Imagine there was a way to generate unlimited, pristine quality data to fuel our machine learning models. Data free from biases, labeling errors or privacy pitfalls hindering model accuracy today.
Well, this data utopia is swiftly becoming a reality with synthetic data!
In this guide, we will cover everything you need to know about synthetic data including:
- Demystifying what synthetic data is and its benefits
- Diving deep on leading open source and cloud-based synthesis solutions
- Tips to integrate synthetic data generation into model development workflows
Let‘s get insightful!
What is Synthetic Data?
Synthetic data is artificially generated data mirroring real data distributions but without exposing actual information.
For example, an insurance firm can produce synthesized records of claimant profiles including attributes like demographics, medical histories and past claims. But the people are fake and data is randomly generated from patterns seen in real profiles.
Let‘s examine a few synthetic data traits in more detail:
1. Statistical Fidelity
At its core, synthetic data aims to retain statistical nuances present in original real-world data.
Advanced generative algorithms capture intricate correlations, distributions spanning textual, discrete and temporal signals in data. They create an abstract mathematical representation of such properties.
Synthetic records are then sampled randomly from this modeled probabilistic space ensuring fidelity to real data patterns.
2. No Personal Identifiable Information
A synthetic dataset bears no elements traceable back to actual people unlike real production data.
Such disassociation from individuals‘ records avoids direct personal privacy risks during sharing and usage. This is a welcome relief for regulations like GDPR and CCPA which govern real PII data usage requiring anonymization.
3. Unconstrained Volumes
Since synthetic data is artificially generated, you aren‘t limited by quantity of records collected from customer interactions. In theory, you can produce gigabytes or terabytes more data – handy for the data hungry machine learning models!
In fact Gartner estimates that 60% of data used for enterprise AI projects will be synthetically generated by 2024.
With the core concepts covered, let‘s discuss why synthetic data promise to be a gamechanger.
The Transformative Potential of Synthetic Data
Here are 4 ways synthetic data unlocks tangible benefits when training machine learning models:
1. Mitigates Data Scarcity & Access Issues
Quality training datasets require substantial effort via collection drives, manual labeling and verification etc. Synthetic data alleviates sourcing headaches by working from smaller seed data samples.
Certain niche categories may have few publicly available datasets too. Healthcare, financial services, CPG and industrial IoT analytics suffer from limited sharing due to confidentiality norms. Synthetic data bridges these data access gaps.
2. Uplifts Model Accuracy & Fairness
Well sampled, unbiased synthetic records improve model generalizability and fairness. Synthetic oversampling also makes models robust against subgroup data shifts. Stanford research reveals neural networks built solely on synthetic data can outperform models trained on 10-100X more real-world data across image, text and tabular analytics.
3. Faster Model Development Life Cycle
Getting hurried model projects out the door is challenging without enough labelled datasets. Synthetic data removed delays for data collection, cleaning and annotation – providing readymade abundant training corpora.
Rapid prototyping with synthetic data allows testing many modelling assumptions saving research dead ends. This accelerates getting working models deployed earning business value faster.
4. Regulation-compliant AI Development
Stringent data sovereignty and privacy statutes like GDPR impede global data pooling. Synthetic data sidesteps compliance roadblocks through its disassociated impersonal nature allowing open model development.
Healthcare analytics have used synthetic data to prevent HIPAA violations by producing millions of fake patient records resembling local hospital data.
Clearly synthetic data promises to accelerate, simplify and derisk previously data-starved modeling initiatives. But generating realistic synthetic datasets requires specialized algorithms and platforms.
An Overview of Synthetic Data Generation Tools
The surge in synthetic data adoption has spawned open source solutions alongside fully managed cloud platforms:
Key Categories of Synthetic Data Generators
Let‘s analyze notable options across these tool types.
Prominent Open Source Python Libraries
The Synthetic Data Vault ecosystem from MIT spearheaded accessible open source synthetic data engines leveraging innovations in deep generative models.
Popular statistical and GAN based engines include:
- SDV: A modular library orchestrating various synthetization algorithms
- CTGAN: Modeled tabular data via constrained GANs
- TGAN: TGAN is optimized for time series data generation
These embeddable libraries offer ample flexibility to tailor data models. But productionizing open source code demands non-trivial data engineering.
AutoML Cloud Platforms
Fully managed cloud platforms minimize integration hassles providing intuitive interfaces for synthetic data creation.
