Deep Learning

AIGard builds deep learning models for language, vision, audio and other complex data. We design the architecture, train efficiently on GPUs and optimize for deployment.

The work typically covers:

• Neural architecture design
• Training on GPU infrastructure
• Transfer learning and fine-tuning
• Model compression and optimization
• Deployment to cloud or edge
• Experiment tracking

Deep Learning is usually the right investment in one or more of these situations:

• Classical models can't reach the accuracy you need
• You work with images, audio, video or text
• A model needs to run faster or on smaller hardware
• You need to fine-tune an open-source model

On a 30-minute scoping call we confirm whether it fits your case and recommend a team shape and first milestones.

No, you do not need your own GPUs for a deep learning project. AIGard can train on cloud GPU infrastructure and size it to control cost.

We design the architecture and plan training to run efficiently on GPUs. Where suitable, we use transfer learning and fine-tuning in place of training from scratch, and we apply model compression when a model needs to run faster or on smaller hardware.

Yes, AIGard fine-tunes open-source models, including Llama and Mistral models, for domain-specific tasks.

Fine-tuning is carried out with tools such as PyTorch and Hugging Face, with experiment tracking so that results can be compared across runs. Once tuned, a model can be compressed and optimized, then deployed to the cloud or to edge devices.

By treating the model as one part of a system. Production work covers data pipelines, evaluation, deployment, monitoring and retraining as well as the model itself.

We agree the success metric first, assess whether your data supports the use case, and build the surrounding engineering so the system keeps performing after launch.

Privacy and security controls for each AI use case are defined up front, and engineers work inside your environment under least-privilege access.

We agree which AI tools and models may access your code and data, including retention and training settings, with human approval for sensitive actions.

For Deep Learning, AIGard engineers typically work with PyTorch, TensorFlow, Hugging Face, NVIDIA CUDA, MLflow and Python.

The stack is selected to fit the systems you already run, and engineers are matched to it before they join.

Deep Learning is available through any of AIGard’s three engagement models: staff augmentation, where engineers join your team; a dedicated team that runs the workstream; or software outsourcing, where AIGard delivers a defined project.

The choice depends on how much of the delivery you want to manage, and it can change later under the same agreement.

Engineers typically start within two to four weeks of the scoping call. The exact date depends on the roles, seniority and number of people required.

We confirm a realistic start date in writing before you commit, and you meet the proposed engineers before work begins.

AIGard engineers typically cost 30–50% less than the fully loaded cost of a comparable US-based hire, with no recruiting fees, benefits overhead or insurance costs on your side.

The figure for a specific engagement depends on the model, the roles, the seniority mix and the size of the team. We issue a written proposal after the scoping call, so the commercial terms are agreed before any work starts.

NDAs are signed before any access is given, and engineers work inside your environment under least-privilege access, with permissions limited to their responsibilities.

Access is reviewed as roles change and removed when work ends. Our practices align with ISO 27001, SOC 2, GDPR and HIPAA, with PII masking, encryption and audit logging built into data work. We review your access policies, data-handling needs and vendor requirements during scoping.

You do. Intellectual property assignment is written into every contract, in every engagement model.

That covers source code, designs, documentation, data, models and prompts produced for you. Third-party components and their licenses are identified, so ownership is clear at handover.