The tech stack you choose can make or break your AI startup. With a vast ecosystem of tools and technologies available, selecting the right combination can feel overwhelming, especially when the choices you make will impact everything from scalability and cost to team efficiency. In this post, we’ll dive into key considerations for building a tech stack tailored to data and AI startups, including the significance of Python in the data world, best backend/frontend combinations, and advanced tools for startups looking to host their own models or ETL pipelines.
A snake in the world of data
Python has become the unshakable foundation of the data science and AI world, and for good reason. Known for its readability, large community, and abundance of libraries (such as Pandas, Scikit-Learn, TensorFlow, and PyTorch), Python makes developing and deploying AI models significantly more manageable. Python’s extensive support for scientific computing has made it the go-to choice for building models, processing data, and implementing machine learning workflows.
For startups, Python’s appeal isn’t just its libraries; it’s also the availability of talent. With Python dominating academic and industry training, it’s easier to find skilled data scientists and engineers who can jump into a project with minimal ramp-up time. When building a tech stack for AI, adopting Python for data processing and machine learning is almost a given.
However, while Python shines for data-centric tasks, it's not always the best choice across the entire stack. This brings us to selecting backend and frontend stacks that complement Python’s strengths.
Brenden Cambier
Combinations of back-end/front-end stacks
Generally, the choice of tech stack should be guided by the existing skills within the company and the availability of talent in the market. Given that most future data hires will likely be proficient in Python (but not in front-end engineering), the goal should be to limit the number of programming technologies, avoiding the need for a large array of specialised experts.
Since Python is not typically used for front-end development, here are some strong combinations that keep programming languages to a minimum:
- Python back-end + JavaScript front-end: This is an excellent choice for startups building from scratch, especially if you have an initial developer who is familiar with one of these two core technologies. The Python back-end can handle data processing and logic, while a JavaScript framework (e.g., React, Vue) powers the front-end. For early-stage development, even a no-code or low-code front-end can work well, allowing the team to validate the product quickly before committing to full development.
- Unified Stack (Java, JavaScript, PHP, or .NET) for both back-end and front-end + Python for machine learning: This strategy can be effective when the founding developers have limited Python experience or when Java is a requirement for big data (Hadoop/Spark). Minimising the tech stack to 2 programming languages keeps the solution simpler and minimises learning curves. Python can then be introduced solely for machine learning tasks, allowing the team to leverage its strengths in data handling without needing Python for the core application.
This approach ensures a flexible yet streamlined tech stack, with minimal complexity and easy future scalability. By aligning the stack with the team’s strengths and anticipated hiring needs, startups can keep development efficient and focused as they grow.
Advanced tooling
For AI startups that need to host their own models or implement ETL (Extract, Transform, Load) pipelines, there are several tools and platforms to consider.
Hosting your own models
If you plan on running your own models rather than relying solely on third-party APIs, consider the following tools:
- Docker and Kubernetes
Containerisation and orchestration are essential for deploying machine learning models at scale. Docker packages models with all dependencies for consistent environments, while Kubernetes enables you to scale deployments, manage load balancing, and ensure resilience. - MLflow or DVC
Model tracking and versioning are crucial as your models evolve. MLflow offers an all-in-one solution for model lifecycle management, while DVC (Data Version Control) integrates well with Git for versioning both models and data. - TensorFlow Serving or TorchServe
For TensorFlow and PyTorch models, these serving solutions streamline deployment, providing APIs to interact with models and manage multiple versions.
ETL Pipelines
ETL pipelines are crucial for most data workflows, moving and transforming data from source to destination. Here’s a look at popular tools that help with this process.
- Apache Airflow or AWS Step Functions
These tools are a popular choice for managing complex workflows and scheduling tasks. It’s ideal for orchestrating ETL pipelines, allowing you to define, schedule, and monitor tasks, making sure data flows smoothly from source to destination. - AWS Glue
AWS Glue is a fully managed ETL service that simplifies data preparation and transformation. It integrates seamlessly with other AWS services and offers tools for data cataloging, transformation, and job scheduling, making it a powerful choice for startups working within the AWS ecosystem. - Spark and Databricks
For heavy-duty ETL operations, Spark provides a distributed computing framework that can handle massive datasets. Databricks builds on top of Spark, providing a collaborative workspace that simplifies ETL and data processing tasks, while integrating seamlessly with machine learning workflows.
Emma Williams
Conclusion
Selecting the right tech stack is pivotal for setting your AI startup on a path to success. A streamlined approach, centered on key technologies aligned with your team’s strengths, enables faster development, easier scalability, and more efficient hiring. By focusing on flexibility and simplicity, your startup will be well-equipped to adapt, innovate, and seize market opportunities, establishing a strong foundation for long-term success.
Member discussion