Machine Learning

Exploit your unstructured data. Make smarter predictions with Artificial Intelligence and Machine Learning.

machine learning

What is Machine learning?

Machine learning is a branch of computer science that uses algorithms to find patterns in data without being told by humans how to do that and then predict the probable outcome.

google cloud platform machine learning

Introduction to Google AI Platform

AI Platform is a suite of services on Google Cloud specifically targeted at the building, deploying, and managing machine learning models in the cloud.

machine learning google cloud

Hyper-accessible machine learning

Google Cloud AI Platform is designed to make it easy for data scientists and data engineers to streamline ML workflows, and access groundbreaking AI developed by Google. We use it a lot with AutoML (Google’s point-and-click ML engine), but in addition, it supports training, prediction, and version management of advanced models built using Tensorflow, and SKLearn.

Use AI Platform to train your machine learning models at scale, host your trained model in the cloud, and use your model to make predictions about new data.

Where does AI Platform fit into the ML workflow

The diagram on the right gives a high-level overview of the stages in an ML workflow. The blue-filled boxes indicate where AI Platform provides managed services and APIs: All AI functions are accessible through the Google cloud console in one unified analytics platform or stand-alone, depending on your situation and requirements.

machine learning AI

MLOps practice for communication and collaboration

ML can be a game-changer for a business, but without some form of systemization, it can devolve into a science experiment. The real challenge isn't building an ML model, the challenge is building an integrated ML system and continuously operate it in production.

MLOps is a practice for collaboration and communication between data scientists and operations professionals to help manage the production ML lifecycle. It is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops).


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