coretex.ai的动态

My MLOps structure to build an end-to-end machine learning project, from data collection to model deployment. Sounds pretty basic, but it will do for now. GitHub Repo: https://lnkd.in/ehdYJfHU #MLOps #machinelearning

MLOps is a collection of proven principles and best practices applied when implementing machine learning models at scale. An MLOps platform tracks your data, models, experiments and deployments in an automated way. The expected outcomes of this are summarized in the carousel below. Curious about the value that MLOps can bring to your team or organization? Then don't hesitate to get in touch with MLOps Lead Robin Schut to plan a consultation session! https://lnkd.in/eXtWADgH #MLOps #Xomnia

?? Dive into the realm of MLOps Tools, where data science, software engineering and operations converge. Discover how these tools are shaping the future of machine learning integration in this guide by TechDogs! https://bit.ly/3SrwKCE #MLOps #MachineLearning #TechInnovation #TechDogs #TDArticleAlert #Technology #TechTrends #TechDogsInsights

I've had a crack at writing a piece on another topic I'm very passionate about, MLOps! This time attempting to explain what Machine Learning Operations (MLOps) is and why it is so important for Data & Analytics teams to embed when delivering Machine Learning products. ? In summary, by the end of this blog, you should be able to explain to someone: ? What the differences are between a?Machine Learning Algorithm,?Machine Learning Model,?Machine Learning Pipeline, and a?Machine Learning Product. ? What the?Machine Learning Model lifecycle?is and the journey it takes the Machine Learning model through — from?experimentation?to?productionisation. ? How it is the delivery of the?Machine Learning product?that has brought about the requirement for an entirely new set of skills and knowledge to be embedded into Data & Analytics teams. Ultimately giving birth to the?Machine Learning Engineer?role. ? Sitting on top of the Machine Learning Model lifecycle is the?MLOps lifecycle, across which the ultimate role of the Machine Learning Engineer sits. With the MLOps team being made up of these MLE positions. ? The challenges that ultimately motivate Data & Analytics teams to embed MLOps into the delivery of Machine Learning products. Past publications: ? Making Early Investments Into Your Data Teams: https://lnkd.in/ejX2TcSa #mlops #machinelearning #machinelearningengineer #data #analytics

I found it would be helpful to document and share my experiences navigating model deployment on Databricks during my time so far at AnthologyAI. This is my first blog post, so I appreciate any feedback or topics for further interest! #mlops #modeldeployment #databricks #unitycatalog #datascience

Data, as a competency, is cursed by its esoteric nature (“if you know, you know and if you don’t, you have no idea what I’m trying to sell you”). As a result, it turns out that most commercialization of data ends up feeling like sleight of hand, full of lofty and utopian statements that reflect hypothetical data-driven decisions and processes few organizations are in any way equipped to implement (Ahem, the dying art of the programmatic media brief). Despite this being the grease in the wheels that drives industries, budgets and business models, “the prestige” sells short the challenging work of building and executing pipelines, models and automations that make Data (and Analytics) as a service much more than pulling a quarter from behind your ear - and get the most accurate, highest-signal data available whenever you need to make a (real) data-driven decision. This is why I personally love to see practitioner-led and practioner-addressable content like this from AnthologyAI Sr. ML Engineer Awnish C. - on the building blocks of model deployment on the Unity Catalog and working with advanced capabilities of our partners at Databricks.

Hey folks! I've been getting a lot of DMs regarding #mlops #aiandml #sde #microservices for guidance, so decided to take action on it. I'm excited to help folks out and give back to the community via topmate.io. Don't hesitate to reach out and go ahead to book 1:1 call, have clear all your doubts. Follow Atul Anand for more!!!!!!!!!!!! #datascience #machinelearningalgorithms #deeplearningalgorithms #data #algorithms

#Snowflake #Cortex is going to be generally available on May 7th. Snowflake Cortex's serverless #LLM functions keep data secure in Snowflake allowing users to leverage the power of summarization and generation using Large Language Models (LLMs) from various sources. #Snowflake #Cortex can be used for text analytics, improving customer experiences and creating chatbots from documents. https://lnkd.in/dCHyGQQp

Looking into boosting your machine learning game? Let's talk #MLOps! ?? Just like you, we've felt the thrill and challenge of navigating #MachineLearning. That's why I wanted to share our MLOps expertise with you. With tailored MLOps solutions, you can confidently scale your machine learning initiatives while ensuring consistency, reproducibility, and cost-effectiveness. No more resource constraints! Plus, our experienced engineers work with top-notch tools that will help you get smoother collaboration between your data science, development, and operations teams. Ready to begin your journey? Click here to explore more about our MLOps solutions: https://lnkd.in/gjAEVVgb Excited to innovate smarter with you! #MachineLearningApproach #DataScience #TechInnovation #MachineLearningOperations

? New integration with Databricks MLflow! ? This partnership brings together Giskard's LLM evaluation capabilities and MLflow's model management features. Databricks users can now automatically identify vulnerabilities on ML models and LLMs, generate domain-specific tests, and compare model performance across different versions. What's Giskard's open-source scan?? It ensures the automatic identification of vulnerabilities in ML models and LLMs, such as hallucinations, reliability and robustness issues. Learn more about the integration ?? https://lnkd.in/eNxnPM_5 #databricks #mlflow #LLMeval #MLOps