signal processing, automatic hyperparameter tuning, and more. Additionally, the features keep upgrading, with new functionality appearing all the time. 

To solve business problems with ML, you need a platform that makes it easier to see and understand available strategies. A platform that provides visual reports and insights can be far more effective for people who want to start making changes to their operations as quickly as possible. 

For instance, the SAS data science and analytics company offer a Visual Analytics and Enterprise Miner as part of their machine learning platform. This means getting quick, useful information about your ML tools and BI insights, while your AI is still forming. 

Machine learning platforms need to work quickly and effectively to make a rapid change to business functionality. No company today can afford to wait for months, or even years, to build the right database for their artificial intelligence tools. That's why platforms like RapidMiner make sure that you can extend your AI and ML opportunities quickly, on a range of environments, including Hadoop. 

With RapidMiner's server, you can share, collaborate on, and maintain also machine learning models to make progress more quickly. That's one of the reasons why RapidMiner was a Leader in the Gartner Magic Quadrant for Machine Learning and Data Science platforms for the 6th year running in 2019. 

There are many different kinds of data that can nourish and support the growth of a machine learning strategy. One option comes in the natural language conversations that clients and agents have every day. If you want your machine learning tools to be able to access that data, you'll need a natural language understanding and native language processing functionality. This will allow you to convert audio signals into more searchable content. 

The H2O.ai machine learning platform offers various forms of machine learning functionality, depending on the kind of data you want to process. For instance, you can tap into H2O deep water for deep learning. There are also supervised natural language processing options too.

If you're just getting started in the world of machine learning, then you might not have all the talent available in-house that you need to begin designing a high-level framework. This means that your ML platform needs to give you access to a data analytics culture, without requiring the insight of data scientists initially. For instance, Alteryx analytics provides data scientists with a building platform for their machine learning tools that work in a simple and seamless workflow. 

To expand their machine learning offerings, Alteryx purchased a data science enterprise that simplifies management and deployment, allowing smaller businesses to step into the AI landscape.

Most of the machine learning platforms available today appear on the cloud. This gives the platform the ability to scale and adapt to the changing needs of the program. With a cloud-based deployment, you can more easily access the most complex and immersive AI tools, implementing new functionality as your ML strategy evolves. 

For instance, the KNIME analytics machine learning platform is an open-source solution with more than 100,000 users around the world. Cloud-based versions are available through reliable providers like AWS and Microsoft Azure. This gives companies access to 100% of the resources they need, whenever they may need them.