Data Science
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Splunk’s new AI tools aim to ease security, observability tasks
The AI tools introduced at the company’s .conf2023 include the Splunk AI Assistant, Splunk Machine Learning Toolkit 5.4, Splunk App for Anomaly Detection, and the Splunk App for Data Science and Deep Learning 5.1.
The engines of AI: Machine learning algorithms explained
Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.
Coding with AI: Tips and best practices from developers
AI pair programming could be a coder's dream or a nightmare in the making. Nine developers talk shop about how they're using generative AI today.
ChatGPT’s parasitic machine
What do ChatGPT and other large language models owe to the human creators who provide the information they train on? What if creators stop making their insights publicly available?
5 best practices for software development partnerships
Partnerships can accelerate technological innovation in agile, devops, and data science. Just make sure you start with a strong foundation in place.
5 newer data science tools you should be using with Python
Already using NumPy, Pandas, and scikit-learn? Here are five more powerful Python data science tools ready for a place in your toolkit.
14 popular AI algorithms and their uses
Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases.
Preview: Google Cloud Dataplex wows
Google Cloud Dataplex is an amazingly complete system for turning raw data from silos into unified data products ready for analysis. And a bit overwhelming to learn.
6 ways to avoid and reduce data debt
Data debt can be just as bad as tech debt, causing security and trust problems if it isn’t addressed throughout the data pipeline.
How to babysit your AI
AI systems are not yet mature and capable enough to operate independently, but they can still work wonders with human help. We just need a few guardrails.
How to explain the machine learning life cycle to business execs
For data science teams to succeed, business leaders need to understand the importance of MLops, modelops, and the machine learning life cycle. Try these analogies and examples to cut through the jargon.
Data Workshops for Ukraine: Learn a skill and support a cause
The two-hour workshops offer training in data visualization and analysis with R, Python, and SQL and cost just $20 or €20. Next up is ChatGPT in R.
Embrace and extend Excel for AI data prep
Combining machine learning and Excel can get you the data transformation you need while data scientists are scarce.
nbdev v2 review: Git-friendly Jupyter Notebooks
Add-on to Jupyter Notebooks enables a literate Python development style that gives you high-quality documentation, tests, continuous integration, and packaging for free.
The tip of the data science iceberg
Data science is already a vital element of a successful business. Before long it will be part of every application, and AI will be embedded in every transaction workflow.
Why Python is catching on with business analysts
Business analysts are running into the limits of BI tools and looking for ways to do more advanced analytics. Python is the way forward.
Cloud computing gets back to basics
Recent trends show a return to cloud fundamentals, such as data, development, deployment, and security, rather than chasing what’s new and cool.
