Data
MLOps Platforms
Machine Learning operations (MLOps) platforms automate and streamline the entire Machine Learning lifecycle. They provide a centralized place for data scientists to experiment, develop, and deploy models, along with tools for preprocessing, training, and hyperparameter tuning. MLOps platforms enable seamless deployment, monitoring, and scaling of Machine Learning models in production, while also facilitating model management and continuous learning. Using MLOps platforms can help accelerate time-to-market, reduce complexities, and ensure efficient collaboration.
Learn MLOps Platforms with the Practica AI Coach
The Practica AI Coach helps you improve in MLOps Platforms by using your current work challenges as opportunities to improve. The AI Coach will ask you questions, instruct you on concepts and tactics, and give you feedback as you make progress.Intro to MLOps Platforms
Lessons on ML Platforms — from Netflix, DoorDash, Spotify, and moreMany tech companies have built Machine Learning platforms to enable data scientists to quickly deliver value. These platforms handle common tasks like model training, serving, and monitoring. Ernest discusses the common components of these platforms and the tools used by leading companies such as Netflix and Spotify.
How to Assess the Needs of an ML Platform Project
Applying the MLOps LifecycleMLOps projects vary greatly in their needs depending on factors like the data, model complexity, and governance requirements. Understanding these needs at each stage of the MLOps lifecycle is key to Scoping and tackling MLOps projects effectively. Ryan explains what factors and needs to consider and provides example questions to help scope the right MLOps approach.
How to Select an Off-the-Shelf ML Platform
Comparing Cloud MLOps platforms, From a former AWS SageMaker PMAmazon SageMaker and Google Cloud's Vertex AI are the two leading Machine Learning platforms today. Alex compares the platforms across key features like data processing, model training, and serving.
MLOps Platform Case Studies
Our MLOps story: Production-Grade Machine Learning for Twelve BrandsJeffrey explains his company's journey toward building an MLOps platform to deploy Machine Learning models across twelve of their brands. When they started the project, their Machine Learning production landscape was limited and scattered, running different models for different brands. Their goal was to build an MLOps platform to centrally deploy and manage Machine Learning models for all of their brands.
How Instacart’s ML Platform Tripled ML Applications in a YearShahil explains how the Instacart team built Griffin, an extensible Machine Learning platform, to support their growing number and complexity of ML applications.
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