Recently, machine learning (ML) has become an increasingly essential component of big data analytics, business intelligence, predictive analytics, fraud detection, and more. Because there is a plethora of methods and tools businesses can use to analyze their data, companies must select an ML approach that minimizes cost and maximizes efficiency. The concept of machine learning operations (MLOps) has emerged from big data analytics as that solution.
What Is MLOps?
Machine learning operations is a way to scale large ML projects. The job of any data scientist is to figure out what data can teach them about their business and help improve it, but MLOps takes that idea one step further by applying deep learning on top of large-scale datasets. It involves the use of methods, systems, algorithms, and processes for improving data-driven decision-making, and value generation through machine learning.
This area of study combines data mining, AI, analytics, and big data with automation to create a self-managing system capable of handling incredibly complex tasks.
What Are Some Key Use Cases for MLOps in Business?
ML is being used for a wide range of processes and can benefit those involving predictions or simulations. Companies are employing machine learning to optimize their operations, gain a competitive edge, and drive revenue. Here are some use cases of machine learning in business.
- Creating a seamless customer experience: Machine learning allows businesses to collect data from multiple sources like emails, phone calls, or chats and track customer trends over time. They can then automatically generate personalized suggestions or products based on what they’ve learned about each customer.
- Improving the quality of production: Businesses can use machine learning models to predict when things will go wrong with machinery or materials, so they can take corrective action before it negatively impacts production quality
- Boosting marketing efficiency: One of the top use cases for machine learning is in targeted advertising. When paired with big datasets and vast quantities of information, intelligent machines can help companies deliver relevant ads to consumers who have demonstrated an interest in similar products and services
- Accelerating development through automation: Another popular application of machine learning is smart chatbots. These programs can monitor online conversations and identify common questions that users ask while they’re browsing your website. Whenever someone poses one of these queries, the chatbot offers a helpful response — often within seconds. This decreases support costs while improving user satisfaction
- Improving resource planning: Businesses are using ML-based systems to forecast resource needs far into the future by analyzing historical sales figures as well as economic factors
- Driving demand by encouraging repeat purchases: Online retailers commonly benefit from ML techniques designed to analyze purchasing patterns, so they can personalize recommendations.
Top Machine Learning vendors
Alteryx is a California-based computer software company with a development facility in Broomfield, Colorado. The products of the company are used in data science and analytics.
Dataiku is an AI and ML company founded in 2013, which has offices based in New York City and Paris, France. It provides Data Science Studio (DSS) with a focus on cross-discipline collaboration and usability.
DataRobot is a Boston, Massachusetts-based platform for augmented data science and machine learning. The platform automates critical tasks, allowing data scientists to work more effectively and citizen data scientists to more easily develop models.
RapidMiner is headquartered in Boston, Massachusetts. Data preparation, machine learning, deep learning, text mining, and predictive analytics are all offered through the company’s integrated ecosystem.
RapidMiner products include RapidMiner Studio, RapidMiner Auto Model, RapidMiner Turbo Prep, RapidMiner Go, RapidMiner Server, and RapidMiner Radoop.
MathWorks is headquartered in Natick, Massachusetts. The company’s two flagship products are MATLAB, which offers an environment for scientists, engineers, and programmers to analyze and display data and build algorithms, and Simulink, a graphical and simulation environment for model-based design of dynamic systems.
MATLAB and Simulink are widely used in the aerospace, automotive, software, and other industries. Polyspace, SimEvents, and Stateflow are some of the company’s other products.
Risks of Using Machine Learning in Business Operations
There are numerous risks involved when it comes to implementing new, cutting-edge technology like machine learning in business operations, including:
- Poor data: The quality of your dataset can affect whether a model is accurate and what result it provides (i.e., how much it performs on test data).
- Insufficient sample size: Many businesses don’t have enough data to enable accurate models. The model doesn’t know its limitations if you overfit a model (i.e., train it on too small a sample), or if you use too many variables, you risk ending up with an inaccurate model.
- Lack of expertise: Some complex algorithms require training by experts. Certain business processes require more than one set of eyes to make sure there are no gaps in processes and that all potential issues are addressed before moving forward.
- Small teams: Having only a handful of people working on implementing ML tools also increases chances for error and hurts the odds of coming up with a truly innovative idea from scratch.
- Technical barriers: ML tools also face obstacles due to technical reasons — many data scientists say they would prefer the Python language programming environment because it is easier to write and edit code in Python.
- Reliance on third parties: Depending on external vendors for some analytics can be risky. Data, tools, and talent all need to be accessed internally for certain ML tasks.
Read more: What Is Adversarial Machine Learning?
Why Do We Need MLOps?
The data required to train ML algorithms can be quite large. Training models often require hundreds of thousands or even millions of instances to identify meaningful patterns.
Training a deep neural network for object recognition, for example, requires images of tens of thousands of labeled objects, and training a natural language processing system means downloading gigabytes worth of data.
For most organizations, it’s unfeasible to simply push all that data into production and let a model run until it finishes, and many business processes don’t allow for taking things offline in order to retrain.
By combining operations and machine learning, developers can build applications that can continuously learn from new data as they’re being created. Not only does an MLOps approach enable faster time-to-market with improved accuracy, it also has big implications for forecasting, anomaly detection, predictive maintenance, and more.