ODSC - Open Data Science

MARCH 13, 2023

First, there’s a need for preparing the data, aka data engineering basics. Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.

Machine Learning 94

Machine Learning Machine Learning Data Wrangling Data Science 94

Flipboard

DECEMBER 11, 2024

Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Choose the plus sign and for Notebook , choose Python 3.

SQL 159

SQL AWS Data Lakes AI 159

ODSC - Open Data Science

SEPTEMBER 25, 2023

Tapping into these schemas and pulling out machine learning-ready features can be nontrivial as one needs to know where the data entity of interest lives (e.g., customers), what its relations are, and how they’re connected, and then write SQL, python, or other to join and aggregate to a granularity of interest.

Data Preparation 104

Data Preparation Machine Learning Machine Learning ML 104

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

Towards AI

MAY 8, 2024

Vertex AI assimilates workflows from data science, data engineering, and machine learning to help your teams work together with a shared toolkit and grow your apps with the help of Google Cloud. Conclusion Vertex AI is a major improvement over Google Cloud’s machine learning and data science solutions.

Machine Learning 104

Machine Learning Machine Learning ML ML 104

Towards AI

AUGUST 16, 2023

MLOps is the intersection of Machine Learning, DevOps, and Data Engineering. Zero, “ How to write better scientific code in Python,” Towards Data Science, Feb. Galarnyk, “ Considerations for Deploying Machine Learning Models in Production,” Towards Data Science, Nov. 15, 2022. [4]

Machine Learning 98

Machine Learning Machine Learning AI AI 98

AWS Machine Learning Blog

SEPTEMBER 20, 2023

SageMaker Studio allows data scientists, ML engineers, and data engineers to prepare data, build, train, and deploy ML models on one web interface. Finally, we deploy the ONNX model along with a custom inference code written in Python to Azure Functions using the Azure CLI. image and Python 3.0

ML 114

ML ML Azure AWS 114

AWS Machine Learning Blog

JULY 11, 2024

It supports all stages of ML development—from data preparation to deployment, and allows you to launch a preconfigured JupyterLab IDE for efficient coding within seconds. Specifically, we demonstrate how you can customize SageMaker Distribution for geospatial workflows by extending it with open-source geospatial Python libraries.

AWS 110

AWS ML ML Python 110

AWS Machine Learning Blog

JUNE 17, 2024

The training data used for this pipeline is made available through PrestoDB and read into Pandas through the PrestoDB Python client. The queries that are used to fetch data at training and batch inference steps are configured in the config file.

ML 111

ML ML AWS Machine Learning 111

AWS Machine Learning Blog

NOVEMBER 29, 2023

You can use this notebook job step to easily run notebooks as jobs with just a few lines of code using the Amazon SageMaker Python SDK. Data scientists currently use SageMaker Studio to interactively develop their Jupyter notebooks and then use SageMaker notebook jobs to run these notebooks as scheduled jobs.

ML 95

ML ML Data Scientist Python 95

DataRobot Blog

MARCH 16, 2023

The DataRobot team has been working hard on new integrations that make data scientists more agile and meet the needs of enterprise IT, starting with Snowflake. We’ve tightened the loop between ML data prep , experimentation and testing all the way through to putting models into production.

Data Scientist 52

Data Scientist ML ML Data Preparation 52

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).

Python 52

Python ML ML SQL 52

phData

AUGUST 2, 2024

These procedures are designed to automate repetitive tasks, implement business logic, and perform complex data transformations , increasing the productivity and efficiency of data processing workflows. Snowflake stored procedures and dbt Hooks are essential to modern data engineering and analytics workflows.

Data Pipeline 52

Data Pipeline Python Database SQL 52

Flipboard

NOVEMBER 24, 2023

JuMa is a service of BMW Group’s AI platform for its data analysts, ML engineers, and data scientists that provides a user-friendly workspace with an integrated development environment (IDE). It is powered by Amazon SageMaker Studio and provides JupyterLab for Python and Posit Workbench for R.

ML 152

ML ML AWS AI 152

ODSC - Open Data Science

NOVEMBER 19, 2024

Mustafa Hajij introduced TopoX, a comprehensive Python suite for topological deep learning. This session demonstrated how to leverage these tools using Python and PyTorch, offering attendees practical techniques to apply in their research and projects. Introduction to Containers for Data Science / Data Engineering with Michael A.

Deep Learning 52

Deep Learning Deep Learning Data Science AI 52

AWS Machine Learning Blog

SEPTEMBER 3, 2024

With the introduction of EMR Serverless support for Apache Livy endpoints , SageMaker Studio users can now seamlessly integrate their Jupyter notebooks running sparkmagic kernels with the powerful data processing capabilities of EMR Serverless. In this post, we build a Docker image that includes the Python 3.11

AWS 102

AWS Clustering Big Data Big Data 102

AWS Machine Learning Blog

OCTOBER 18, 2023

The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. He is passionate about helping customers to build scalable and modern data analytics solutions to gain insights from the data.

AWS 109

AWS ML ML Machine Learning 109

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 74

AI AI Machine Learning Machine Learning 74

phData

FEBRUARY 24, 2023

Alteryx provides organizations with an opportunity to automate access to data, analytics , data science, and process automation all in one, end-to-end platform. Its capabilities can be split into the following topics: automating inputs & outputs, data preparation, data enrichment, and data science.

