I recently worked on a multi-label classification pipeline on Databricks, using embeddings, scikit-learn classifiers, Delta Lake, and MLflow to track experiments.One result caught my attention: the embedding model that ranked fourth in my initial scr...
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...
On your closing question, the rule I'd use for the line: anything with a checkable ground truth, either inside the document or in a system of record, goes to deterministic logic, and the model only proposes. Amount in figures vs words, date validity,...
I have a time-series that I am trying to model using auto.arima in R. Upon execution on a cluster runtime with 96GB memory and 48 cores, I observed that the code kept running without any end in sight. It ran for over a day without finishing.Curious, ...
Okay, this is resolved. Windows doesn't allow forking, so parallel = TRUE was having no effect. But Databricks runtime runs on Linux, which enables forking. So keeping parallel = TRUE seems to be having the opposite effect than the anticipated one: d...
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...
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...
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...
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...
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...
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...
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 ...
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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...
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!
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...
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.
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...
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.
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 ...
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 ...
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...
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.
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 ...
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...
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()....
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...
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 ...
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...
