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Principles of MLOps

Heartbeat

Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. What is MLOps? MLOps is a broad field that encompasses all aspects of developing a machine learning model. This means that even in the business world, MLOps should be addressed.

DevOps 96
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MLOps and the evolution of data science

IBM Journey to AI blog

MLOps is the next evolution of data analysis and deep learning. Simply put, MLOps uses machine learning to make machine learning more efficient. What is MLOps? MLOps fosters greater collaboration between data scientists, software engineers and IT staff. How MLOps will be used within the organization.

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Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

AWS Machine Learning Blog

Machine learning operations (MLOps) applies DevOps principles to ML systems. Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations. PwC MLOps Accelerator is designed to be modular and delivered as infrastructure-as-code (IaC) to allow automatic deployments.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning Blog

In this post, we discuss how to operationalize generative AI applications using MLOps principles leading to foundation model operations (FMOps). MLOps engineers are responsible for providing a secure environment for data scientists and ML engineers to productionize the ML use cases.

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Using Self-Critiquing Chains in LangChain

Heartbeat

Benchmarking responses against predefined constitutional principles it ensures the generation of content that is not only accurate but also ethically sound and devoid of harmful or biased elements. It is implemented using the, which checks responses against predefined constitutional principles. Otherwise, it returns all principles.

LLM 52
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Operationalizing Large Language Models: How LLMOps can help your LLM-based applications succeed

deepsense.ai

In this blog post we will discuss the importance of LLMOps principles and best practices, which will enable you to take your existing or new machine learning projects to the next level. Assigning more weight to these parts is one of the key principles of LLMOps. What is LLMOps? apply to these parts of your system.

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Data Fabric & Data Mesh: Two Approaches, One Data-Driven Destiny

Heartbeat

I decided to write a series of blogs on current topics: the elements of data governance that I have been thinking about, reading, and following for a while. Let’s continue by understanding the four basic principles. Data mesh as a concept was first introduced by Zhamak Dehghani in 2019 and is shaped by four fundamental principles.