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Automated Continuous Epicor ERP Replication to Databricks

Always-on applications rely on automatic failover capabilities and real-time data access.

CData Sync integrates live Epicor ERP data into your Databricks instance, allowing you to consolidate all of your data into a single location for archiving, reporting, analytics, machine learning, artificial intelligence and more.

In the Schedule section, you can schedule a job to run automatically, configuring the job to run after specified intervals ranging from once every 15 minutes to once every month.

Databricks with Machine Learning flow all in one solution #2021

Databricks Machine Learning is an integrated end-to-end machine learning environment for experiment tracking, model training, feature development , management, and model serving.

Databricks Runtime ML includes many external libraries, including tensorflow, pytorch, Horovod, scikit-learn and xgboost, and provides extensions to improve performance, including GPU acceleration inxgboost, distributed deep learning usinghorovodrunner, and model checkpointing using aDatabricks File System (DBFS) FUSE mount.

On this page, you configure the automl process, specifying the dataset, problem type, target or label column to predict, metric to use to evaluate and score the experiment runs, and stopping conditions.

You can also useDatabricks automl, which automatically prepares a dataset for model training, performs a set of trials using open-source libraries such as scikit-learn and xgboost, and creates a Python notebook with the source code for each trial run so you can review, reproduce, and modify the code.

In Databricks, you can usemlflow trackingto help you keep track of the model development process, including parameter settings or combinations you have tried and how they affected the model’s performance.

Model Registry provides chronological model lineage (which mlflow experiment and run produced the model at a given time), model versioning, stage transitions (for example, from staging to production or archived), and email notifications of model events.

I'm Baishalini Sahu working as a data scientist specializing in Artificial intelligence and machine learning, message behind this article on Databricks with ml flow to optimize our time and workflows so businesses can grow without adding more headcount and reps can advance their AI careers.

Do we really need a data lakehouse? Hashing AI, cloud, and customer proof points with Databricks CEO Ali Ghodsi

Readers know I'm not a fan of enterprise buzzword bingo.

didn't get here first: Diginomica contributor Neil Raden already skewered data lakehouses (and other cloudy data terms) in his piece Data lakes, data lakehouses and cloud data warehouses - which is real?

As Ghodsi told me: If you look at the enterprise space today, especially since the pandemic started, everyone wants to quickly figure out how they can get AI into their business and become more data-driven...

I think something happened in business leaders' minds in the last year, as they were sitting home and everything was turned upside down.

There are vendors that do data management and data processing, like Snowflake, and they're great for data processing - but they have no AI or machine capabilities whatsoever.

They're great for the machine learning algorithms, but they actually are not in the business of processing massive petabytes of data...

they found the genome responsible for chronic liver disease in Databricks, using AI, and they actually have a drug [to treat that] now.

And you want to quickly iterate between those, and find the needle in the haystack: the gene markers that are responsible for that disease.

They reduced the time it takes data scientists and computational biologists to run queries on their entire dataset, from 30 minutes to down 3 seconds - a 600x improvement.

It's all machine learning, and it's based on real-time patterns, how people have been buying stuff, the diurnal patterns, what's happening in their other stores, and then that forecasting is done.

As it turns out, 70% accuracy is still a big deal: It's massively cost efficient to go replace that right away, when we give that prediction.

I realize there is some benefit into drilling in, as Raden did, to understand the semantics - and say, the difference between a cloud data warehouse and a data lakehouse.

Here's what I care about: You say you're using AI and 'big data' to help customers - let's see the proof points.

Ghodsi also wanted to talk about Comcast, and how Databricks uses voice data from Comcast and converts it to digital data inside their Data Lakehouse, and 'actuates it in real-time.'

From the press release: Today, at the Data + AI Summit, Databricks announced the launch of a new open source project called Delta Sharing, the world's first open protocol for securely sharing data across organizations in real-time, completely independent of the platform on which the data resides.

If traction can be achieved, this opens up possibilities, beyond the data access issues inherent in proprietary systems.