Remove Data Scientist Remove DevOps Remove ETL
article thumbnail

How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Steep learning curve for data scientists: Many of Rockets data scientists did not have experience with Spark, which had a more nuanced programming model compared to other popular ML solutions like scikit-learn. This created a challenge for data scientists to become productive.

article thumbnail

Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Software Engineering Patterns for Machine Learning

The MLOps Blog

Data Scientists and ML Engineers typically write lots and lots of code. From writing code for doing exploratory analysis, experimentation code for modeling, ETLs for creating training datasets, Airflow (or similar) code to generate DAGs, REST APIs, streaming jobs, monitoring jobs, etc.

article thumbnail

Top AI/Machine Learning/Data Science Courses from Udacity

Marktechpost

They learn the complete data analysis process, including data wrangling, exploration, visualization using Matplotlib and Seaborn, and effective communication of findings. Real-world projects provide hands-on experience in investigating datasets and performing advanced data-wrangling tasks.

article thumbnail

FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning Blog

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

article thumbnail

Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning Blog

About the authors Samantha Stuart is a Data Scientist with AWS Professional Services, and has delivered for customers across generative AI, MLOps, and ETL engagements. He has touched on most aspects of these projects, from infrastructure and DevOps to software development and AI/ML.

article thumbnail

Top Predictive Analytics Tools/Platforms (2023)

Marktechpost

The company’s H20 Driverless AI streamlines AI development and predictive analytics for professionals and citizen data scientists through open source and customized recipes. The platform makes collaborative data science better for corporate users and simplifies predictive analytics for professional data scientists.