Selecting an Enterprise Platform for Python and Open Source: A Checklist for Buyers
Introduction Marc Andreessen famously opened an August 2011 blog article with this provocative sentence: “Software is eating the world.” His prediction was that software development would disrupt traditional industries. Indeed, companies like Airbnb, Netflix, and Uber emerged as just a few of many winners in the “on-demand” economy that disrupted industries like travel, entertainment, and […]
Leveraging AI for Enterprise Success: Insights for Leaders
Artificial intelligence (AI) stands at the forefront of technological evolution, offering unprecedented opportunities for enterprise organizations. AI is not just a technology or a tool—it is a pivotal cornerstone that will define the future of enterprises. Its applications transcend traditional boundaries, offering transformative solutions across various business functions. For example, over the past two years, […]
How to Implement an OSS Governance Program for Data Science, Artificial Intelligence, and Machine Learning
What’s Missing From Enterprise Data Science and Machine Learning? Security. Open-source software (OSS) is the backbone of data science and machine learning innovation. No single technology vendor can outmatch the open-source community. While OSS is generally safe, vulnerabilities creep in over time. Just like DevOps teams, data scientists need to develop processes that ensure they […]
Building Data Science Solutions with Anaconda
About this report Building Data Science Solutions with Anaconda is a comprehensive starter guide to building robust and complete models. The book covers everything you need to know about algorithm families and helps you build must-have skills such as building interpretable models and avoiding bias in data. By the end of the book, you’ll be able […]
Determine the Right DS Platform
Virtually every industry has embraced data science and machine learning as the next frontier for growth and innovation. Whether you have a full-fledged data science team building machine learning models or not, you’re probably wondering how to operationalize and scale your data science program. Our build versus buy whitepaper highlights the key considerations for evaluating […]
Enterprise Machine Learning
Creating Context for Data Scientist and Developer Collaboration n this report, we drill down into the developer and data scientist demographic from our 2019 State of Data Science survey of 5,000 users. From the data we find key differences and similarities between the two roles, including: Tool preferences Coding background Model delivery techniques In this […]
The Definitive Guide to AI Platforms for Open-Source Data Science and ML
Artificial intelligence (AI) is undeniably and rapidly transforming life as we know it. Leading tech companies are investing heavily, and Google CEO Sundar Pichai has compared the impact of AI to electricity and fire. Organizations that have adopted AI to improve products and services are seeing significant financial returns on their investments. We are just beginning […]
Tech Talk: CAIO Unplugged
Join us for an exclusive Tech Talk with Anaconda’s Chief AI and Innovation Officer and Co-founder, Peter Wang. In this interactive Q&A session, Peter shares expert insights on Python, AI, and the latest industry trends while answering key questions from participants. The discussion is moderated by Javvi Joyce Ferrer, Lifecycle Marketing Manager at Anaconda. Don’t […]
Data Science vs Data Analytics: What’s the Difference?
Introduction Organizations are constantly looking for ways to leverage their data to save time, reduce costs, and accelerate innovation. However, there are many different approaches to consider for generating valuable insights with data. By understanding which approach better suits specific use cases, you can maximize the impact of future data and AI initiatives. Data science […]
Comparing Data Science and AI: Where They Overlap and Differ
Introduction Two terms that often arise in discussions around leveraging data are data science and machine learning. While these concepts are closely related (and sometimes mistaken to be the same), it’s important to understand the differences between data science and machine learning and their distinct characteristics and applications. Understanding the ways in which data science and machine Machine learning is a […]
