Let’s Talk ML
It’s been 67 years since Stanford professor John McCarthy defined artificial intelligence as “the science and engineering of making intelligent machines.”
Machine learning (ML) helps businesses find insights hidden in huge quantities of data while avoiding expensive and time-consuming traditional methods of analysis.
This guide will walk you through the top trends in ML for 2023 and provide some examples of how it can help your business.
Refresher: What is Machine Learning?
Machine learning (ML) algorithms use data to learn, adapt, recommend, and draw inferences. Unlike rules-based programs, ML algorithms become more accurate as they iterate.
Machine learning has already become one of the most common forms of AI in the business world. The creation of incredible amounts of data and the inception of deep learning and artificial neural networks fueled the rise of ML.
Companies often deploy ML to reveal insights hidden in huge quantities of data that no humans could study at that scale or pace.
Trends in Machine Learning for 2023
Trend 1: Rise of MLOps
Machine Learning Operations (MLOps) is a practice that improves ML model accuracy and efficiency. The complex and compounding challenges generated by multiple stakeholders implementing ML create the need for the practice.
By 2025 the MLOps market is expected to expand to almost
Trend 2: Data Governance
Organizations realize a solid data and ML governance strategy is critical to delivering value through their ML efforts while ensuring standardization and ownership.
Trend 3: Use of Third-Party Data
Companies realize that only using internal data leaves gaps in their understanding of the market. To address this need, businesses are increasingly making use of publicly available reports, news, and records.
The MIT Sloan Management Review found that analytically mature businesses were more likely to leverage external data sources, such as customers, vendors, regulators, and competitors.
Third-Party Data Use Case: Pricing Simulator
Trend 4: No and Low-Code ML Platforms
Users create custom programs by dragging and dropping algorithms. No- and low-code platforms provide companies challenged by hiring tech- and AI-skilled workforces with access to ML.
Trend 5: Microservices
Organizations are adopting microservices architectures, which use smaller, modular, independent software pieces to complete specific tasks. Different microservices use APIs to interface with each other and achieve the same functions as a broader application.
of organizations were developing or migrating at least a quarter of their systems to microservices
Microservices Use Case: Automated Financial Spreading
Trend 6: ML in Embedded Analytics
Embedded analytics is when data analysis happens in a user’s usual platform or workflow. Users no longer have to interrupt their work to go to another portal and access analytics information.
Real-time analytics at a much lower cost can be a huge advantage of embedding analytics.
ML and Embedded Analytics Use Case: Intel Smart Scale
Trend 7: Domain-Specific ML
Machine learning is increasingly domain-specific, meaning data scientists select algorithms built for the specific industry or job function addressed by the project. Domain knowledge helps data scientists save time with ML models that already fit the business need and require fewer iterations to meet the use case.
Subject matter expertise is crucial to defining the use case and developing successful ML models.
Domain-Specific ML Use Case: Diagnosing Alzheimer’s with ML and AI
Trend 8: Multi-Modal Learning
Multi-modal learning teaches a machine to process data and information from multiple modalities at once. A multi-modal learning project might combine Natural Language Processing (NLP) with computer vision.
ML Creates Business Outcomes for Bayer and Makes Agriculture Smarter
ML advances and adoption continue to soar. ML has many invaluable use cases and seemingly endless potential – which continues to grow with new capabilities and trends – to create business impact.
Build ML into your organization’s systems and processes to filter through all the data noise.
Businesses need to explore how they can use ML to optimize their processes and make the most of their resources, or they will be left behind.