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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. And everybody agrees that in production, this should be automated.” Yet this leads into another important point.

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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. MLOps – Model monitoring and ongoing governance wasn’t tightly integrated and automated with the ML models.

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Responsibility for maintenance and troubleshooting: Rockets DevOps/Technology team was responsible for all upgrades, scaling, and troubleshooting of the Hadoop cluster, which was installed on bare EC2 instances. This created a challenge for data scientists to become productive. Analytic data is stored in Amazon Redshift.

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Top AI/Machine Learning/Data Science Courses from Udacity

Marktechpost

The curriculum also includes classical search, automated planning, and probabilistic graphical models for comprehensive AI training. It covers advanced topics, including scikit-learn for machine learning, statistical modeling, software engineering practices, and data engineering with ETL and NLP pipelines.

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Top Predictive Analytics Tools/Platforms (2023)

Marktechpost

We can automate the procedure to deliver forecasts based on new data continuously fed throughout time. A few automated and enhanced features for feature engineering, model selection and parameter tuning, natural language processing, and semantic analysis are noteworthy.

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

ODSC - Open Data Science

By automating repetitive tasks and generating boilerplate code, these tools free up time for engineers to focus on more complex, creative aspects of software development. Well, it is offering a way to automate the time-consuming process of writing and running tests. Just keep in mind, that this shouldn’t replace the human element.

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning Blog

Scaling ground truth generation with a pipeline To automate ground truth generation, we provide a serverless batch pipeline architecture, shown in the following figure. The serverless batch pipeline architecture we presented offers a scalable solution for automating this process across large enterprise knowledge bases. 201% $12.2B