IBM Data Science in Practice

MARCH 8, 2023

Source: IBM Cloud Pak for Data Feature Catalog Users can manage feature definitions and enrich them with metadata, such as tags, transformation logic, or value descriptions. Source: IBM Cloud Pak for Data MLOps teams often struggle when it comes to integrating into CI/CD pipelines. Spark, Flink, etc.)

Machine Learning 264

Machine Learning Machine Learning ML ML 264

ODSC - Open Data Science

FEBRUARY 2, 2023

To get a better grip on those changes we reviewed over 25,000 data scientist job descriptions from that past year to find out what employers are looking for in 2023. Much of what we found was to be expected, though there were definitely a few surprises. You’ll see specific tools in the next section.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

phData

MARCH 1, 2024

CREATE TABLE CUSTOMER_TABLE USING TEMPLATE ( SELECT ARRAY_AGG(OBJECT_CONSTRUCT(*)) WITHIN GROUP (ORDER BY order_id) FROM TABLE( INFER_SCHEMA( LOCATION=>'@csv_stage/customer-initial-data.csv', FILE_FORMAT=>'csv_format' ) )); This creates CUSTOMER_TABLE with the definition generated by INFER_SCHEMA.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Flipboard

MARCH 6, 2023

In the previous tutorial of this series, we built the dataset and data pipeline for our Siamese Network based Face Recognition application. Specifically, we looked at an overview of triplet loss and discussed what kind of data samples are required to train our model with the triplet loss.

Deep Learning 97

Deep Learning Deep Learning Data Pipeline Computer Science 97

Tableau

OCTOBER 8, 2021

With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics. Connecting directly to this semantic layer will help give customers access to critical business data in a safe, governed manner. Direct connection to Google BigQuery.

Tableau 133

Tableau Analytics Analytics Machine Learning 133

phData

AUGUST 17, 2023

Data pipeline orchestration tools are designed to automate and manage the execution of data pipelines. These tools help streamline and schedule data movement and processing tasks, ensuring efficient and reliable data flow. This enhances the reliability and resilience of the data pipeline.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Tableau

OCTOBER 8, 2021

With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics. Connecting directly to this semantic layer will help give customers access to critical business data in a safe, governed manner. Direct connection to Google BigQuery.

Tableau 98

Tableau Analytics Analytics Machine Learning 98

Alation

SEPTEMBER 21, 2021

DataOps is about the continuous development, testing and deployment of data products, and the data itself. Intelligence about where data is, what it looks like, what it means and how it is flowing to combat the 3 D’s and help Gen-D visualize data pipelines from producer to consumer.

DataOps 52

DataOps Data Governance Data Pipeline 52

Alation

JANUARY 26, 2023

That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. However, data needs to be easily accessible, usable, and secure to be useful — yet the opposite is too often the case. Narrow the scope It’s tempting to mark huge swaths of data as critical.

Data Governance 52

Data Governance Data Engineering Data Engineer Data Engineering 52

Iguazio

JANUARY 22, 2024

Definitions: Foundation Models, Gen AI, and LLMs Before diving into the practice of productizing LLMs, let’s review the basic definitions of GenAI elements: Foundation Models (FMs) - Large deep learning models that are pre-trained with attention mechanisms on massive datasets. This helps cleanse the data.

Data Pipeline 52

Data Pipeline ML ML Database 52

phData

SEPTEMBER 13, 2023

Third-Party Tools Third-party tools like Matillion or Fivetran can help streamline the process of ingesting Salesforce data into Snowflake. With these tools, businesses can quickly set up data pipelines that automatically extract data from Salesforce and load it into Snowflake.

Tableau 52

Tableau Data Pipeline Data Silos Analytics 52

The MLOps Blog

MARCH 15, 2023

The data pipelines can be scheduled as event-driven or be run at specific intervals the users choose. Below are some pictorial representations of simple ETL operations we used for data transformation. But there is definitely room for improvement in our deployment as well. For more information, please refer to this video.

