If you’re here, you’ve most likely hit a point where you know there’s more you can do with your data. Going back to school to learn ML isn’t an option and hiring the most talented data scientist team isn’t either. So the first step to digging into Artificial Intelligence is to set up your infrastructure and get your team ready to actually use it. In this article, we will be guiding you through several tech stacks we personally recommend to our clients so they can establish an adaptable infrastructure that allows working with data within and across all teams.


The first thing you should consider is the type of organization you’re working with. Ask yourself these questions:


  1. Do you have Data Scientists or Data Analysts in your teams?
  2. Do you have a BI team?
  3. Is your analytics team embedded across your business units?
  4. Does your team have people who know SQL?


After answering these questions you can jump to infrastructure planning.


Just like humans, AI takes vast amounts of data from many places such as servers, the software we use every day, to the smartphones we carry everywhere. So breaking down what needs to happen once this information is consumed is very important. Let’s start with data collection. These could be tools that connect to your data warehouse, databases, Stripe, or even Shopify. Next, we’ll need somewhere to store it. Followed by processing– you must dedup and clean your data in order to work with it. Then you need to analyze your data with easy visualization charts. And lastly, you must send the output to your business applications and maybe report to stakeholders. So now that a framework has taken shape, you can decide which tools are the best ones for your business.

ML Tech Stack Infographic


ML Tech stacks for companies with teams of Data Scientists and Analysts


Teams with a number of data scientists and dev-ops need a stack in parallel in order to ship faster and test models. Let’s say you’re using Azure’s data warehouse, and Azure pipelines. You might want to use Datagran in conjunction with your tech stack to model fast, ship fast, and make core what works. Datagran provides a level of speed that in tandem improves the relationship between your business units and the output. 

Microsoft Azure 

Azure is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers. 

It’s an excellent choice for cloud computing, artificial intelligence (AI), and blockchain platforms parallel to your web hosting. Now, the downside is that because of its powerful cloud computing capabilities, it is not suited for beginners. So if your team members include data scientists and analysts, this is a great option.

Datagran

Datagran connects to your most-used business applications, lets you run ML models, and automate workflows without coding. The platform can be compared to a Zapier for ML modeling, from centralization to sending the outputs fast and efficiently. Its UI is simple and easy to use and provides all the flexibility dev ops, data scientists, and analysts need.

Need a tech stack to run your business? Check out this guide.

ML Tech Stack for companies with data analysts with no SQL knowledge


This type of stack is considered No-Code since it must serve data analysts in your team who don’t have SQL knowledge. This stack a great solution to perform basic modeling, with the only downside being having to stick to templates, leaving little flexibility. Don’t be discouraged though, this tech stack is a great starting point.

Segment

Segment is a customer data platform (CDP) that helps you collect, clean, and control your customer data. Segment is a great option to add data sources without native integration, but it still requires technical support to set it up. 

DataStudio

DataStudio lets you visualize your data through highly configurable charts and tables. It connects to a variety of data sources and gives you the option to share your insights with your team. Additionally, it allows you to collaborate on reports with your team, speeding up your report creation process with built-in sample reports. Learn more about this platform with their DataStudio course.

Amplitude

Amplitude helps you better understand user behavior, ship improved experiences, and retain more customers. It’s a product intelligence platform that helps you use your customer data to build product experiences for digital growth.

ClearBrain

ClearBrain is a Causal Analytics platform, built on AI to automatically separate causation from correlation. It helps you understand what’s causing your best users to convert, identify patterns in user behaviors, and prioritize which experiments to run.

Intercom

Intercom is a live chat system for customer services, sales, and marketing. It has three main offerings: live chat to communicate with your customers, an onboarding system to convert users as their navigation your site, and an FAQ type of module to educate your users with common questions.

ML Tech Stacks for companies with data analysts with SQL knowledge

Let’s assume your team includes data analysts with SQL knowledge, in that case, a more robust Machine Learning tech stack is recommended. 

Segment

Segment provides the reliability and power to track events across web and mobile applications.

Fivetran

Fivetran helps you centralize data into your warehouse for advanced analytics. 

BigQuery

Data warehouse.

Datagran

Send the data to Datagran for flexible descriptive and predictive modeling. Then, you can send the ML model outputs to business applications without the need to code. 

Intercom

The outcome can be sent out to Intercom for instant contact with your current users in the form of email campaigns, onboarding tours, product tours, FAQs, and more.


Once your company scales modeling outputs can be sent out from Datagran to tools like Tableau to track revenue, churn, and more, or across tools like Looker, or PowerBI. Datagran gives the necessary tools your team needs to speed up the modeling and shipping process so your business can keep up with loads of data generated by users. Give it a try by building ML workflows fast, without coding here.