Notable services include:
- Gretel: End-to-end workflow for sensitive data synthesis
- Mostly AI: Browser-based data modeling and mass synthesis
- Resemble AI: Encrypted model training on distributed web infrastructure
These platforms automate infrastructure provisioning, leveraging scale-out parallel Spark jobs for big data synthesis pipelines. But subscription costs apply on top of usage based pricing.
Specialist Toolkits
Some tools target niche synthetic data use cases:
- Image synthesis: TensorFlow GAN collections for photo-realistic synthetic imagery
- Text generation: Deep learning models like GPT-3 which create human-like synthetic text
- Edge device simulation: Synthesis solutions mimicking sensors, computer vision feeds
Specialist tools excel at a genre but rarely generalize beyond their domain.
Now with an overview of the tooling landscape, let‘s do a deeper dive!
Feature Comparison of Top Synthetic Data Tools
I shortlisted some popular representative tools for an comparative analysis across key yardsticks:
Let me elaborate on some comparison criteria:
Statistical Accuracy & Integrity
We want assurances that synthetic data preserves core statistical essence of original datasets.
Fidelity metrics include:
- Prediction errors if ML models trained on synthetic vs real data.
- Divergence scores quantifying distribution skew.
- Visualizations inspecting multidimensional correlations.
Synthetic records straying significantly from expected formats indicate poor generalization.
Scalability & Throughput
Production systems need to synthesize billions of records daily across terabytes of data. Performance benchmarks should confirm:
- Number of rows generated per minute
- Peak memory consumption constraints
- Parallelizable data transformations
We desire linear scaling in synthetic data volumes ensuring velocities keep pace with business.
Security & Compliance
For applications dealing with sensitive information, accredited security controls and compliance coverage is vital especially in regulated sectors like Healthcare.
- Isolated cloud infrastructure
- Control over synthetic data residency
- Audited sanitization protocols
Governance gaps around synthetic data may heighten data protection risks.
Let‘s focus now on putting synthetic data into action!
Workflows for Operationalizing Synthetic Data
Streamlining synthetic data flows critically ensures sustained improvements versus one-off gains. Here are 5 best practices I recommend:
1. Establish Baseline Metrics
Compare model quality metrics on representative ML use cases leveraging real-world datasets versus synthetic surrogate samples. Demonstrate clear metric uplifts from adding synthetic data through A/B testing.
2. Ensure Wide Data Distribution Coverage
Analyze corner case synthetic records manually to catch potential modeling gaps. Statistical assertions should unit test random samples flagging anomalies.
3. Retrain Models on Recent Data
Inevitable schema updates or emerging data trends may cause concept drift. Hence refresh stale synthetic data models through periodic retraining to prevent accuracy decay over time.
4. Safeguard Transparency & Privacy
Data ethics demand full visibility into modeled synthetic distribution rules. Ensure algorithms don‘t secretly encode biases or unwanted residual attributes from original datasets.
5. Automate Generation Pipelines
Trigger and schedule bulk synthetic data outputs within standard ETL/ELT workflows instead of manual synthesis. This scales seamless, versioned integration with downstream analytics.
Now that we have compelling context, let‘s get hands on!
Generating Synthetic Customer Data Using SDV
I will demonstrate creating fake customer transaction records using the popular SDV open source library.
Step 1 – Import Python Modules
First import required classes and helper functions:
from SDV import Demo, SDV
from SDV.tabular import CTGANStep 2 – Load Source Data
We load some sample customer profile data through utility demo loader:
data = Demo.load_demo()This is our real-world distribution to be modeled.
Step 3 – Fit Synthetic Data Model
Now a CTGAN model statistical learns metadata from loaded data:
model = CTGAN()
model.fit(data) Step 4 – Generate Synthetic Profiles
Finally we sample 10,000 new synthetic customer records:
synthetic_profiles = model.sample(10000)
print(synthetic_profiles.head())And we have accurately modeled synthetic data ready for downstream use cases!
While coding synthetic data engineering requires some learning, the unlimited flexibility makes it worthwhile. AutoML platforms alternatively trade off customization for simplicity.
When Should You Use Synthetic Data?
Based on our analysis, here is guidance on apt use cases for synthetic data:
In a nutshell, synthetic data acceleration works best on structured, tabular datasets given mature generative algorithms today. Unstructured data like complex imagery or video still challenges existing synthetic techniques.
Precursors to usage also require historical baseline data pools for the algorithms to learn distributions.
Regulated domains like finance and healthcare leading synthetic adoption to address data access bottlenecks and compliance overheads.
With the exponential evolution of generative AI, synthetic data promises to soon power a majority of enterprise machine learning initiatives!
So are you ready to ride the synthetic data wave to turbocharge your model development?
