- 2439 Views
- 2 replies
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Getting error when running databricks deploy bundle command
HI all,I am trying to implement MLOps project using https://github.com/databricks/mlops-stacks repo.I have created azure databricks with Premium (+ Role-based access controls) (Click to change) and following bundle creation and deploy using uRL: http...
- 2439 Views
- 2 replies
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Greetings @jitenjha11, I did some digging and here is what I found. @emma_s is on the right track. Make sure the identity running the deploy has workspace access and cluster creation rights, and that the workspace is attached to a Unity Catalog metas...
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- 252 Views
- 2 replies
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MLflow traces accepted by StartTraceV3 (200 OK) but never stored: GetTrace returns NOT_FOUND
Hi all,We're seeing cases where traces go missing in an experiment-backed MLflow trace store (not Unity Catalog). The client gets a successful response for both trace writes, and the server echoes back the same trace id, but the trace is never stored...
- 252 Views
- 2 replies
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Short version: your new observation rules out a client-side drop. If a retried StartTraceV3 with the same id returns "already exists" while GetTrace and SearchTraces return NOT_FOUND, the server accepted and recorded something for that id, but the r...
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- 187 Views
- 0 replies
- 1 kudos
Document AI is a pipeline, not a model
When a financial document needs to be approved or rejected, there usually isn't one model making the decision.In the check / payment-slip systems I worked on, the pipeline looked more like this:1. Multimodal models + OCR to extract fields2. YOLO to d...
- 187 Views
- 0 replies
- 1 kudos
- 524 Views
- 3 replies
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Resolved! How do you train forecasting models for events that never happened?
Most of my time-series work hits the same wall. The models only learn from scenarios that already happened.Rare events barely show up in the training data. Demand spikes, stockouts, and extreme regimes are exactly the cases I care about. They are als...
- 524 Views
- 3 replies
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Hey @ThiamLee , great question, and one I've wrestled with too. Haven't tried Remix Labs specifically, tho.My take, after going back and forth between pure historical data and augmentation: real data first, augment second, but only where augmentation...
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- 5384 Views
- 3 replies
- 1 kudos
Resolved! Docker image with libraries + MLFlow Experiments
Hi everybody,I have a scenario where we have multiple teams working with Python and R, and this teams uses a lot of different libraries. Because of this dozen of libraries, the cluster start took much time. Then I created a Docker image, where I can ...
- 5384 Views
- 3 replies
- 1 kudos
- 1 kudos
Mojang's commitment to regularly releasing a new Minecraft Patch Download demonstrates their deep respect and genuine appreciation for the passionate global community that has made Minecraft the most beloved game in history.Click Here
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- 333 Views
- 2 replies
- 2 kudos
Image Annotation
What is Image Annotation?What are the Steps of Image Annotation?What are the Different Techniques of Image Annotation?Types Used in Image AnnotationHow are Companies Handling Image Annotation?Features to Look for in Image Annotation Service Providers...
- 333 Views
- 2 replies
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Great overview of image annotation! The sections on different techniques, real-world use cases, and pricing factors are especially helpful for anyone getting started with AI/ML. Looking forward to exploring the future trends in image annotation!
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- 1045 Views
- 5 replies
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Resolved! Difference between Workspace and Unity Catalog experiments when using MLflow autologging?
Hi everyone,I am trying to understand the exact differences between using Workspace experiments versus Unity Catalog experiments, specifically in the context of MLflow autologging (mlflow.autolog()).Does autologging behave differently depending on wh...
- 1045 Views
- 5 replies
- 6 kudos
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Great question! I’d also be interested to know if autologging has any behavioral or permission differences with Unity Catalog experiments, especially around governance and model lineage.
- 6 kudos
- 1159 Views
- 2 replies
- 1 kudos
Data practitioner in AI Era
As the AI revolution takes off in 2025, there is a renewed emphasis on adopting a Data-First approach. Organizations are increasingly recognizing the need to establish a robust data foundation while preparing a skilled fleet of Data Engineers to tack...
- 1159 Views
- 2 replies
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Absolutely agree! AI is only as powerful as the data behind it. The evolving role of data engineers in building that foundation is definitely worth discussing.
