Sounds interesting but don't know where to start? Check out this end-to-end MLOps platform by @abacusai https://
abacus.ai
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Abacus AI: Complete MLOps Platform for End-to-End Solutions
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MLOps Bridges Data Science and IT for Secure AI Deployment
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At the end of the day, MLOps practices can help organizations to achieve their AI goals more quickly and efficiently. By fostering collaboration between data science and IT teams, MLOps can help to ensure that AI is deployed in a way that is secure, reliable, and scalable.
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MLOps Enables Data Scientists to Focus on Model Development
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Another important aspect of MLOps is that it enables data scientists to focus on what they do best – developing models and algorithms – while IT takes care of the operational aspects of model deployment and management.
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MLOps ensures secure reliable scalable model deployment
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This improved collaboration between data science and IT also helps to ensure that models are deployed in a way that is secure, reliable, and scalable. For ex, MLOps practices can help to prevent security breaches and ensure that models continue to perform well in production.
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MLOps Enables Efficient Collaboration Between Data Scientists IT
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One of the key benefits of MLOps is that it enables data scientists and IT professionals to work together more efficiently and effectively. For example, MLOps practices can help to ensure that data scientists have access to the right infrastructure and tools.
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MLOps: Bridging the Gap Between Data Science and IT Teams
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Do you ever feel like your data science and IT teams are speaking different languages? That's where #MLOps comes in! By standardizing workflows and processes, MLOps can bridge the gap between these two critical teams. (A thread)
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How AI Will Transform Project Management
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How #AI Will Transform Project Management https://
hbr.org/2023/02/how-ai
-will-transform-project-management
… @HarvardBiz #MachineLearning #DataScience #BigData #Analytics #DeepLearning @Shi4Tech @jblefevre60 @sallyeaves @gvalan @data_nerd @ahier @CatherineAdenle @Damien_CABADI @FmFrancoise #DigitalTransformation -
Taxonomy of Integration: Unbundling Data Movement, Reverse ETL, and Automations
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a taxonomy of integration would be useful at this point. people use the word like monolithic blob but needs to be unbundled to be tractable. eg: data movement – @airbytehq (heheh)
"reverse" ETL – census/hightouch
effectful automations – zapier, windmill, inngest & friends
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Splunk Outperforms Datadog as Cloud Market Slows
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As cloud slows, Splunk is the safe choice over Datadog, says KeyBanc https://
thetechnologyletter.com/the-posts/as-c
loud-slows-splunk-is-the-safe-choice-over-datadog-says-keybanc
… // $SPLK $DDOG $AMZN $MSFT $GOOGL #observability #stocks #investing #technology #software #cloudcomputing #earningsseason -
LangChain Unstructured Integration for Data Cleaning
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Before you can use LangChain with your data, you first need to clean it up. That's where an integration with @UnstructuredIO comes in Blog Post: https://
blog.langchain.dev/langchain-unst
ructured/
… We'll use Unstructured to power a lot of our document loaders: https://
langchain.readthedocs.io/en/latest/modu
les/document_loaders.html
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