AWS 52

AWS ETL ML ML 52

PyImageSearch

FEBRUARY 5, 2024

lossTracker=keras.metrics.Mean(name="loss"), ) Loading the Trained Siamese Model for Evaluation Now that we have completed the definition of our metrics function, let us load our trained Siamese model and evaluate its performance. On Line 29 , we get our modelPath , where we save our trained Siamese model after training.

Database 60

Database Data Pipeline Deep Learning Deep Learning 60

The MLOps Blog

MARCH 20, 2023

However, in scenarios where dataset versioning solutions are leveraged, there can still be various challenges experienced by ML/AI/Data teams. Data aggregation: Data sources could increase as more data points are required to train ML models. Existing data pipelines will have to be modified to accommodate new data sources.

ML 59

ML ML Machine Learning Machine Learning 59

The MLOps Blog

MARCH 28, 2024

We’ll explore how factors like batch size, framework selection, and the design of your data pipeline can profoundly impact the efficient utilization of GPUs. We need a well-optimized data pipeline to achieve this goal. The pipeline involves several steps. What should be the GPU usage?

Deep Learning 52

Deep Learning Deep Learning Data Pipeline Machine Learning 52

phData

SEPTEMBER 5, 2023

Let’s briefly look at the key components and their roles in this process: Azure Data Factory (ADF) : ADF will serve as our data orchestration and integration platform. It enables us to create, schedule, and monitor the data pipeline, ensuring seamless movement of data between the various sources and destinations.

Azure 52

Azure Analytics Analytics Data Pipeline 52

PyImageSearch

JUNE 5, 2023

Specifically, we will develop our data pipeline, implement the loss functions discussed in Part 1 and write our own code to train the CycleGAN model end-to-end using Keras and TensorFlow. With this, we finish the definition of our TrainMonitor class. Finally, we combine and consolidate our entire training data (i.e.,

Deep Learning 56

Deep Learning Deep Learning Data Pipeline Python 56

Flipboard

AUGUST 17, 2023

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. With this Spark connector, you can easily ingest data to the feature group’s online and offline store from a Spark DataFrame.

ML 93

ML ML AWS Data Warehouse 93

phData

SEPTEMBER 15, 2023

Conclusion Integrating Salesforce data with Snowflake’s Data Cloud using Tableau CRM Sync Out can benefit organizations by consolidating internal and third-party data on a single platform, making it easier to find valuable insights while removing the challenges of data silos and movement.

Data Warehouse 52

Data Warehouse Tableau Data Silos Analytics 52

Smart Data Collective

OCTOBER 17, 2022

It may conflict with your data governance policy (more on that below), but it may be valuable in establishing a broader view of the data and directing you toward better data sets for your main models. Data pipeline maintenance. It adds a layer of bureaucracy to data engineering that you may like to avoid.

Big Data 106

Big Data Big Data Data Engineering Data Engineer 106

phData

MARCH 26, 2024

Explore phData's Snowflake Services Closing Snowflake’s Hybrid tables are a powerful new feature that can help organizations break down data silos and bring transactional and analytical data together in one platform. Hybrid tables can streamline data pipelines, reduce costs, and unlock deeper insights from data.

Clustering 52

Clustering Internet of Things Data Silos Analytics 52

DagsHub

APRIL 7, 2024

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. Data scientists and machine learning engineers need to collaborate to make sure that together with the model, they develop robust data pipelines.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

phData

SEPTEMBER 7, 2023

We’ve had many customers performing migrations between these platforms, and as a result, they have a lot of Data Definition Language (DDL) and Data Markup Language (DML) that needs to be translated between SQL dialects. Let’s take a look at some of the more interesting translations.

SQL 52

SQL Data Profiling Data Pipeline Database 52

phData

JANUARY 10, 2024

The data source tool can also directly generate the Data Definition Language (DDL) for these tables as well if you decide not to use dbt! This allows you to better understand the existing structures that are in place and more accurately perform your migration (or generate documentation, everybody’s favorite!).

