💻 Platforms Race to Lift the Data Infrastructure Burden

The challenges that come with lack of infrastructure have been a common refrain amongst the data science leaders we’ve interviewed. 

The largest and most advanced players are wielding custom-built machine learning platforms that allow them to seamlessly translate innovative ideas into production-ready models.

However, for the majority of businesses in the field, this transition remains a daunting challenge, leaving them grappling with the intricate process of turning concepts into actionable, scalable solutions. 

🌐 From the web

Generative AI Can Change the World – But Only if Data Infrastructure Keeps Up - “Despite the buzz surrounding Generative AI, most industry experts have yet to address a significant question: Is there an infrastructural platform that can support this technology long-term” 

Enterprises need a centralized data infrastructure to seamlessly access relevant data for Large Language Models (LLMs) without dedicated pipelines. According to Capgemini Research Institute, 74% of executives believe in generative AI's benefits, but its true potential hinges on robust data architectures. 

Data is choking AI. Here’s how to break free. - 

To ensure success in AI endeavors, enterprise leaders should adopt two key strategies. First, they should prioritize considering the availability of data throughout the entire AI development process rather than treating it as a problem to be resolved later, which often leads to costly and slow fixes. Second, they should focus on AI infrastructure that seamlessly integrates data and models with existing IT systems, ensuring that AI technology is effectively embedded into the broader technology architecture. 

Why data infrastructure remains hot into 2023 even as the economy cools - “The prize for a startup that becomes an integral part of the data stack is massive.”  

As companies rapidly pivoted to meet evolving needs during the pandemic, they discovered that their existing data stacks couldn't keep up. This realization has given rise to the need for a modern data stack. The data infrastructure sector has attracted significant venture capital, promising substantial returns for early investors.

When Infrastructure is lacking, data science teams find themselves mired in operational tasks. 

According to one poll, approximately 80% of a data scientist's work hours are dedicated to the essential task of data preparation and management, and a significant 76% of them find this aspect of their job the least enjoyable. 

Another survey touches on time spent on problem identification. 84.3% of data scientists and machine learning engineers express concerns about the time it takes to identify and address model-related problems within their teams. Plus, more than a quarter (26.2%) of respondents admit that they spend a week or longer on detecting and resolving these issues. 

In the period between when these polls were published and now, a number of startups have taken on the challenge of making these aspects of the work less time consuming for data science teams. To name just a few examples: 

💻 Platform Highlight

🤖 Atoma (currently in beta): A tool that automates the work that data teams do related to ad hoc analytics, data reporting, and alerting. 

🧠 Galileo: An AI model development platform. They raised $18 million in a Series A round in November last year. 

🗄 MotherDuck: SQL analytics platform offering cloud database storage, hybrid query execution and database sharing. They recently raised $52.5 million in new funding. 

🛠 Qwak: An MLOps platform allowing users to transform and store data, build, train, and deploy models, and monitor their ML pipeline. They raised $12 million in March. 

The consideration of platforms like Atoma, Galileo, etc. should typically come at different stages of the data science implementation process, depending on the specific needs and goals of the organization. Here's a general guideline for when to consider these platforms: 

1. Automation and Efficiency: Consider platforms that automate routine data-related tasks, streamline ad hoc analytics, and improve data reporting and alerting early in the process to enhance efficiency. 

2. Model Development and Deployment: Platforms focused on AI model development and deployment become relevant in the mid to later stages of the data science process when you are actively building and deploying machine learning models. 

3. Advanced Analytics and Data Management: Platforms designed for advanced analytics, robust data storage, query execution, and data sharing should be considered as your data science projects progress and involve larger volumes of data. 

4. MLOps and Workflow Management: Platforms specializing in MLOps, data transformation, model building, training, deployment, and pipeline monitoring are best suited for the advanced stages of data science implementation when you need efficient management of complex machine learning workflows. 

Tailor the introduction of platforms such as those above to align with your project timelines and goals. 

🤝 Partnerships in Focus

We helped Satya Systems overcome talent gaps and budget constraints, resulting in 62% cost savings and enhanced AI capabilities.

See you next week,

Mukundan

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