As someone who has consulted on machine learning (ML) initiatives for over a decade, I‘ve seen firsthand the transformation ML can drive in organizations. However, I‘ve also witnessed many teams struggle with the complexity of in-house ML model development.
Outsourcing parts or all of the ML process offers a solution – but it also poses unique considerations.
In this comprehensive guide, we‘ll dive deep into:
- The exponential growth in enterprise ML adoption
- Where outsourcing fits vs in-house or consulting
- Key benefits and risks to evaluate
- Best practices to drive outsourcing success
Let‘s get started.
The Massive Growth of Enterprise ML
Machine learning has rapidly gone mainstream across industries. Per IBM‘s AI Adoption Index 2022, 85% of businesses are now exploring or actively using ML and AI.
This represents a massive opportunity, with global ML spending predicted to reach $96 billion by 2026. However, for many companies AI investments have yet to pay off – only 8% of firms report gaining measurable value from ML.
So what‘s holding adoption back? Talent is one of the biggest constraints. An estimated 95% of data science projects never make it into production due to skill gaps.
Figure 1: Machine learning adoption is surging, but scaling value remains a challenge. [Sources: 1, 2]
As demand for scarce ML expertise explodes, many firms are turning to outsourcing as an alternative.
How Outsourcing Complements In-House & Consulting Models
Outsourcing can power successful ML adoption – but it‘s not the only approach. Here‘s how it fits with in-house development and consulting engagements:
In-House ML Teams
Building internal ML capabilities allows closer integration with business processes. But it requires substantial investment and time to hire and skill up talent.
With outsourcing, you can spin up and down ML capacity faster without fixed overheads. Think of it as an elastic extension of your team.
Outsourcing also provides domain expertise from implementing models across various clients and industries.
ML Consulting Firms
Consultants offer advisory services across ML strategy, architecture, and program management. Outsourcing provides dedicated production-grade model building resources.
Many clients leverage both – consultants to design solutions and outsourcing vendors to develop and implement them. This combines industry expertise with technical specialization.
The bottom line? Outsourcing occupies a vital middle ground between the flexibility of consulting and permanence of in-house teams.
Key Benefits of ML Outsourcing
Let‘s examine five compelling benefits of an outsourcing approach compared to solely in-house ML initiatives:
1. Access Specialized Talent and Experience
The number one benefit of outsourcing is access to qualified talent. As discussed earlier, the supply of ML experts lags severely behind market demand.
Large outsourcing firms have already tackled the talent recruitment and retention challenge. They employ hundreds of data scientists, ML engineers, and project managers.
This talent is expensive and time-consuming for a single company to attract and grow organically. Partnering with established providers yields ready access.
According to KPMG, it takes large firms over 6 months to fill ML roles. Specialist vendors have pre-screened bench strength.
Outsourcing also provides cross-industry experience. For example, experts who have built predictive maintenance models across manufacturing, utilities, and mining can share best practices.
Such versatility is hard to cultivate in-house.
2. Significantly Lower Costs
The Total Cost of Ownership (TCO) of outsourced ML projects is estimated to be 30-50% below fully in-house efforts.
Lower costs stem from avoiding fixed overheads of salaries, infrastructure, and learning curves of permanent teams. Specialist vendors already have optimized processes and reusable frameworks.
According to PwC, in-house ML proof-of-concepts cost ~$250,000 vs $150,000 with outsourcing. The gap widens further in later stages:
Consider outsourcing as a variable expense that flexes with your project pipeline vs fixed costs of permanent headcount.
3. Faster Implementation Times
Outsourcing accelerates your ML application development through reuse of proven building blocks.
Pre-trained models, workflow templates, code libraries, and deployment automation tools amortize learnings from vendor engagements.
According to McKinsey, outsourcing reduces ML development timelines by 30-50%.
Your in-house staff also avoids getting bogged down experimenting with new technologies. They can focus on high-value business needs while outsourcing handles implementation.
Avoiding costly distractions and reliance on trial-and-error methods are key sources of speed.
