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Basil Faruqui, BMC: Why DataOps needs orchestration to make it work

AI News

The operationalisation of data projects has been a key factor in helping organisations turn a data deluge into a workable digital transformation strategy, and DataOps carries on from where DevOps started.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. AWS also helps data science and DevOps teams to collaborate and streamlines the overall model lifecycle process. This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice.

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Software Engineering Patterns for Machine Learning

The MLOps Blog

From writing code for doing exploratory analysis, experimentation code for modeling, ETLs for creating training datasets, Airflow (or similar) code to generate DAGs, REST APIs, streaming jobs, monitoring jobs, etc. Related post MLOps Is an Extension of DevOps. Explore how these principles can elevate the quality of your ETL work.

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Top AI/Machine Learning/Data Science Courses from Udacity

Marktechpost

These courses cover foundational topics such as machine learning algorithms, deep learning architectures, natural language processing (NLP), computer vision, reinforcement learning, and AI ethics. Udacity offers comprehensive courses on AI designed to equip learners with essential skills in artificial intelligence.

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Azure service cloud summarized: Part I

Mlearning.ai

One can only train and mange so many algorithms/commands with one computer, thus it is attractive to use a service cloud platform with more computers, storage, and deployment options. You can monitor and make changes to the deployed model using the three areas in the DevOps platform.

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Top Predictive Analytics Tools/Platforms (2023)

Marktechpost

It relates to employing algorithms to find and examine data patterns to forecast future events. Algorithms and models Predictive analytics uses several methods from fields like machine learning, data mining, statistics, analysis, and modeling. Machine learning and deep learning models are two major categories of predictive algorithms.

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Why Software Engineers Should Be Embracing AI: A Guide to Staying Ahead

ODSC - Open Data Science

AI for DevOps and CI/CD: Streamlining the Pipeline Continuous Integration and Continuous Delivery (CI/CD) are essential components of modern software development, and AI is now helping to optimize this process. In the world of DevOps, AI can help monitor infrastructure, analyze logs, and detect performance bottlenecks in real-time.