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Machine Learning


Building an Automated ML Pipeline with a Feature Store Using Iguazio & Snowflake

When operationalizing machine and deep learning, a production-first approach is essential for moving from research and development to scalable production pipelines in a much faster and more effective manner. Without the need to refactor code, add glue logic and spend significant efforts on data and ML engineering, more models will make it to production and with less issues like drift.


Optimizing a Huggingface model with TensorRT and serving it on ClearML

What if I want to serve a Huggingface model on ClearML? Where do I start? In general, machine learning engineers know by now that a good model serving engine is invaluable when serving models in production. These days, NVIDIA’s Triton inference engine is a popular option to do so, but it is lacking in some respects.

Get to anomaly detection faster with Cloudera's Applied Machine Learning Prototypes

The Applied Machine Learning Prototype (AMP) for anomaly detection reduces implementation time by providing a reference model that you can build from. Built by Fast Forward Labs, and tested on AMD EYPC™ CPUs with Dell Technologies, this AMP enables data scientists across industries to truly practice predictive maintenance.

ModelOps, ML Validation & ML Assurance: The Next Frontiers of AI-led Digital Assurance

Like humans, Machine Learning (ML) models can recognize intricate patterns and anticipate the outcome of new data. On some natural language problems, ML models have even surpassed human performance. But much like people, ML models are susceptible to error. For every ML application in the real world, estimating how frequently a model will be inaccurate is essential. Intuitively presenting information and emphasizing the best ways to enhance a model are equally important.


Best 13 Free Financial Datasets for Machine Learning

Financial services companies are leveraging data and machine learning to mitigate risks like fraud and cyber threats and to provide a modern customer experience. By following these measures, they are able to comply with regulations, optimize their trading and answer their customers’ needs. In today’s competitive digital world, these changes are essential for ensuring their relevance and efficiency.


Iguazio Product Update: Optimize Your ML Workload Costs with AWS EC2 Spot Instances

Iguazio users can now run their ML workloads on AWS EC2 Spot instances. When running ML functions, you might want to control whether to run on Spot nodes or On-Demand compute instances. When deploying Iguazio MLOps platform on AWS, running a job (e.g. model training) or deploying a serving function users are now able to choose to deploy it on AWS EC2 Spot compute instances.

Enterprises Need to Deploy, Monitor, and Govern ML Models to Solve Real-World Use Cases

The hype about AI from a few years ago is undeniably a reality today, with every business searching for ways to take advantage of the potential long-term advantages. The number of businesses employing the top AI and data scientist teams to support their company performance is expanding daily, regardless of whether you operate a company that focuses on retail, finance, construction, or anything in between.