No BS Guide to Deep Learning in the Enterprise
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This presentation provides a taxonomy for understanding deep learning technologies in the enterprise.
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No BS Guide to Deep Learning in the Enterprise
- 1. A No-BS Guide to Deep Learning in the Enterprise Invector Labs
- 2. About Invector Labs • Platform for top-class computer science talent • Uses artificial intelligence to connect enterprises with top freelance talent around the world • Focused on deep-tech • Artificial intelligence • Blockchain technologies • Internet of things • Cybersecurity • Advanced cloud computing • …. • http://invectorlabs.com
- 3. Agenda • Challenges of deep learning in the enterprise • A taxonomy for understanding deep learning technologies in the enterprise • Types of deep learning stacks • Pros/Cons
- 4. 3 Introductory Questions • What is deep learning? • What makes deep learning conceptually challenging? • What makes deep learning practically challenging?
- 5. Deep Learning • Deep learning is a subset of machine learning • Uses a hierarchy of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. • Learns in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners. • Learns multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.
- 6. Deep Learning Sub-Disciplines Deep Learning Convolutio nal Neural Networks Recurrent Neural Networks Adversaria l Neural Networks Reinforce ment Learning Generative Models Transfer Learning ….
- 7. What Makes Deep Learning so Challenging? Curse of Dimensionality • Models with millions of nodes Over/Under Fitting • Models too tailored to the datasets Interpretability • Understanding complex network structures Bias/Variance • Preconceptions included in the datasets
- 8. Implementing Deep Learning in the Enterprise is Brutally Hard
- 9. There are very high barriers of entry…
- 10. To Implement Deep Learning Solutions High Computer Science Barrier Different App Development Lifecyle Overcrowded Market
- 11. High Computer Science Skills • Understanding fundamental deep neural network architectures • Convolutional neural networks • Recurrent neural networks • Reinforcement learning • Adversarial neural networks • Generative models • Ensemble/Transfer learning • …. • Solid mathematical foundation in areas such as probabilistic theory or linear algebra
- 12. We are dealing with a new app lifecycle… Traditional App Lifecycle Machine Learning App Lifecycle Experimentation Model Creation Training Testing Regularization Deployment Monitoring Optimization Design Implementation Deployment Management/ Monitoring
- 13. The Ecosystem is Incredibly Crowded
- 15. How to get started?...
- 16. The Lifecycle of Deep Learning Applications Experimentation Model Creation Training Testing Regularization Deployment Monitoring Optimization
- 17. A Taxonomy of the Deep Learning Ecosystem Cloud Deep Learning Runtimes On-Premise Deep Learning Runtimes High Performance Computing Runtimes Tier1 Deep Learning Frameworks Tier2 Deep Learning Frameworks Cloud ML Platforms Cloud AI APIs Cloud Deep Learning Experimentation Tools On-Premise Deep Learning Experimentation Tools Deep Learning Optimization Tools CLOUD On-PREMISE
- 18. Experimentation…
- 19. Interactive Data Science Environments • Good for: Model Experimentation • Rapid data exploration • Prototyping of algorithms and neural network architectures • Evaluating challenges with models before they get implemented • Mostly support R and Python frameworks • Integrated with cloud and on-premise deep learning runtimes
- 20. Pros-Cons • Test ideas and explore data • Validate different libraries and algorithms • Collaborate with other data scientists • Not designed for production workloads • Mostly constrained to Python environmentsVS.
- 22. Tier 1 Deep Learning Frameworks • Good for: Model implementation • Abstract the fundamentals of a computation graphs in deep neural nets such as tensors, sessions, placeholders • Focused mostly on static computation graphs and low level computation routines • Great support across different runtimes
- 23. Pros/Cons • High performance • Rich algorithm catalogs • Large developer communities • Broad support in deep learning runtimes • Embedded versions that run on mobile and IoT devices • Hard to modify • Difficult to debut • Low level primitives VS.
