For some years, Artificial Intelligence (AI) and Machine Learning (ML) becoming part and parcel of modern business solutions. While both of these technologies are making huge strides by helping businesses with intelligent means to address customer demands, these technologies are also going through a rapid transformation. Incorporating AI and Machine Learning capabilities into modern software solutions is also paving the way for intelligent automation. This is how we have access to an increasing number of self-service Machine Learning models, and several offerings for machine learning-as-a-service.

Such ready to use services and solutions are facing an increasing level of demand corresponding to the popularity and enhanced demand of the AI and Machine Learning technologies in the enterprise IT sector. Several statistics clearly confirm this steady growth of demand. According to the reports of Gartner, the adoption of AI-based solutions has increased more than 3 times in the past year alone. A report by MIT Sloan Management Review and Boston Consulting Group showed that more than 90% of businesses now consider AI to have the potential of driving growth by the year 2023.

From the above-mentioned statistics and reports, it is clear that the future of the enterprise IT and software is closely intertwined with the AI and Machine Learning (ML) capabilities. The AI and ML together will play the most important role in driving more accurate and audience-centric business decisions. In this regard, automated Machine Learning (ML) models will play a massive role.

AutoML: The AI and Machine Learning of the Future

Among the most dynamic and era-defining trends that are going to make the biggest impacts on ML-based solutions is the AutoML. AutoML basically refers to the automated Machine Learning, and it can help developers addressing complex scenarios with ready-to-use and adaptive solutions. AutoML basically consists of two key aspects, respectively, data acquisition and prediction. Following the data acquisition, all the intermediate steps are taken care of by the AutoML platform to deliver precise predictions.

Basically, the AutoML platforms fetch diverse datasets from the users, followed by the labeling of the categories. This is followed by preparing the data, ascribing the perfect algorithm for the respective dataset and optimization of the algorithm in the backdrop. At a certain point, the algorithm comes to a REST endpoint, which is used for making predictions. The process is quite different from the traditional workflow and process of training the Machine Learning models.

Not only the workflow and processes but in respect of capabilities and many aspects AutoML models differ from the traditional ML. AutoML platforms can also deliver and export fully trained mobile-compatible model to work in sync with the iOS and Android platforms. Thanks to these mobile compatible models developers can easily integrate them with their mobile apps. The best thing is, for integrating these models developers do not need to learn the tit-bits of Machine Learning.

Now, coming to scalability, AutoML models can be exported as a highly scalable package. By being packed into Docker containers, AutoML models allow developers to deploy them while maintaining a scalable capability to deal with different production environments. These models enable hosting the cluster of containers in a scalable manner.

AutoML: The Future and Promising Solutions

As the enterprise IT sector is increasingly getting on its feet to provide AutoML as a stand-alone service, we now have several AutoML services from the world’s leading tech companies. Some of the most promising AutoML services from leading tech companies include Google Cloud AutoML, Microsoft Custom Vision, and the Image Recognition solution from Clarifi.

AutoML ideally fills the gap between the cognitive APIs and custom Machine Learning (ML) platforms. It offers the perfect scope of customization without really forcing the developers to undertake a robust and scrupulously big workflow. While the cognitive APIs always makes the revelation of insights challenging, AutoML with its unmatched flexibility can deal with custom data and requirement of portability.

While every technology company and service provider is bent on democratizing the field of Machine Learning for the greater good of the enterprise IT, AutoML has now emerged as the promising future of Artificial Intelligence (AI). Thanks to AutoML the power and capability of AI could now be made available to the business analysts and the business leaders with strategic decision-making capacity.

Automated Machine Learning to Simplify and Augment UI: Microsoft Azure

Microsoft has been a forerunner in the field of Machine Learning based services and solutions with its Azure ML services. From 2018 December, the company has adopted the automated machine learning. Thanks to this, accelerating and simplifying the AI-based solutions has now become easier than ever before. Azure AutoML service will now help data scientists in a never-before manner and create more room for them to concentrate on strategic planning and other objectives.

The platform will also make AI available for a wider business audience across the niches. A vast majority of people who has little understanding and knowledge about the cutting-edge data science and the nitty-gritty of coding can now avail the power of latest Machine Learning algorithms. The Power BI, which allows data analyst’s access to Machine Learning is a great example of this.

