ODSC - Open Data Science

FEBRUARY 2, 2023

Computer Science and Computer Engineering Similar to knowing statistics and math, a data scientist should know the fundamentals of computer science as well. While knowing Python, R, and SQL are expected, you’ll need to go beyond that. Big Data As datasets become larger and more complex, knowing how to work with them will be key.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

ODSC - Open Data Science

FEBRUARY 17, 2023

Cloud Computing, APIs, and Data Engineering NLP experts don’t go straight into conducting sentiment analysis on their personal laptops. Data Engineering Platforms Spark is still the leader for data pipelines but other platforms are gaining ground. Knowing some SQL is also essential.

Deep Learning 111

Deep Learning Deep Learning Data Science Natural Language Processing 111

AWS Machine Learning Blog

MARCH 8, 2023

In order to train a model using data stored outside of the three supported storage services, the data first needs to be ingested into one of these services (typically Amazon S3). This requires building a data pipeline (using tools such as Amazon SageMaker Data Wrangler ) to move data into Amazon S3.

ML 105

ML ML AWS Python 105

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

AWS Machine Learning Blog

DECEMBER 6, 2023

To overcome these limitations, we propose a solution that combines RAG with metadata and entity extraction, SQL querying, and LLM agents, as described in the following sections. Typically, these analytical operations are done on structured data, using tools such as pandas or SQL engines.

SQL 101

SQL AWS Database Analytics 101

Pickl AI

JULY 3, 2023

Here are steps you can follow to pursue a career as a BI Developer: Acquire a solid foundation in data and analytics: Start by building a strong understanding of data concepts, relational databases, SQL (Structured Query Language), and data modeling.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Dataconomy

MAY 16, 2023

Key skills and qualifications for machine learning engineers include: Strong programming skills: Proficiency in programming languages such as Python, R, or Java is essential for implementing machine learning algorithms and building data pipelines.

Data Scientist 156

Data Scientist ML ML Machine Learning 156

ODSC - Open Data Science

DECEMBER 5, 2023

We had bigger sessions on getting started with machine learning or SQL, up to advanced topics in NLP, and of course, plenty related to large language models and generative AI. You can see our photos from the event here , and be sure to follow our YouTube for virtual highlights from the conference as well.

Data Science 98

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 98

Heartbeat

JANUARY 5, 2024

Introduction to LangChain for Including AI from Large Language Models (LLMs) Inside Data Applications and Data Pipelines This article will provide an overview of LangChain, the problems it addresses, its use cases, and some of its limitations. Python : Great for including AI in Python-based software or data pipelines.

AI 52

AI AI Data Pipeline Deep Learning 52

ODSC - Open Data Science

APRIL 26, 2023

Though scripted languages such as R and Python are at the top of the list of required skills for a data analyst, Excel is still one of the most important tools to be used. Because they are the most likely to communicate data insights, they’ll also need to know SQL, and visualization tools such as Power BI and Tableau as well.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

IBM Journey to AI blog

NOVEMBER 3, 2023

In this spirit, IBM introduced IBM Event Automation with an intuitive, easy to use, no code format that enables users with little to no training in SQL, java, or python to leverage events, no matter their role. Imagine if you could have a continuous view of your events with the freedom to experiment on automations.

Apache Kafka 78

Apache Kafka Data Warehouse Data Pipeline SQL 78

IBM Journey to AI blog

SEPTEMBER 19, 2023

To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala. And you should have experience working with big data platforms such as Hadoop or Apache Spark.

Data Science 84

Data Science Analytics Analytics Data Scientist 84

ODSC - Open Data Science

SEPTEMBER 29, 2023

Furthermore, we’ve developed data encryption and governance solutions for HPCC Systems to help secure data, ensure it is only accessed by appropriate personnel, and to create audit trails to ensure data security SLAs and regulations are met. It truly is an all-in-one data lake solution. Tell me more about ECL.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

The MLOps Blog

JANUARY 23, 2023

Dolt LakeFS Delta Lake Pachyderm Git-like versioning Database tool Data lake Data pipelines Experiment tracking Integration with cloud platforms Integrations with ML tools Examples of data version control tools in ML DVC Data Version Control DVC is a version control system for data and machine learning teams.

ML 52

ML ML Data Lakes Machine Learning 52

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. What are Orchestration Tools?

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

O'Reilly Media

SEPTEMBER 15, 2021

When we looked at the most popular programming languages for data and AI practitioners, we didn’t see any surprises: Python was dominant (61%), followed by SQL (54%), JavaScript (32%), HTML (29%), Bash (29%), Java (24%), and R (20%). The tools category includes tools for building and maintaining data pipelines, like Kafka.