Data Warehouse 52

Data Warehouse Data Profiling Data Pipeline Database 52

phData

MARCH 1, 2023

When customers are looking to perform a migration, one of the first things that needs to occur is an assessment of the level of effort to migrate existing data definition language (DDL) and data markup language (DML). The first one we want to talk about is the Toolkit SQL analyze command.

SQL 52

SQL Data Pipeline Data Quality Database 52

Alation

MAY 31, 2023

American Family Insurance: Governance by Design – Not as an Afterthought Who: Anil Kumar Kunden , Information Standards, Governance and Quality Specialist at AmFam Group When: Wednesday, June 7, at 2:45 PM Why attend: Learn how to automate and accelerate data pipeline creation and maintenance with data governance, AKA metadata normalization.

Data Governance 52

Data Governance DataOps Data Pipeline Business Intelligence 52

AWS Machine Learning Blog

SEPTEMBER 18, 2023

It integrates smoothly with other data processing libraries like Spark, Pandas, NumPy, and more, as well as ML frameworks like TensorFlow and PyTorch. This allows building end-to-end data pipelines and ML workflows on top of Ray. The goal is to make distributed data processing and ML easier for practitioners and researchers.

Machine Learning 101

Machine Learning Machine Learning ML ML 101

phData

JUNE 26, 2023

As customers are performing platform migrations, they frequently need to translate existing stored procedures, data definition language (DDL), and data markup language (DML) to the target system. SQL Translation Updates SQL Translation is another major component of the Toolkit CLI.

SQL 52

SQL Data Profiling Data Pipeline Data Governance 52

Snorkel AI

JUNE 22, 2023

Tools like Bigeye, Deequ, and HoloClean are leading the charge here as we see the shift moving from data health and data monitoring to fixing data on the right side—changing data to improve ML and to improve operations, there are definitely risks. Can a data-centric approach help? Most, most definitely.

Data Quality 52

Data Quality AI AI ML 52

Snorkel AI

JUNE 22, 2023

Tools like Bigeye, Deequ, and HoloClean are leading the charge here as we see the shift moving from data health and data monitoring to fixing data on the right side—changing data to improve ML and to improve operations, there are definitely risks. Can a data-centric approach help? Most, most definitely.

Data Quality 52

Data Quality AI AI ML 52

Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

Data Pipeline 52

Data Pipeline Exploratory Data Analysis Data Scientist Machine Learning 52

Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

Data Pipeline 52

Data Pipeline Exploratory Data Analysis Data Scientist Machine Learning 52

Alation

JANUARY 17, 2023

You should look for a data warehouse that is scalable, flexible, and efficient. Popular cloud data warehouses today include Snowflake, Databricks, and BigQuery. If your organization is large, you definitely need to look for robustness. Good data warehouses should be reliable.

Data Warehouse 52

Data Warehouse ETL Cloud Data Tableau 52

The MLOps Blog

AUGUST 4, 2023

1 Data pipeline integration 2 Consistency across experiments 3 Simplified model versioning 4 Cross-functional collaboration 5 Atomic changes 6 Unification of coding standards Data pipeline integration Machine learning projects often involve data pipelines that preprocess, transform, and feed data into the model.

ML 52

ML ML Machine Learning Machine Learning 52

Alation

APRIL 14, 2022

Data pipeline orchestration. Moving/integrating data in the cloud/data exploration and quality assessment. It’s not a simple definition. So how do you take full advantage of the cloud? Collaboration and governance. Low-code, no-code operation. Support for languages and SQL.

Data Governance 59

Data Governance ML ML Cloud Data 59

DagsHub

DECEMBER 5, 2023

The Git integration means that experiments are automatically reproducible and linked to their code, data, pipelines, and models. Other tools Even though we have already covered 7 different experiment tracking tools/frameworks, that definitely does not exhaust all the available options.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

phData

MAY 25, 2023

While the loss of certain DAX functions is definitely a shortcoming that we hope Microsoft will address in the near future, the impact of these lost DAX functions is not necessarily as big as you would expect. To get around losing Time Intelligence functions, a robust calendar table is suggested to reference for time-based metrics.

Power BI 84

Power BI Analytics Analytics Azure 84