Analytics 52

Analytics Analytics Database Python 52

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for data engineering and MLOps workflows.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Airflow for workflow orchestration Airflow schedules and manages complex workflows, defining tasks and dependencies in Python code. An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time.

AWS 106

AWS Machine Learning Machine Learning ML 106

Pickl AI

SEPTEMBER 5, 2024

Data Preparation: Cleaning, transforming, and preparing data for analysis and modelling. Collaborating with Teams: Working with data engineers, analysts, and stakeholders to ensure data solutions meet business needs.

Azure 52

Azure Data Scientist Data Science Machine Learning 52

Women in Big Data

JULY 19, 2024

We hope that the hackathon experience enhanced their exposure to real world data and skills to tackle real world challenges using data engineering and machine learning skills. We look forward to future hackathons and continuing our journey in data science!

Data Science 52

Data Science Big Data Big Data Machine Learning 52

ODSC - Open Data Science

DECEMBER 16, 2024

Youll gain immediate, practical skills in Python, data preparation, machine learning modeling, and retrieval-augmented generation (RAG), all leading up to AI Agents. Each course features focused, interactive sessions with hands-on notebooks and exercises, along with dedicated office hours.

Machine Learning 40

Machine Learning Machine Learning Data Preparation Artificial Intelligence 40

Pickl AI

APRIL 10, 2023

In case of professional Data Analysts, who might be engaged in performing experiments on data, standard SQL tools are required. Data Analysts need deeper knowledge on SQL to understand relational databases like Oracle, Microsoft SQL and MySQL. Moreover, SQL is an important tool for conducting Data Preparation and Data Wrangling.

Data Analyst 52

Data Analyst SQL Data Analysis Data Analysis 52

IBM Journey to AI blog

AUGUST 11, 2023

Because the machine learning lifecycle has many complex components that reach across multiple teams, it requires close-knit collaboration to ensure that hand-offs occur efficiently, from data preparation and model training to model deployment and monitoring.

Data Science 68

Data Science Machine Learning Machine Learning ML 68

ODSC - Open Data Science

AUGUST 9, 2023

For a comprehensive understanding of the practical applications, including a detailed code walkthrough from data preparation to model deployment, please join us at the ODSC APAC conference 2023. He is passionate about large scale distributed systems and is a vivid fan of Python. if the recipe is a dessert, 0.0

Machine Learning 52

Machine Learning Machine Learning AWS Big Data 52

Mlearning.ai

APRIL 6, 2023

Airflow allows you to configure, schedule, and monitor data pipelines programmatically in Python to define all the stages of the lifecycle of typical workflow management. We use DAG (Directed Acyclic Graph) in Airflow, DAGs describe how to run a workflow by defining the pipeline in Python, that is configuration as code.

Data Pipeline 52

Data Pipeline ML ML AWS 52

Pickl AI

MAY 30, 2024

Additionally, you will work closely with cross-functional teams, translating complex data insights into actionable recommendations that can significantly impact business strategies and drive overall success. Also Read: Explore data effortlessly with Python Libraries for (Partial) EDA: Unleashing the Power of Data Exploration.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

ODSC - Open Data Science

JANUARY 29, 2024

Data, Engineering, and Programming Skills Programming Despite the rise of no-code platforms and AI code assistance, programming skills are still essential for training and fine-tuning LLM models, scripting for data processing, and integrating models into applications. Python Python’s prominence is expected.

Data Science 52

Data Science Machine Learning Machine Learning Natural Language Processing 52

phData

DECEMBER 4, 2023

Knowing this, you want to have data prepared in a way to optimize your load. Snowflake Connectors For accessing data, you’ll find a slew of Snowflake connectors on the Snowflake website. For example: ODBC JDBC Python Snowflake Connector And, generally, things will be okay. Be sure to test your scenarios, though.

Clustering 98

Clustering Database SQL Data Pipeline 98

Kaggle

JULY 29, 2020

In August 2019, Data Works was acquired and Dave worked to ensure a successful transition. David: My technical background is in ETL, data extraction, data engineering and data analytics. All of the notebooks are in Python. Do you have any advice for those just getting started in data science?

ETL 71

ETL Data Scientist Data Science Machine Learning 71

Dataconomy

MARCH 19, 2025

Key disciplines involved in data science Understanding the core disciplines within data science provides a comprehensive perspective on the field’s multifaceted nature. Overview of core disciplines Data science encompasses several key disciplines including data engineering, data preparation, and predictive analytics.

Data Science 91

Data Science Citizen Data Scientist Data Scientist Machine Learning 91

AWS Machine Learning Blog

NOVEMBER 13, 2024

With the integration of SageMaker and Amazon DataZone, it enables collaboration between ML builders and data engineers for building ML use cases. ML builders can request access to data published by data engineers. Additionally, this solution uses Amazon DataZone. It’s mapped to the custom_details field.

ML 96

ML ML AWS Data Preparation 96

AWS Machine Learning Blog

DECEMBER 5, 2024

Solution overview To implement our RAG workflow on SageMaker JumpStart, we use a popular open source Python library known as LangChain. SageMaker JumpStart simplifies this process because the model artifacts, data, and container specifications are all pre-packaged for optimal inference.

AWS 97

AWS ML ML Machine Learning 97