The list below is dynamic and evolves as new models and technologies emerge daily. If you’re interested in working with a specific technology, simply ask us. If it’s not already in our arsenal, our development team typically requires about one month to become proficient.
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Frameworks: MXNet, PyTorch, Keras, Caffe2, Deeplearning4j, Detectron
Cloud Platforms: AWS, Google Cloud, Azure
Generative AI Models: GPT-4, GPT-3,GPT-3.5 Turbo, Lamda, DALL-E, Codex, Stable diffusion, Google Gemini, Claude 3, Transformer
Libraries: TensorFlow, Tensor2Tensor, tf-slim, NLTK, spaCy, AutoKeras, TensorRT, FastAI, TenorFlow Lite, PyTorch Mobile, Scikit-learn, NumPy, Pandas
Algorithms: Clustering, Metric Learning, FewShot Learning, Supervised/Unsupervised Learning, Online Learning, Ensemble Learning, Transfer Learning, Reinforcement Learning
Neural Networks: CNN,  RNN, Variational Autoencoders, Bayesian Network, Autoregraession Networks, Long Short-term Memory (LSTM)
Learning Paradigms: Representational Learning, Manifold Learning
Module/Toolkits: ONNX, Core MLModule/Toolkits - ONNX, Core ML
Image Classification Models: VGG-16, ResNet 50, Inceptoinv3, EfficientNet, YOLO, SSD, U-net, Vision Transformer, CLIP
Programming Languages: Python, R Programming, Java, JavaScript, Django, React, Angular, Vue,js, Ruby, Flask, ExpressJs
AI Development Tools: Jupyter, Anaconda, Google Colab, Deepnote,  RStudio, MiniConda, Virtualenv, Pienv, Kaggle Kernals, MS Azure Notebooks, Amazon Sagemaker Studio Labs, IBM
Data Warehousing & Management: Apache Spark, Hadoop, NoSQL,PostgreSQL,  MongoDB Amazon Redshift, Apache Flink, Apache Storm, Google BigQuery, Azure Synapse, Bigtable, Cassranda, Cosmos, Oracle, SQL Service, MySQL, NVIDIA RAPIDS, Apache HIVE
Intergration & Deployment: Kubernetes, Docker, Ansible, Terraform, Github Action, Jenkins, Splunk
Data Visualisation & Reporting: Tableau, Power BI, Matplotlib, Seaborn, Plotty, Amazon QuickSight, ChartJS, Apache Superset
API Development: Rest API, GraphQL API's, FastAPI
Edge Devices: Raspberry Pi, Jetson Nano, Intel NCS,  Deepstream, TAO Toolkit
Text Processing: OpenAI, Llamaindex, Langchain, Langflow, Dify, Flowise
Analytics: Rasa, Dialogflow, Logstash, Kibana, Grafana, Wavenet, Lyrebird, Resemble AI
Security: Oauth, let's encrypt, JWT, OpenID

• Visual Detection and Inspection
• Chatbots and Virtual Assistance
• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Data Science and Data Analytics
• Process Automation

The time it takes to develop an AI model can vary significantly based on several factors, including the complexity of the model, the size and quality of the data, and the specific application you're working on. However, here are some general stages in the process and timeframes:
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1. Data Collection and Pre-processing (2 weeks to 6 months)
2. Model Selection and Design (1 week to 1 month)
3. Training the Model (1 week to several months)
4. Model Evaluation and Fine-Tuning (2 weeks to 3 months)
5. Deployment and Integration (2 weeks to 6 months)
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Total Time Estimate: 2 months to 12+ months.

‍Simple Projects: If you're using pre-trained models and data is already prepared, it could take just a few months (2-3 months).

Complex or Novel Projects: For more complex tasks (e.g., building a generative model from scratch with new research or large datasets), the timeline could extend to 6-12 months or longer.

Summary of Estimated Costs

Data Acquisition & Storage: $1,000 to $50,000+
‍Hardware (GPU/TPU/Cloud): $5,000 to $100,000+/month
‍AI Talent: $50,000 to $500,000 (depending on team size and project duration)
‍Model Development: $10,000 to $250,000+
Licensing and Software: $1,000 to $50,000+/annually
‍Testing: $10,000 to $100,000+/week
‍Deployment & Integration: $10,000 to $100,000+
‍Maintenance & Updates: Ongoing (usually 20% of initial development costs per year)
‍Compliance & Security: $10,000 to $50,000+

Total Cost Estimate
For a typical AI project, costs can range from $50,000 to $1,000,000+, depending on scale, scope, and customisation.

All costs in US$

1. Define Objectives and Use Cases (1-2 weeks)

• Identify business goals, use cases, and target users
• Define success metrics and ethical considerations

2. Data Collection and Preparation (4-8 weeks)

• Collect relevant datasets (public, proprietary, or synthetic data)
• Clean, pre-process, and format the data (tokenization, normalization)
• Data augmentation if needed (e.g., adversarial examples, synthetic samples)

3. Model Selection and Architecture Design (2-4 weeks)

• Choose the right model type (Transformer, GAN, VAE, etc.)
• Design and optimise the model architecture
• Define hyperparameters and training strategy

4. Model Training (6-12 weeks)

• Train on GPUs/TPUs (longer for large models)
• Implement optimisations (dropout, batch normalization, fine-tuning)
• Conduct preliminary testing and early stopping if needed

5. Evaluation and Testing (4-6 weeks)

• Use quantitative metrics (BLEU, FID, perplexity)
• Perform human evaluation (user testing, Turing test)
• Identify and mitigate bias or hallucinations

6. Deployment and Integration (3-6 weeks)

• Optimise the model for inference (quantisation, pruning)
• Deploy to cloud services or APIs (AWS, Azure, on-prem)
• Integrate into business applications (chatbots, content generation, creative tools)

7. Monitoring and Continuous Improvement (Ongoing)

• Implement logging and real-time monitoring
• Update models with new data (retraining every 3-6 months)
• Address ethical concerns and model drift

Total Estimated Timeline: 4 to 6 Months for MVP
‍For a production-ready model, expect 6-12 months, depending on complexity.