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- 508 Views
- 1 replies
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Resolved! How are you combining forecasting models for time-series work?
Most time-series forecasting jobs do not come down to one model. Teams run N-BEATS, NHITS, LSTM, and GRU, then struggle to combine them well.I want to open a discussion on model synthesis. Not data cleaning. Not generative AI. I mean merging several ...
- 508 Views
- 1 replies
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Hi,Good topic. A few things that have held up for us.On weighting across horizons, fit weights per horizon step rather than one global set, and derive them from rolling origin backtests so the errors are genuinely out of sample. Models tend to trade ...
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- 1126 Views
- 8 replies
- 4 kudos
Resolved! fastai import in databricks broken
Has anyone else had a problem today (2026-09-04) with imports from fastai?The error message points to a problem with the underlying fastcore package:AttributeError: 'Function' object attribute '__doc__' is read-onlyI'm getting the same error using fa...
- 1126 Views
- 8 replies
- 4 kudos
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I’m seeing the same issue today—seems like the recent compute runtime update may be related. Hopefully someone from the fastai/Databricks side can confirm.
- 4 kudos
- 1929 Views
- 2 replies
- 1 kudos
Learn Databricks AI medium article series for fellow learners.
When it comes to machine learning, the platform plays a pivotal role in successful implementation. Databricks offers a best-in-class machine learning platform with cutting-edge features such as MLflow, Model Registry, Feature Store, and MLOps, which ...
- 1929 Views
- 2 replies
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Great share! Databricks really has built something special on the ML side — MLflow alone has become almost a default for experiment tracking, and pairing it with Model Registry and Feature Store makes the whole path from experimentation to productio...
- 1 kudos
- 423 Views
- 2 replies
- 1 kudos
Tuning with Optuna and MlflowSparkStudy
I am following the guide for tuning a model with Optuna and MlflowSparkStudy. My compute is configured with autoscaling enabled, with 1–2 Spark workers, each with 8 cores and 32 GB of memory. I set n_jobs=2 and trials=100, in mlflow_study.optimize()....
- 423 Views
- 2 replies
- 1 kudos
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Great questions—especially the distinction between Optuna’s trial-level parallelism and LightGBM’s intra-trial threading. The interaction with Spark autoscaling and data locality is also something I’d love to see documented with a concrete example. C...
- 1 kudos
- 501 Views
- 1 replies
- 4 kudos
End-to-End Streaming NLP Pipeline with GDELT, Azure Data Factory, ADLS Gen2 and Databricks
Building an End-to-End Streaming NLP Pipeline with GDELT, Azure Data Factory, ADLS Gen2 and DatabricksI recently worked on an end-to-end streaming NLP project using GDELT news data, Azure Data Factory, ADLS Gen2 and Azure Databricks.The goal was not ...
- 501 Views
- 1 replies
- 4 kudos
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Great end-to-end project! To take it to the next level, consider exploring these native Databricks capabilities.Lakehouse Monitoring: Track data drift and automate model retraining when performance drops.Model Serving: Deploy your models behind serve...
- 4 kudos
- 795 Views
- 4 replies
- 3 kudos
End-to-End Streaming NLP Pipeline with GDELT, Azure Data Factory, ADLS Gen2 and Databricks
I have been working on a project to understand Databricks end to end, rather than just loading some data and training a model.I picked GDELT news data and the use case is to identify supply chain disruption related news and eventually predict which e...
- 795 Views
- 4 replies
- 3 kudos
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@kartheek_rao, you are on the right track.Since you are doing clustering of new articles, it's unsupervised learning; you need to understand the feature engineering part more and the EDA part with MLFlow experiments. You have model monitoring as well...
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- 626 Views
- 2 replies
- 1 kudos
Model Serving An internal error occurred during feature store lookup all deploys failing
We can't deploy models to Model Serving. Failures started around 2026-08-16 and were intermittent at first — our current production config deployed cleanly on 08-20 — but since then every attempt fails at the feature store lookup setup step, with no ...
- 626 Views
- 2 replies
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Update — reproduced at minimum complexity, and the failure is unobservableWe stopped theorising and bisected with a deliberately trivial model: SimpleImputer + LogisticRegression, logged with fe.log_model, no custom code, on an online store created t...
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