Over the last few weeks, I’ve been talking to multiple large financial services and insurance customers — the kind of enterprises that have data centers bigger than most cities, security teams larger than some armies, and compliance requirements that make the IRS look easygoing.

These conversations started similarly: "We want to fine-tune or train an open-source foundation model on our proprietary data. For example, improve the risk assessment of securitized mortgage assets using a large language model to analyze the underlying loan applications."

And they all ended the same way: "But… it’s a lot more complicated than we expected."

Because here’s the thing nobody tells you when you’re reading headlines about AI innovation: The hard part isn’t the model. It’s everything around the model.

The infrastructure, security, governance, compliance, plumbing, and politics are the real obstacles.

Let me show you what I mean.

🟢 1. Infrastructure: You’re Managing a Multi-Cloud Circus

Enterprises don’t live in a clean, simple world. They live in:

• AWS. Azure. GCP. On-Prem. Private Cloud.
• Often, all at once, because that’s how procurement decisions were made in 2018.

So when your data science team wants to run a model, the first question isn’t Can we? It’s:

Where? On which GPUs? In which region? Will that trigger a compliance escalation because we crossed a regulatory boundary? What will that cost us? (Spoiler: too much.)

You’re not just fine-tuning a model. You’re managing a supply chain.

🔒 2. The Python Dependency Tree is a Security Dumpster Fire

Every ML pipeline is built on a teetering tower of Python packages:

• Libraries that haven’t been maintained since the Obama administration.
• Dependencies that conflict with each other like it’s a cage match.
• Security vulnerabilities that no one noticed until your CISO did.

Every time you run pip install, a security engineer’s blood pressure spikes somewhere deep in your IT department.

⚖️ 3. The Legal Minefield is Real and Spectacular

You can’t just grab an open-source model and sprinkle your proprietary data on top.

• That dataset? Might contain sensitive or copyrighted information.
• That model license? Might have a non-commercial clause buried in footnote 37.
• Those Python dependencies? Probably violating half your enterprise’s open-source software policy.

Congratulations — you just gave your Legal and Compliance teams a brand-new headache. Worse, many open-source models don't come with a standard licensing agreement. It is easy to violate the terms.

🌍 4. Data Location: Where the Data Lives, the Lawyers Follow

Where your data lives isn’t a side note — it’s a full-blown cost, compliance, and operational headache.

• Move data to GPUs? Burn money on cloud egress fees.
• Move GPUs to the data? Good luck finding capacity.
• Accidentally move European citizen data out of Europe? Call your privacy officer — and your lawyer.

Infrastructure and compliance teams now have to coordinate like they’re launching a moon mission.

📊 5. Benchmarking: “Does It Work?” is a Loaded Question

Once you fine-tune the model, you need to know:

• Is it accurate?
• Does it generalize?
• Is it fair, secure, and compliant?
• Will it keep performing six months from now?
• Is it ethical?

This isn’t Kaggle. It’s your customer data, your risk profile, your board presentation on the line. This is especially complex when the data scientist is trying to merge two datasets together (e.g., credit data with loan applications).

🐍 6. Python, Conda, Docker, Kubernetes… and Why You Now Have an ML Platform Team

Fine-tuning models means:

• Fighting with Conda environments that break every other day.
• Wrestling pip dependencies into submission.
• Containerizing everything in Docker.
• Running it all on Kubernetes — which is Latin for you will need three more platform engineers.

And suddenly, you’ve got DevOps, Security, Compliance, Infrastructure, and Legal all in the room — and your data scientists can’t even start their actual work yet.

🤯 7. And That’s Just the Beginning

You’ll also need to:

• Monitor GPU spend like a hawk.
• Handle batch inference pipelines at scale.
• Ensure reproducibility, audit trails, and disaster recovery.
• Constantly monitor for model drift, compliance violations, and performance degradation.

By the time you’ve built this scaffolding, it’s no longer an AI project — it’s an enterprise IT initiative with a very expensive hobby.

👋 This is Why We Built Project Robbie

At Project Robbie, we’ve spent years listening to these enterprise horror stories. So, we built something better.

Robbie automates the operational, legal, and infrastructure complexity that’s holding your data scientists hostage:

✅ Automatically selects the right infrastructure across clouds and on-prem.

✅ Colocates compute and data for performance, cost, and compliance.

✅ Wrangles environments, containers, and Kubernetes so you don’t have to.

✅ Validates models and enforces security & licensing policies out of the box.

✅ Keeps your lawyers, compliance officers, and platform engineers happy.

✅ And most importantly, let your data scientists focus on the science.

The future of AI in the enterprise isn’t about bigger models — it’s about removing the bureaucracy and chaos around them. That’s what we’re building at Robbie.

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