Today, most enterprises are onboarding various machine learning platforms as a part of introducing cognitive solutions to the enterprise. Here is a brief overview of Machine Learning Platforms.
What is Machine Learning?
As IBM puts it, “Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.”
The algorithms deployed in machine learning are used to identify patterns in enormous data sets, a feat impossible for people to do without advanced software and a robust technology stack. It’s nontrivial to roll your own machine learning system and maintain it in-house. That’s why instead, many companies elect to use machine learning platforms.
SAS explains that “Machine learning is a method of data analysis that automates analytical model building. It is…based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”
You may hear people use other terms to refer to machine learning, such as deep learning, artificial intelligence, A.I., expert system, natural language processing system, and neural network, according to Thesaurus.com.
Whatever term you prefer, machine learning is now growing by leaps and bounds. According to Markets and Markets, “The machine learning market expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.”
Organizations opt for machine learning solutions to improve the customer experience, get a better return on investment and obtain an edge over their competition.
What Are the Enterprise Use Cases for Machine Learning Platforms?
Before you commit to working with a machine learning platform, it’s useful to consider how other enterprises have benefited from this technology, by examining their use cases:
- Detecting Fraud on the Fly: “Machine learning regression and classification models have replaced rules-based fraud detection systems,” noted IBM, “which have a high number of false positives when flagging stolen credit card use and are rarely successful at detecting criminal use of stolen or compromised financial data.”
- Improving Ad Targeting to Website Visitors: A new kind of contextual online advertising approach comes together with machine learning that can understand the material in a website page, including the details of an article’s meaning as well as subtleties such as opinion or authorial attitude, which allows for showing ads that speak more directly to the reader.
- Reduce Customer Service Workforce: “Most people are familiar with virtual assistants from tech companies like Apple and Google. What they might not know is the extent to which machine learning powers these bots,” says TechTarget. “Deep learning plays an important role in developing natural language processing, which is how the bot is able to interact with the user, and in learning the user’s preferences.”
- Understanding Customer Desires: With the majority of businesses gathering data to some extent on their customers, there’s far too much for people to analyze and understand by themselves. A company can process this information (including social media and web browsing activities) on a machine learning platform to gain new insight into their loyal customers as well as figure out why some customers never come back. The raw data holds secrets companies can use to create targeted advertisements and other messaging.
- Provide Better Customer Experience to Drivers: “Kia Motors builds more than 3 million vehicles a year for customers in 180 countries. In addition to external-facing sensors that assist with automatic braking and lane departure warnings, Kia is using computer vision inside the cabin to better understand and assist drivers,” according to a report from Amazon AWS. “Kia uses machine learning technology like Amazon Rekognition to create a highly personalized driver experience,” analyzing images and videos to customize seat and mirror positioning for each person who sits behind the steering wheel.
Typical Features, Functions, and Capabilities Representative of Machine Learning Platforms
Enterprises turn to machine learning platforms so they don’t have to worry about taking care of the computation in-house. The benefit is you eliminate the need to hire more staff for your IT department.
Towards Data Science noted that “Machine learning (ML) platforms are services or tools that allow you to automate or outsource parts of your data science work.” It says that the three main aspects of machine learning platforms concern data management, model development, and prediction serving.
Dataversity advises businesses to look at machine learning platform solutions for their ability to integrate with their ecosystem, to scale easily as needed, to be extensible to accommodate growing workloads, and to provide tools to help teams collaborate on the platform. Here are common features, functions, and capabilities of machine learning platforms:
Machine Learning Platform Features:
- Data Analysis: The AI system analyzes, manipulates, transforms data so we can derive meaning from it.
- Data Catalog: The catalog indexes your enormous data sets, tracking the lineage of the data and providing support for data analysis.
- Data Labeling: Identifying raw information and adding labels to bring meaning and context
- Data Transformation: Moving data from its raw state to join it, de-normalize it, and prepare for analysis.
- Data Validation: Verifying with a sample of data how useful the model is.
- Experiment Tracking: The platform keeps track of all data related to your experiments, such as for purposes of comparison with previous experiments and evaluating what changes to make next.
- Feature Extraction: A process to cut down the number of resources you need to describe large data sets in your model.
- Model Review and Testing: To ensure the model is doing what you intend, you use the machine learning platform to test how it is processing
- Model Training: Using machine learning algorithms to see what predictions you can derive from your training data. Data is split into two sets, with a portion to test and the rest to train.
- Model Tuning: Efforts to improve your model’s performance, by choosing hyperparameters and making different adjustments, fine-tuning until you get the desired results.
- Monitoring and Observability: Your team can monitor and understand what is happening in the system, with logs or by metrics you defined earlier. Observability has to do with understanding what the system is doing, for active debugging.