As machine learning and AI continue to transform industries, more and more companies are looking to leverage predictive models to gain a competitive edge. However, developing high-quality models requires significant data, computing power, and human expertise. For many companies, especially smaller organizations or those just starting out with AI, the cost to build and maintain models in-house can be prohibitive.
What factors influence model cost?
There are several key factors that determine the overall cost to build, deploy and manage predictive models:
Data acquisition and preprocessing
Gathering quality, relevant data is often the most expensive part of model development. Data may need to be purchased from third-party providers orrequire extensive engineering work to clean and preprocess. Larger datasets typically produce more accurate models but cost more upfront.
Model experimentation and optimization
Data scientists need time to experiment with different algorithms, model architectures, and parameters to find the best performing model. More complex models like deep neural networks require more computation resources and tuning.
Cloud services for development and deployment
Specialized hardware like GPUs and TPUs can accelerate model training but incur ongoing cloud computing costs. Hosting models in production also requires cloud infrastructure.
Model maintenance and monitoring
Models degrade over time and need to be retrained on new data periodically. Monitoring systems are required to detect model drift and performance issues.
Data scientist and engineering talent
Developing and maintaining models requires specialized expertise which is expensive, especially for top talent. Smaller teams slow down the model development lifecycle.
While some costs like cloud services are straightforward to quantify, data acquisition and team productivity can vary widely across use cases.
Cost breakdown for common model types
Here is a breakdown of typical costs associated with some popular types of machine learning models:
| Model Type | Data Requirements | Compute Needs | Engineer Time | Total Cost |
|---|---|---|---|---|
| Linear Regression | Low, <100K rows | Low, CPU | 2-4 weeks | $5K-$20K |
| Logistic Regression | Medium, 1M rows | Low, CPU | 4-8 weeks | $30K-$60K |
| Random Forest | Medium, 1M rows | Medium, CPU cluster | 8-12 weeks | $80K-$120K |
| Neural Network | High, 10M+ rows | High, GPU cluster | 12-26 weeks | $150K-$250K+ |
As you can see, neural networks have the highest costs due to larger data needs and intensive compute requirements for training. But the upside is they tend to have better predictive performance for complex data like images, video, speech, and text.
Strategies for reducing model costs
If building models in-house is too expensive, what are some options to cut costs? Here are a few strategies:
Leverage pre-trained models
Many cloud providers offer pre-trained models for common tasks like image classification or object detection. These models were trained on large datasets so they can be used with minimal additional training, reducing engineering time significantly. APIs and microservices make it easy to integrate these models.
Use automated machine learning
Automated ML tools like Azure ML, DataRobot and H2O Driverless AI can automatically test multiple models and find the best performing one. This speeds up development by reducing manual data scientist time.
Deploy on efficient infrastructure
Managed cloud services like AWS SageMaker, Azure Machine Learning, and GCP AI Platform optimize model training and deployment automatically for efficiency and cost savings.
Focus on incremental improvements
Rather than building models from scratch, start with simpler models or ensembles and make incremental improvements. Look for small wins first before investing in complex models.
Build internal data science expertise
Over the long-term, having in-house data scientists and ML engineers provides more flexibility and helps institutionalize knowledge. But focus on hiring mid-level talent rather than senior level to control costs.
Outsourced modeling services
If current data science bandwidth is limited, companies may want to consider partnering with specialized machine learning consultants and agencies. These services allow accelerating model development without having to immediately hire more internal staff.
Typical pricing models for outsourced data science services include:
- Fixed fee project-based pricing – for well defined projects
- Time and materials – for more open ended consulting engagements
- Monthly retainer – for ongoing model development and maintenance
For small to medium sized models, project fees can range from $10,000 to $100,000+ depending on complexity. Larger engagements around enterprise-level initiatives are typically $250,000+. Expect strategic partnerships to cost $25,000+ per month for part-time support.
When evaluating consultants, look for technical expertise in your industry as well as experience deploying models into production. Having end-to-end capabilities from raw data to application integration is ideal.
Leveraging AutoML and MLOps
Emerging techniques like Automated Machine Learning (AutoML) and MLOps promise to further streamline model building and drive down costs:
Automated Machine Learning
AutoML tools automate key steps in the model lifecycle including:
- Automatic feature engineering
- Neural architecture search
- Hyperparameter tuning
- Model selection
This reduces manual oversight needed from data scientists.
MLOps
MLOps (DevOps for machine learning) automates model retraining, deployment, monitoring and governance. This improves efficiency and reduces drift.
Together, AutoML and MLOps aim to produce a self-serve modeling platform that reduces human effort and allows citizen data scientists to train and manage models with minimal help.
The future of low-cost modeling
Here are some other trends that will continue lowering barriers to AI adoption:
- More curated, industry-specific data lakes
- Improved natural language interfaces for non-experts
- Code-free modeling tools
- Growth of model marketplaces and APIs
- Better tools for model governance, auditability, and explainability
Democratizing access to robust and scalable predictive models will enable more organizations to take advantage of AI and machine learning. Striking the right balance between customizability and ease-of-use will be key to mass adoption moving forward.
Conclusion
Developing machine learning models can entail significant upfront investment. But companies have many options to reduce costs – whether by using pre-built solutions, automating parts of the process, or outsourcing to experts. The explosion in compute power, data availability, and AutoML is making models more accessible than ever across industries.
Carefully evaluating business needs and allocating budget to balance internal vs external resourcing are key. With the right strategic approach, predictive models can deliver material benefits well beyond their upfront costs for organizations at any scale. The future points towards a democratization of powerful and scalable models – unlocking productivity gains and new insights from data.