Broken Lifecycle

Introducing Rasgo’s accelerated modeling preparation platform

Jared Parker
Introducing Rasgo’s accelerated modeling preparation platform

Over the past few years, we have seen a massive evolution in data science. What started as laptop-based python and R development has evolved into major cloud companies and new venture-backed businesses building powerful platforms to enable data science to be delivered at scale. Teenage kids are now teaching themselves python and universities are creating new majors and graduate programs to develop world class data science talent. As a result, experts predict that by 2022, data science will become the number one emerging profession in the world, creating roughly 11.5 million jobs by 2026. The world’s largest enterprises are making material changes to their recruitment and hiring strategies to attract the best of the breed.

With $52B in venture capital going into the data science market every quarter, you would expect projects have become more efficient. You would expect friction has been removed from the data science lifecycle. You would expect that new technologies have solved the mundane problems and enabled Data Scientists to work as efficiently as possible. You would expect… but what we heard from Data Scientists across the market paints a very different picture.

My co-founder and I have spent our entire careers in the data science space. In that time, we have worked with over 100 organizations and supported thousands of Data Scientists. We have heard the same story every single time:

  1. Executives don’t understand how difficult and complex it is to deliver ML at scale, get starry eyed that “AI will transform their business overnight” and invest heavily in data science and ML. 
  2. Organizations then hire Data Scientists and Data Engineers and set unrealistic  model delivery KPIs and “magic button” business value goals.
  3. After failing to deliver with laptop-based data science, companies then invest in foundational technologies, such as a cloud data warehouse (Snowflake, Redshift, BigQuery) and a data science platform (Sagemaker, Domino, Dataiku, etc.) to support model training and deployment.

At every company and team that we worked with we witnessed massive misalignment between unrealistic business expectations and what Data Scientists could realistically deliver.

We heard thousands of Data Scientists scream in frustration:

“I went to school to be an expert in developing models and solving interesting problems. Instead, I’m spending the vast majority of my time extracting, exploring, cleansing, joining, and transforming raw data into a model training ready dataframe.” 

Today, if we look across silicon valley, the world’s largest tech companies have realized that this is an urgent problem to solve. They have developed internal technologies to meet the demands of their users and drive competitive advantage with ML. However, for organizations outside of Big Tech, building internal solutions is not scalable and commercially available solutions that truly solve the problem do not yet exist.

This is why we created Rasgo.

Today, we are excited to introduce Rasgo’s Accelerated Modeling Preparation (AMP) platform that accelerates the critical, yet tedious work Data Scientists must complete to get raw data ready for model training. The Rasgo AMP platform, by mitigating the friction associated with data exploration, data prep, and feature engineering, enables Data Scientists to spend their time where they should: training and evaluating models for maximum impact. Stop spending your time on tedious data plumbing and get back to the joy of data science!

Request a demo for a sneak peak into the technology we are bringing to market and connect with us to provide your feedback on how we can improve our solution. Rasgo was built by Data Scientists, for Data Scientists to level-up the ML community as a whole!

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