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Machine Learning Tools

Machine learning, a subset of AI that enables computers to learn from data and improve through experience, is a booming segment. Spending around machine learning is expected to grow from about $1.58 billion in 2017 to $20.8 billion in 2023, according to a recent report.

A variety of sectors, including banking, government, health care, life sciences, retail and telecom, are increasing spending around ML enterprises.

Meanwhile, a shortage of qualified AI practitioners inhibits even greater progress for many businesses.

With the right machine learning tools, however, data scientists are empowered to optimize ML endeavors.


Similar to data warehousing tools and BI tools, machine learning tools facilitate analytics and a variety of reporting. There might also be overlap with Big Data tools. ML tools establish their own space, however, via machine learning framework.


Common programming languages like Python, R, Java and C++ are popular in the ML community. Given the high level of familiarity among data scientists with these languages, there's little to no additional learning needed to begin.

Depending on the platform and data warehousing situation, there may be the need for additional steps with data cleansing and data conversion. Conventional data warehouses slow the ML process and contribute to data wrangling headaches. Fast data tools allow for data scientists to spend more time fine tuning models and less time worrying about waiting for access.

Machine learning for data science taps into the tools of the trade and can integrate machine learning tools into a framework.


A machine learning framework is an interface or platform that facilitates the development of machine learning models. They are generally user-friendly, optimized for performance and ease of use.

Some versions of machine learning framework liberate users of infrastructure management. Others are more scalable and flexible. All machine learning framework comes with some machine learning tools baked in.

There are nuances within machine learning framework, consistent with the advancements in AI. Deep learning framework is an offshoot of more traditional machine learning framework.

Deep learning, a subset of ML that refers to deep artificial neural networks, varies because of its ability to process unstructured data. Deep learning is used in image and sound recognition, for example.

Additionally, there are numerous options for an open source machine learning framework.

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