Machine learning is an application of artificial intelligence (AI) that enables systems to learn automatically and improve through experience without the assistance of explicit programming. Simply put, machine learning (ML) is a process of instructing machines how to learn from data.
Machine learning finds patterns in massive amounts of data — numbers, text, images, and more. It has enabled self-driving cars, practical speech recognition, effective web search, a greater understanding of the human genome, and personalized streaming media recommendations (e.g., “You might like…”).
For businesses, machine learning provides predictive power and allows for data analysis on a deeper level. Marketing teams can use machine learning to understand customer behavior better and predict churn, for instance. Operations teams can use it to predict when something might fail. HR can use it for employee retention. Organizations that use machine learning are more likely to measure a top- or bottom-line impact than those that do not.
Artificial intelligence (AI) is a wide-ranging concept that describes machines carrying out tasks in a “smart” way.
Machine learning is an applied AI solution in which machines are given access to data and self-train and learn from this data to produce insightful results. Machine learning uses algorithms and increasingly complex calculations, based on example data, to create a generalized solution.
Rather than it being a case of machine learning versus AI, or even data science versus machine learning, it’s about machine learning empowering AI and data science. Machine learning gets better with more data. Embracing machine learning as well as AI and data science leads to improved business intelligence.
Machine Learning for Business
Greater volumes and varieties of available data, more powerful computational processing, and affordable data storage make it possible to quickly produce models that can analyze bigger, more complex data sets on a vast scale. By building accurate models, you can identify profitable opportunities and avoid unknown risks.
There are many different machine learning algorithms and even more applications for them. Here are a few that can help you better understand your customers and revenue opportunities:
Machine learning is responsible for the improved recommendation features of streaming media services and those of ecommerce businesses, including Amazon. Recommendation engines aggregate customer and sales data to predict how likely a customer is to engage or buy a product or service and adjust recommendations accordingly.
Dynamic pricing, also known as surge pricing, demand pricing, or time-based pricing, is a pricing strategy that involves continuously changing prices for products or services based on market conditions and customer engagement. Machine learning can provide an understanding of these contributing factors to enable price optimization strategies to maximize revenue.
Customer Lifetime Value Modeling
Machine learning can help you identify, understand, and retain customers. Machine learning models predict customer behavior, allowing you to focus your marketing and customer service efforts where they can be most effective — whether by encouraging more engagement with current customers or targeting new ones.
Machine learning can be used for clustering and classifying groups of customers into personas based on specific criteria. Connecting personas with engagement and purchase behavior allows you to personalize marketing campaigns and improve sales.
Machine learning can help you identify which of your customers will most likely stop engaging with your business and why. This knowledge, in turn, can help you develop retention strategies to optimize marketing and minimize churn.
Machine learning algorithms differ in how they work, the type of data they work with, and the kind of task or problem they are intended to solve. There are five primary types of machine learning algorithms.
Supervised learning involves informing the machine learning algorithm about the relationships between data. Outputs, then, exist within a finite universe and in relationship to inputs.
Supervised learning requires labeling data. While supervised learning allows for greater control over the data and the classes within the data, it limits the potential of machine learning and may not discover insights that unsupervised learning can.
It can also be labor-intensive and has a limited upside. Supervised learning enables excellent specificity regarding the definition of classes, but it can become unruly when working with big data.
In unsupervised learning, the machine classifies data based on the patterns it finds without the data being labeled beforehand. Unsupervised learning is beneficial when there’s minimal initial insight available. It brings structure to an existing data set in which the variables are unclear to humans.
Unsupervised learning algorithms reveal otherwise unavailable insights through clustering large amounts of data.
Existing in the space between supervised and unsupervised learning is semi-supervised learning, which combines features of both types of machine learning.
Within this framework, the algorithm is provided some rules — but not all. The machine learns from experience and can then apply rules to unclassified data.
Reinforcement learning does not require a training data set but learns through its own experience by trial and error in the service of achieving its objective.
Reinforcement learning is the type of machine learning used for many “smart” products and features, such as search engine result refinement, virtual assistants, product and service recommendations, and fraud detection.
These models are trained through human reinforcement, both positive and negative. Reinforcement learning is ideal when the best or only way to gather knowledge about an environment is to interact with it.
What Is a Machine Learning Framework?
A machine learning framework is an interface or platform for developing machine learning models. They are user-friendly and optimized for performance and ease of use.
Some versions of machine learning frameworks provide infrastructure management. Others are more scalable and flexible. All machine learning frameworks are bundled with machine learning tools.
The right machine learning tools empower data scientists to optimize your machine learning projects. With cloud-based machine learning technologies, you can innovate, gain a competitive advantage, and optimize code so you can focus on the strategic work of building machine learning models.
Similar to data warehousing tools and BI tools, machine learning tools facilitate analytics and varieties of reporting. Machine learning tools establish their own space via machine learning frameworks.
Basic Machine Learning Tools
Standard programming languages like Python, R, Java and C++ are popular for machine learning. As most data scientists are familiar with these languages, there’s little to no additional learning needed to get started.
Depending on your platform and data warehousing situation, there may be a need for additional data cleansing and data conversion steps. Fast data tools such as a cloud-based data warehousing platform allow data scientists to spend more time fine-tuning models and less time worrying about access.
Machine learning covers a broad category of vendors, tools, and technologies that provide advanced statistical and predictive modeling capabilities.
These tools and technologies often share some overlapping features and functionality with BI tools but focus less on analyzing and reporting past data. Instead, they focus on examining large data sets to discover patterns and uncover useful business information that can be used to predict future trends.
Snowflake for Machine Learning
Snowflake’s Data Cloud architecture enables easy data preparation for machine learning model building. Snowflake also partners with a wide range of machine learning solutions providers, including Amazon SageMaker, Dataiku, and DataRobot.
Snowflake supports machine learning for data science and AI-driven data science applications. Snowflake Snowpark delivers runtimes and libraries that securely deploy and process non-SQL code in Snowflake, in Python, Java, and Scala.
Performance speed is a critical factor in supporting robust machine learning models. Snowflake workloads scale up and down and can bear data preparation responsibilities, reducing data-related burdens from machine learning tools.