AI 145

AI AI Azure AWS 145

phData

FEBRUARY 7, 2024

Snowpark is the set of libraries and runtimes in Snowflake that securely deploy and process non-SQL code, including Python, Java, and Scala. On the server side, runtimes include Python, Java, and Scala in the warehouse model or Snowpark Container Services (public preview). filter(col("id") == 1).select(col("name"),

Python 52

Python ML ML SQL 52

phData

JANUARY 31, 2024

Applying Machine Learning with Snowpark Now that we have our data from the Snowflake Marketplace, it’s time to leverage Snowpark to apply machine learning. Python has long been the favorite programming language of data scientists. For a short demo on Snowpark, be sure to check out the video below.

Machine Learning 52

Machine Learning Machine Learning Python Data Scientist 52

phData

FEBRUARY 14, 2023

Source data formats can only be Parquer, JSON, or Delimited Text (CSV, TSV, etc.). Streamsets Data Collector StreamSets Data Collector Engine is an easy-to-use data pipeline engine for streaming, CDC, and batch ingestion from any source to any destination.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

The MLOps Blog

JANUARY 26, 2024

Reference table for which technologies to use for your FTI pipelines for each ML system. Related article How to Build ETL Data Pipelines for ML See also MLOps and FTI pipelines testing Once you have built an ML system, you have to operate, maintain, and update it. All of them are written in Python.

Machine Learning 73

Machine Learning Machine Learning ML ML 73

ODSC - Open Data Science

FEBRUARY 24, 2023

These tools will help make your initial data exploration process easy. ydata-profiling GitHub | Website The primary goal of ydata-profiling is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Output is a fully self-contained HTML application. You can watch it on demand here.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

phData

DECEMBER 4, 2023

Copy Into When loading data into Snowflake, the very first and most important rule to follow is: do not load data with SQL inserts! Loading small amounts of data is cumbersome and costly: Each insert is slow — and time is credits. For example: ODBC JDBC Python Snowflake Connector And, generally, things will be okay.

Clustering 98

Clustering Database SQL Data Pipeline 98

Applied Data Science

AUGUST 2, 2021

Automation Automating data pipelines and models ➡️ 6. The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data. The Data Engineer Not everyone working on a data science project is a data scientist.

Data Science 130

Data Science Data Scientist Data Analyst ML 130

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. as the image and Glue Python [PySpark and Ray] as the kernel, then choose Select.

ML 93

ML ML AWS Data Warehouse 93

phData

FEBRUARY 24, 2023

When you think of the lifecycle of your data processes, Alteryx and Snowflake play different roles in a data stack. Alteryx provides the low-code intuitive user experience to build and automate data pipelines and analytics engineering transformation, while Snowflake can be part of the source or target data, depending on the situation.

Analytics 52

Analytics Analytics Database Python 52

The MLOps Blog

SEPTEMBER 7, 2023

These combinations of Python code and SQL play a crucial role but can be challenging to keep them robust for their entire lifetime. This involves considering the entire system’s architecture and components, including training, inference, data pipelines, and integration.

Machine Learning 52

Machine Learning Machine Learning ETL ML 52

ODSC - Open Data Science

AUGUST 23, 2023

That’s a problem when you’re trying to work with that data in pandas because you have to pull the dataset into the memory of your machine, which can be slow, expensive, and lead to fatal out-of-memory issues. Ponder solves this problem by translating your pandas code to SQL that can be understood by your data warehouse.

Data Warehouse 52

Data Warehouse Data Science Database SQL 52

The MLOps Blog

JUNE 27, 2023

For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc., It could help you detect and prevent data pipeline failures, data drift, and anomalies. and Pandas or Apache Spark DataFrames.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

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

Snorkel AI

JANUARY 24, 2023

Snowpark, which is Snowflake’s developer framework that extends the benefits of the Data Cloud beyond SQL to Python, Scala, and Java, can be used to scale batch inference across your Snowflake data warehouse. Models built in Snorkel Flow can be registered on Snowflake as Snowpark UDFs.

AI 52

AI AI ML ML 52

Snorkel AI

JANUARY 24, 2023

Snowpark, which is Snowflake’s developer framework that extends the benefits of the Data Cloud beyond SQL to Python, Scala, and Java, can be used to scale batch inference across your Snowflake data warehouse. Models built in Snorkel Flow can be registered on Snowflake as Snowpark UDFs.

AI 52

AI AI ML ML 52

DagsHub

APRIL 21, 2024

Scikit-learn Scikit-learn is a machine learning library in Python that is majorly used for data mining and data analysis. Apache Airflow Apache Airflow is an open-source workflow orchestration tool that can manage complex workflows and data pipelines.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Alation

JANUARY 17, 2023

A legacy data stack usually refers to the traditional relational database management system (RDBMS), which uses a structured query language (SQL) to store and process data. While an RDBMS can still be used in a modern data stack, it is not as common because it is not as well-suited for managing big data.

Data Warehouse 52

Data Warehouse ETL Cloud Data Tableau 52

IBM Journey to AI blog

JULY 17, 2023

Within watsonx.ai, users can take advantage of open-source frameworks like PyTorch, TensorFlow and scikit-learn alongside IBM’s entire machine learning and data science toolkit and its ecosystem tools for code-based and visual data science capabilities.

AI 67

AI AI Machine Learning Machine Learning 67