Anyscale, the company behind Ray open source, the unified compute framework for scaling any machine learning or Python workload, announced several new advancements on the Anyscale Platform™ at AWS re:Invent in Las Vegas, NV. The new capabilities extend beyond the advantages of Ray open source to make AI/ML and Python workload development, experimentation, and scaling even easier for developers.
The Anyscale Platform™, built on Ray, Introduces New Breakthroughs in AI Development, Experimentation and AI Scaling
November 29, 2022 by Leave a Comment
Anaconda Announces Collaboration with Esri, Setting the Enterprise Standard for Python Across the Geospatial Community
April 12, 2022 by Leave a Comment
Anaconda Inc., provider of the popular data science platform, announced a collaboration with Esri, the global market leader in geographic information system (GIS) software, location intelligence, and mapping. This collaboration supports Esri and the geospatial community by providing users of Esri’s software with preloaded geospatial packages for use with Python.
Python Madness
April 4, 2022 by Leave a Comment
The Python MVP March Madness 2022 tournament also has arrived at its championship game, with echoes of the real NCAA. Two weeks ago, the tournament began with 32 Python packages matched up in a head-to-head, lose-or-go-home tournament play. The community voted, round-by-round, and delivered NumPy and pandas to the final game.
Video Highlights: A Path Into Data Science
July 18, 2021 by Leave a Comment
Are you interested in getting ahead in data science? On this TalkPython podcast episode, you’ll meet Sanyam Bhutani who studied computer science but found his education didn’t prepare him for getting a data science-focused job. That’s where he started his own path of self-education and advancement. Now he’s working at an AI startup and ranking high on Kaggle.
Video Highlights: Thinking Sparse and Dense
July 4, 2021 by Leave a Comment
The video below, “Thinking Sparse and Dense” is the presentation by Paco Nathan from live@Manning Developer Productivity Conference, June 15, 2021. In a Post-Moore’s Law world, how do data science and data engineering need to change? This talk presents design patterns for idiomatic programming in Python so that hardware can optimize machine learning workflows.
Circular Statistics in Python: An Intuitive Intro
February 12, 2021 by 2 Comments
In this contributed article, Amit Babayoff, a data scientist at Deeyook, discusses the principles of circular statistics, by looking at some its basic principles and tools and why conventional linear methods don’t work well on circular data. She also explores how a simple filtering for handling noise can be constructed from these basic tools.
Video Highlights: BigQuery + Notebooks: Building an Analytics Pipeline on Kaggle
September 5, 2020 by Leave a Comment
Your architecture choices impact how efficiently you’re able to use your data. In this “Snapshots” video produced by Kaggle, Data Scientist Wendy Kan demonstrates how she incorporates BigQuery and Kaggle Notebooks into her workflow. Watch her create an interactive network analysis graph that explores the most commonly installed Python packages!
The Impact of Python: How It Could Rule the AI World?
March 13, 2020 by 1 Comment
In this contributed article, writer, AI researcher, and business strategist Michael Lyman discusses the growth of use of the Python language and how it is playing a significant role in the rise of AI and deep learning. Python’s power and ease of use has catapulted it to become one of the core languages to provide machine learning solutions.
Interview: Terry Deem and David Liu at Intel
September 3, 2019 by Leave a Comment
I recently caught up with Terry Deem, Product Marketing Manager for Data Science, Machine Learning and Intel® Distribution for Python, and David Liu, Software Technical Consultant Engineer for the Intel® Distribution for Python*, both from Intel, to discuss the Intel® Distribution for Python (IDP): targeted classes of developers, use with commonly used Python packages for data science, benchmark comparisons, the solution’s use in scientific computing, and a look to the future with respect to IPD.
Supercharge Data Science Applications with the Intel® Distribution for Python
July 29, 2019 by Leave a Comment
Intel® Distribution for Python is a distribution of commonly used packages for computation and data intensive domains, such as scientific and engineering computing, big data, and data science. With Intel® Distribution for Python you can supercharge Python applications and speed up core computational packages with this performance-oriented distribution. Professionals who can gain advantage with this product include: machine learning developers, data scientists, numerical and scientific computing developers, and HPC developers.
