Top machine Learning Platforms 2023

Machine learning is a term referring to a computational system with the capability to ingest data, analyze it and spot trends and patterns. As a subdivision of AI, machine learning (ML) systems build algorithms based on sets of sample data and deliver forecast, without being exclusively programmed to do so. What's more, these algorithms change and adapt in response to emergance of new data or alteration of conditions. This autonomous learning ability is at the core of today’s enterprise. It’s increasingly used to take major decisions and drive automation. Even though ML is closely associated with statistical analysis and data mining—and there are often overlaps across these disciplines—what sets ML apart is the capabilitly to detect patterns, trends and properties that would otherwise go unnoticed or remain unreachable. Essetially, ML focuses on known knowledge and ways to put it to use more effectively.

While it’s feasable to create a customized ML system, many organizations rely on a dedicated machine learning platform from a data science or data analytics vendor. It’s best to assess your organization’s needs, including the type of ML you require. This includes whether your company can benefit from a conventional method or deep learning, what programming languages are needed, and which hardware, software and cloud services are necessary to deploy and scale a model properly. While it’s feasable to create a customized ML system, many organizations rely on a dedicated machine learning platform from a data science or data analytics vendor. It’s best to assess your organization’s needs, including the type of ML you require. This includes whether your company can benefit from a conventional method or deep learning, what programming languages are needed, and which hardware, software and cloud services are necessary to deploy and scale a model properly. One of the key decisions comes from the underlying machine learning framework, which typically includes one of four approaches: Sci-Kit Learn: A user-friendly and highly flexible open-source framework that nevertheless delivers sophisticated functionality. H2O: ML framework that’s open source and heavily slanted to decision support and risk analysis. TensorFlow: An open source and highly modular framework created by Google. PyTorch: A more intuitive open source framework that incorporates Torch and Caffe2, integrated with Python.