Azure has just announced a new web UI based on automated Machine Learning (AutoML) in its own portal, and we can have a look to get a first-hand feel of the offerings.

The new AutoML UI from Microsoft Azure is a great example of how the Machine Learning technology can be made accessible to the general masses. This new AutoML UI offering business domain specialists command on cutting-edge analytics and algorithms without any knowledge of coding is a great example of how future AutoML based services will add more value to the enterprise IT development.

In the time to come, we can expect the automated machine learning to be subject to a variety of experiments. Different combinations of granular parameters and algorithms will be tried and tested on various Machine Learning (ML) models. More audience-specific and customized ML models based on user data will continue to come.

Wrapping Up

Already businesses are focused on data-driven decision making to take on the untapped business opportunities in their respective niches and among target audiences. The modern Business Analytics with the power of AI and ML has made huge progress in deciphering the customer and user data. Automated Machine Learning will only make this process speedier and effortless than ever before.

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By definition, Big Data is all about collecting large (or “Big”) volumes of structured and unstructured data. What makes Big Data useful is analysis of the collected information to find patterns and meaning that otherwise would be left undiscovered. Making sense of Big Data is the realm of Big Data analytics tools, which provide different capabilities for organization to derive competitive value.

What should you look for when selecting Big Data Analytics tools for your business?

• Analytic Capabilities. There are multiple types of analytics capabilities with different models for various types of analysis including:  predictive mining, decision trees, time series, neural networks, path analysis, market basket analysis, and link analysis.

• Integration. Often additional statistical tools and programming languages (such as R) are needed by organization to conduct other forms of custom analysis.

• Data Import and Export. Getting data in and out of various tools is a critical feature and understanding how difficult (or easy) it is connect the analytics tool to the big data repository is a key consideration.

• Vizualization. Seeing the numbers is one thing, but having data displayed in a graphical format, often makes the data more useable.

• Scalability. Big Data can be big to start with, and generally has a tendency to grow even bigger over time. Organizations need to consider and understand the scalability options for the analytics tools they choose.

• Collaboration. Analysis can sometimes be a solitary exercise, but more often than not it involves collaboration.

In this Datamation guide, we look at 8 of the top Big Data Analytics Tools that cover multiple aspects of the market.

• Cloudera
• Microsoft Power BI
• Oracle Analytics Cloud
• Pentaho Big Data Integration and Analytics
• SAS Institute
• Sisense
• Splunk
• Tableau

Cloudera

When it comes to the core of Big Data, few if any companies are as closely tied with the core Hadoop Big Data open source platform as Cloudera. After all, the founders of Hadoop itself started the company. Cloudera recently got  an even bigger foothold in the Hadoop ecosystem with the merger of Hortonworks which was its primary rival.

The key differentiator for Cloudera is the company’s deep understanding and core competence in Hadoop, which carries through its portfolio including the company’s Cloudera Enterprise platform. This is built on top of the open source CDH distribution.

Cloudera’s Big Data tools are a good fit for organizations that need a full stack that includes the core Hadoop technology for collecting and creating Big Data. With Cloudera Enterprise, organizations are able to create and process predictive analytics models, using a variety of integrated tools.

Microsoft Power BI

Microsoft’s Power BI has been a perennial favorite for analyst firms in the business intelligence space, based largely on the platform’s ease of use and accessibility.

In 2018, Microsoft expanded Power BI, extending the same ease of use to Big Data, enabling data ingest and transformation. The key differentiator for the platform is integration with the Azure Data Lake Storage Gen2 which supports HDFS (Hadoop Distributed File System) for advanced big data analytics.

Power BI is a good choice for organizations looking for an easy on-ramp into Big Data Analytics and is a particularly obvious choice for those that have already standardized on a Microsoft stack. Power BI provides cloud based business analytics and integrates what Microsoft calls “content packs” with pre-built dashboards and report for different types of analysis and data monitoring. The collaboration capabilities in the platform enables users to share data and dashboard, while also providing alerting capabilities.

