AI & Data Science Tech Blog
Excel Alternative: Want More Capable Data Analytics Software Used By GE, For Free?
Sure, Microsoft Excel is perfect for entering data in rows and columns. But what if you want to quickly find practical meaning in your data that allows you to take action and make better data-driven decisions? For example, what if you wanted to learn how many steps you should take to sleep better tonight based on your smartwatch data history? Here’s what you need to know.
Feature Store: Uncover Uber's Secret to Large Scale Machine Learning
Most data science projects never make it to production. Fortunately, you can now easily deploy machine learning models like Uber. Here's what you need to know.
Machine Learning Operations: 3 Challenges Evaluating Machine Learning Models
How model drift impacted the KPIs of two businesses. Challenges, solutions, and outcomes.
Data Science Technology New Year’s Resolutions for 2021
In 2021 I aim to be better at evaluating software and the companies that support it. Here are my New Year’s resolutions for 2021 (Data Science Technology Edition)
Data Science, Machine Learning, and AI Analytics Platforms to Secure Funding in 2020
In spite of a tumultuous year caused by the COVID-19 pandemic, these advanced analytics technology vendors closed funding rounds in 2020. Congratulations!
2021 Predictions for Data Science Teams and Technologies
Want to know what 2021 has in store for data science technologies? Will MLOps dominate the conversation? How will the Covid recession change data science inside companies? Spend 5 minutes and checkout Datagrom’s predictions for 2021.
Business Intelligence vs Data Science? Here’s Why BI Needs AI
New platforms combine BI and AI to help you move beyond “What happened?” to answer “Why did things change?”
Docker for Data Scientists Made Simple: Why Pay for Data Science Software?
Here's how you can get started in one-click for free.
Concern: 5 Tips to Curb Vendor Lock-In: Data Science and Analytics Technologies
Tips and tricks to avoid the painful lessons of vendor lock-in in data science and analytics technologies.
Snowflake vs Databricks: Where Should You Put Your Data?
Software ate the world, turned it into data, and now it’s suffering from indigestion. Fortunately, cloud offerings from Databricks & Snowflake are here to help. Here's what you need to know.
Datagrom Magic Surfboards For Cloud Data Science & Machine Learning Platforms
An alternative review to Gartner and Forrester of the cloud machine learning platforms: Amazon AWS SageMaker, Microsoft Azure Machine Learning, and Google Cloud AI Platform.
Released in 2020: Meet AWS SageMaker Studio, Azure Machine Learning Studio, & GCP AI Platform
Review of machine learning platforms released in 2020: Amazon AWS SageMaker Studio, Microsoft Azure Machine Learning Studio, and Google Cloud AI Platform.
Databricks IPO in 2021? The Next Data Science & Machine Learning Platform to go Public
Map of data science nerve centers across the US in 2020. Interpretation, and projections post COVID-19.
My Take on the Gartner MQ for Data Science and Machine Learning Platforms: “Why I buy” Data Science and Machine Learning Platform Edition
Update: Gartner has postponed its Data and Analytics Conference. Hopefully, this article helps to inform in that conference’s absence. If you have any questions about my comments, please comment below, or send me a note.
A few weeks ago, Gartner released its 2020 Machine Learning and Data Science platform Magic Quadrant. Each year Gartner uses their “Completeness of Vision /Ability to Execute” methodology to categorize and rank each vendor who participates in the annual Gartner dance. The Gartner report claims to provide clarity on a complicated market. I believe it creates total confusion. (Yes, this I know that’s their model)
As a veteran of the machine learning and data science technology market, I have some of my own thoughts on these products, their value proposition, their targeted personas and their actual personas reached. I have paid close attention to the MQ for the last 5 years and have worked closely with and for many of the companies/technologies in the quadrant. I wanted to share some of my thoughts in a series of posts. This is the first.