4. Advanced Data Engineering Capabilities
Data is the lifeblood of ML but wrangling it demands expertise. Outsourcing partners bring specialized data engineering skills:
- Sourcing and labeling: Finding and correctly tagging training data
- Cleansing and preprocessing: Fixing inconsistencies and outliers
- Feature engineering: Enhancing raw data to uncover useful patterns
- MLOps: Automating large-scale data processing pipelines
These capabilities take years to instill organically. Outsourcing gives you a head start.
5. More Flexible Resourcing Options
Outsourcing provides greater flexibility to scale ML capabilities on-demand compared to in-house resources.
Partners offer varied staffing models – individual contractors, dedicated teams, managed capacity – to meet changing needs.
This allows optimizing for different phases, from aggressive prototyping to steady-state enhancement. You expand and contract dynamically without laborious hiring and firing cycles.
Key Risks and Mitigations in ML Outsourcing
While the benefits are compelling, outsourcing does come with unique risks to evaluate:
1. Data Security and Potential Loss of IP
Sharing proprietary data such as customer information with third-parties always carries inherent risk. However, there are ways to mitigate this:
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Conduct rigorous security assessments of potential vendors, reviewing their controls, protocols, and past audits.
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Limit data access through anonymization, synthetic data, and federated learning techniques.
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Utilize contractual clauses clearly specifying security requirements and remedies for non-compliance.
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Maintain oversight of how vendor teams store, process, and share your data.
With emphasis on due diligence and continuous governance, data security risks of outsourcing can be reduced.
2. Less Visibility as Work Is Offsite
Proximity enables closer supervision and course correction. With outsourced ML work, direct visibility is reduced.
Mitigate this by structuring engagements to maintain involvement through mechanisms like:
- Code repository access for continuous integration
- Requirement walkthroughs before each sprint
- Ongoing model evaluation and feedback loops
- Semi-regular onsite presence and touchpoints
While offsite, outsourced teams should not equate to "out of sight, out of mind". Leverage tools to stay connected.
3. Finding the Right-Fit Partner
All outsourcing firms are not equal. Without diligence evaluating capabilities, culture fit, and governance, engagements can underdeliver or pose unnecessary risks.
A rigorous vendor selection process is key, assessing factors like:
- Specific ML capabilities required
- Relevant industry experience
- Client references and feedback
- Pricing models and total cost
- Security and compliance track record
This upfront vetting sets the stage for choosing the right partner positioned to drive success.
7 Best Practices for ML Outsourcing Engagements
Once you‘ve selected a trusted partner, optimize the engagement by planning across the full ML lifecycle:
1. Start with A Pilot Project
First, execute a smaller pilot to establish team rhythms before committing to larger initiatives.
Use this to evaluate capabilities, culture synergies, and deliverable quality.
2. Structure Payments Based on Milestones
Link installment payments to verified delivery milestones vs upfront lump sums or hourly billing.
This ties partner incentives to your desired outcomes.
3. Maintain Involvement in Data Prep and Model Evaluation
Stay closely involved during data preprocessing and post-model performance reviews. Don‘t fully handoff these critical steps.
4. Make Models Interpretable
Ensure models are interpretable to build understanding and trust. Ask partners to explain how models produce their results.
5. Create Feedback Loops
Schedule periodic model reviews and enhancement cycles. Continual retraining is key for maintaining accuracy.
6. Build Some Internal ML Capability
Absorbing some partner learnings internally will enable better oversight and reduced long-term dependency.
7. Institute IP and Data Governance Upfront
Specifically define ownership, security protocols, access controls contractually before engagement starts.
Following structured best practices in defining, contracting, executing, and governing outsourced ML projects is key to managing risks and harnessing benefits.
Key Takeaways on Machine Learning Outsourcing
We‘ve covered a lot of ground on the potential of ML outsourcing. Let‘s recap the key insights:
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Outsourcing provides flexible ML talent augmentation compared to in-house teams or pure consulting engagements.
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Key benefits include reduced costs, faster development, specialized expertise, and flexibility.
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However, also plan for risks like data security, lower visibility, and partner selection.
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Following best practices around diligent vendor selection and project governance is critical for managing the risks.
The bottom line is that outsourcing will play an increasingly crucial role in scaling enterprise ML – if harnessed strategically.
Hopefully this guide offers useful perspectives to consider when exploring outsourcing to drive your ML and AI initiatives. Please reach out for help applying these lessons to your specific needs and use cases.