- 24. Tier 2 Deep Learning Runtimes • Provide higher level APIs for deep learning models • Many use tier 1 frameworks as the underlying runtime mechanism • Some incorporate dynamic computation graphs
- 25. Pros/Cons • Great for prototyping • Easier to write deep neural networks • Easier to debug • Some frameworks like PyTorch enable dynamic computation graphs • Sacrifice control • Constrained to large scale environments VS.
- 26. Tier 2 High Performance ML Runtimes • Good for: Implementing deep learning pipelines that are tightly integrated with big data infrastructures • Enable the infrastructure to execute deep learning pipelines at scale using parallel computation engines • Combine batch, stream, SQL-based computations into deep learning solutions using a single programming model • Leverage mainstream data computation engines
- 27. Pros/Cons • Executing high performance computation job • Leverage your existing big data infrastructure • Write code in familiar languages such as Java or Scala • Limited deep learning capabilities • Constrained algorithm catalog VS.
- 28. Tier 2 Cloud ML Platforms • Good for: Implementing deep learning solutions using native cloud services • Use tools and programming models optimized for your cloud platform • Leverage languages such as R or Python into your cloud deep learning solutions • Expose deep learning models as cloud APIs • Natively leverage cognitive APIs in your deep learning solution
- 29. Pros/Cons • Leverage a native cloud ML service • Integrate with other cloud services • Minimize the infrastructure to create and execute your deep learning applications • Limited extensibility • Limited support for on- premise scenarios VS.
- 30. Self-Service Deep Learning Runtimes • Low-code, graphical user experience for the creation of deep learning model • End to end automation of the deep learning app lifecycle • Proprietary runtime server-side environments
- 31. Pros/Cons • Implementation of simple, well-defined models • Data exploration and working with small datasets • Extensibility • Custom model authoring • Operate across different runtimes • Sophisticated training cycles • Integration with existing systems and APIs VS.
- 32. Deep Learning API Suites • Good for: Using basic deep learning capabilities via APIs • Ready to use, pre-trained models • Focused on established cognitive domains: • Image analysis • Natural language processing • Speech analysis • Video analytics • …
- 33. Pros/Cons • Low level of entry to deep learning • Optimized for small data, basic deep learning scenarios • Extensibility • Custom model authoring • Custom training • Scalability/Pricing ratioVS.
- 35. Hybrid Deep Learning Runtimes • Good for: Automate the complete lifecycle of deep learning applications • Combine many open source stacks into cohesive stacks • Enable the fundamental building blocks of deep learning applications • Data Loading • Model Execution • Performance Monitoring
- 36. Pros/Cons • Executing and managing deep learning applications at scale • Leverage open source stacks in an integrated way • Maintain symmetric hybrid deep learning runtimes between cloud and on- premise environments • Complex to manage and scale VS.
- 37. Cloud Deep Learning Runtimes • Good for: Running deep learning models at scale • Support different deep learning runtimes • Include proprietary tools for model management and optimization • Integrates with other cloud services
- 38. Pros/Cons • Scale deep learning models with minimum infrastructure • Enable a common infrastructure for different deep learning frameworks • Build deep learning applications that integrate with other cloud services • Limited support in on-premise environments VS.
- 39. Managing and Optimizing Models…
- 40. Hyperparameter Optimization Tools • Good for: Fine tuning and evaluating models • Tracking the performance of models across different versions • Run experiments with different hyperparameter configurations
- 41. Pros/Cons • Hyperparameter tuning and optimization • Correlate model performance with different objectives • Integrate with different deep learning frameworks • Lack of interoperability with deep learning runtimes VS.
- 42. Other Important Categories • Specialized frameworks: Bonsai (reinforcement learning) • Training frameworks: OpenAI Gym, Uber’s Horovod • ML Databases: MLDB.ai
- 43. Summary • Building deep learning solutions in the real world is a tough challenge • The deep learning space is incredibly crowded • There are several categories that can help you better navigate the space: • Tier 1-2 deep learning frameworks • Self-service deep learning platforms • Hybrid-Cloud deep learning runtimes • Optimization tools • … • Start small….iterate fast…