Oracle Analytics Cloud

Oracle hasn’t always been known as a Big Data analytics provider, but it’s a space where the database giant has moved aggressively into in recent years. Self-service Big Data analytics on a consumption usage model is what the Oracle Analytics Cloud is all about.

Among the key differentiators of the Oracle Analytics Cloud that users comment on is the platform’s automation capabilities for different types of analytics and Big Data analysis use-cases. Organizations that are already used to using Oracle tools, including Oracle’s namesake database, will likely be the most attracted to the Analytics Cloud offering.

The ability to bring multiple data sources together is a core capability of the Oracle Analytics Cloud, with a strong infrastructure that including the Oracle Event Hub Cloud service to ingest data and the Oracle Big Data Cloud Service to store data.

Hitachi Vantara Pentaho

Hitachi is not a name that many would associate with Big Data, but ever since the company acquired Pentaho in 2015, it has been a solid player in the space.

Pentaho’s roots are with its open source analytics platform upon which the more expansive Enterprise edition is built. It’s the open source nature of the platform that is a key differentiator and has led to a broad community of users that is also often seen as a key strength by users.

Pentaho is a good choice for organizations with lots of different types of data and big data sources. The ability to rapidly ingest and blend data from different sources is another key benefit that users gain from the Pentaho Big Data Integration and Analytics platform. Pentaho’s platform enables multiple models including predictive analytics to help organizations guide toward specific outcomes.

SAS Visual Analytics

SAS Institute has a long history in the analytics market that predates the use of Big Data as both a term and a technology by decades. The company has deep domain expertise in analytics which is manifest across a number of different offerings that can help with Big Data Analytics, among them is the Visual Analytics solution that runs on the broader SAS platform for analytics.

Visual Analytics is for users and organizations that are looking for deep analytics tools, with drag and drop functionality for building advanced visualizations. Extensibility of the platform for different types of business intelligence and data reporting needs is a key differentiator for the platform.

Collaboration is a core component as well with the ability to share information and comments across multiple options including mobile devices, web browsers and even Microsoft Office applications. SAS Visual Analytics can be deployed on-premises or as a service in the cloud.

Sisense

Getting Big Data repositories in a state where they can be rapidly used for analytics is a non-trivial challenge, that Sisense aims to help solve with its platform

The promise of helping to make it easier to get Big Data ready for analysis is an area of strength and a key differentiator for Sisense, with its Big Data preparation capabilities that aim to make is easier for users to model data.

Sisense is a good choice for larger organizations that are looking for fast implementation time and solid customer support. The data visualization via the systems dashboard is often seen by users as being easy to use and as a time saver to get the required results. Accessing the dashboards and sharing data is another core strength of the platform, with mobile and web options as well as the ability to easily generate different types of reports.

Sisense offers both on-premises as well as cloud-based offering for its platform.

Splunk

Splunk started out as a log analysis platform and has found a loyal based of users and organizations that love the way the platform works and enables data manipulation and visualizations. For those organizations that are already using Splunk for log or other types of analysis, embracing Splunk Analytics for Hadoop is an easy step.

Splunk as a platform is known for its user-friendly web based log inspection and analytics capabilities, which can be extended to look at Big Data stores in Hadoop systems. The platform benefits from a proven collaboration component and enables users to create and share graphs and analytics dashboards.

Key differentiators for Splunk include the ability to integrate with other elements of the Splunk platform, including security controls and Splunk’s own search process language (SPL) which further provides strong benefits to users.

Tableau

The Tableau platform is a recognized leader in the analytics market and is a good option for non-data scientists working in enterprises, across any sector.

The VizQL data visualization technology at the core of Tableau is a key differentiator for the platform overall, creating data visualization without the need to first organize data. Connectivity to different types and backends of Big Data is also a core attribute of the Tableau platform.

A big benefit that users find from Tableau is the ability to reuse existing skills, in the Big Data context. Tableau makes use of a standardized SQL (Structured Query Language) to query and interface with Big Data systems, making it possible for organizations to make use of existing database and analyst skills sets to find the insights they are looking for, from a large data set. Tableau also integrates its own in-memory data engine called “Hyper” enabling fast data lookup and analysis.

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