AI Readiness Checklist for Your Tech Stack

To maximize the ROI of any AI project, businesses need to establish a firm technological foundation.

According to Ernst & Young, 97% of leaders who are already investing in AI say they’re seeing positive returns, but are limited by the organization’s infrastructure. 83% said AI adoption would be faster with a stronger data infrastructure in place, while 67% said a lack of infrastructure is actively hindering AI adoption.

Assessing and strengthening your data and technical infrastructure gives your organization a stronger foundation for AI, leading to greater efficiencies, higher productivity, a faster ROI, and better overall results.

Data Foundations

The quality of your AI’s output starts with the quality of your data. This can include traditional databases, logs, images, and even unorganized data. Your tech stack should be able to handle whatever format your use case demands, without forcing you to clean and convert everything manually.

Data must be available for AI consumption and not locked in departmental silos. Some applications need a live data stream, while others work better with scheduled jobs. Tools like Kafka, Flink, or Airflow can help you handle both.

Dev Environment

Before any AI model can go live, it has to be built, trained, and tested. Depending on your use-case you might use an off-the-shelf model, or build one from scratch.

If you choose to go the build-your-own route, your developers will most likely use tools like PyTorch and TensorFlow to create and train deep learning models, while frameworks like scikit-learn might be employed for classical machine learning techniques.

These frameworks should be part of your environment from Day One. Most cloud providers like AWS, GCP, Azure, etc., provide pre-built compute instances and platforms that can be activated for this purpose. Examples include AWS SageMaker, AWS Bedrock, GCP Vertex AI Workbench, and Azure Machine Learning.

You’ll also need the right hardware behind the scenes. CPUs can handle basic workloads, but for serious training, access to GPUs or TPUs is a must. These make it possible to train larger models faster and run experiments without long delays. Most cloud platforms provide specialized hardware with the requisite packages pre-installed. See above for examples.

As your team builds and tests models, tools like MLflow or Weights & Biases help log each experiment and manage the machine learning lifecycle.

Regardless of the tool you’re using, tests should be written so that anyone on the team can run them and get the same results.

Training Setup

Once your development environment is in place, the next step is to ensure that your training setup can handle your workload, both now and as your business grows.

A single machine won’t be enough if you’re working with large datasets or training heavy models. Tools like Horovod or DeepSpeed let you distribute training across multiple GPUs or machines so training can finish faster.

You’ll also want to keep track of which version of your dataset was used for each model, which is called dataset versioning. It helps you trace back how a model was trained and makes it easier to debug or retrain later if needed.

Large models and training files can take up a lot of space, so your storage needs to be scalable. Cloud-based options like S3 or GCS can grow with your needs.

Model Serving

After your model is trained, it needs to be deployed so real users or systems can start using it. This process is called model serving.

Start by packaging your model using a tool like Docker or ONNX. It makes it easier to run the model anywhere on your local machine, in the cloud, or inside a larger application without running into setup issues.

Next, you’ll need a way for other systems to access the model, usually through an API. Tools like FastAPI, AWS API Gateway, Triton, or similar help you set up fast and reliable endpoints.

Connecting your model deployment to a CI/CD pipeline allows you to automatically update and deploy models as new versions are created, just like you would with regular software.

As more people or systems use your model, load balancing and autoscaling ensure the system can handle the increased traffic without slowing down or crashing.

MLOps Tools

MLOps tools work behind the scenes to keep your AI running smoothly. They automate processes, manage model and data versioning, and monitor for drift. Cloud platforms provide managed deployments of MLFlow, Valohai, etc. that are built for this exact purpose.

Automated Pipelines

Repeating the same steps to manually clean data and train and test an AI model takes time and introduces errors. Tools like Kubeflow, Prefect, or ZenML let you automate this flow to streamline the workflow, accelerate training, and eliminate human error.

Model + Data Versioning

Keep records of which model was trained on which dataset and using what configuration. Without this, it’s hard to know what changed or how to go back if something breaks.

Drift Monitoring

Models trained on last year’s data can behave differently once input changes. Monitoring for drift helps you spot those shifts early, so you don’t end up with a decline in model performance or accuracy.

Integration with DevOps Tools

Machine learning connects your models to Git for code control, CI pipelines for testing, and issue tracking to manage updates. The closer it fits with your existing tools, the easier it is to maintain.

Security & Privacy

Security covers how your system handles data and restricts access.

At a minimum, your security measures should include:

• TLS encryption to protect data moving between systems
• Identity and Access Management (IAM) to define who can access what, such as datasets, training pipelines, or model endpoints
• Role-based access controls to ensure access to sensitive tasks is limited to authorized users

Privacy measures are also critical, especially if your AI system handles personal data, especially anything covered by regulations like HIPAA. Privacy measures should include:

• Data anonymization or masking to remove identifiable information before training
• Privacy layers that tokenize or restrict access to sensitive fields depending on the user or environment

Likewise, your pipelines should support audit logging to record who accessed data, when changes were made, and how models behaved. These logs help with both debugging and compliance.

Fairness and transparency should be built into the system itself. Use tools like SHAP or LIME to explain how models reach their decisions. Add fairness checks during model training to flag potential bias before it reaches production.

GenAI Readiness

Generative AI needs a different setup than traditional machine learning. These models create content and answer questions in real time. To support that, your stack needs a few key upgrades.

First, you’ll want to connect a vector database like Pinecone, Weaviate, Chroma, etc. to help the model find and use the most relevant information instead of relying on training data. It’s useful for things like chatbots, document search, or anything that needs accurate responses.

Then there’s the framework layer. Tools like LangChain or LlamaIndex make it easier to manage how the model interacts with users or systems. They help you control the flow of prompts, context, and logic between steps.

Generative models use a lot of memory, which can slow things down. To fix that, you can use quantization or other optimizations that shrink the model without losing quality. You can also run them on efficient backends like DeepSpeed or vLLM.

If you’re building something that pulls in live information, like a retrieval-augmented generation (RAG) system, you’ll need fast search, streaming output, and an architecture that can handle live updates.

Is Your Tech Stack Ready for AI?

When your tech stack covers everything from data flow to deployment, MLOps, and GenAI support, your organization should be AI-resilient and prepared for growth.

But technical infrastructure is only one of the four pillars of AI readiness. How does your business measure up in terms of data, strategy, and culture?

Before you commit to building an AI solution for your business, measure your readiness across all four pillars with Taazaa’s AI Readiness Assessment. In less than five minutes, our free, online tool will give you a benchmark readiness score, identify challenge areas, and deliver a complimentary report.

Take the AI Readiness Assessment now.