Incorporating AI/ML into Your Application Architecture. Norman Sasono CTO & Co-Founder, bizzy.co.id

Transcription

1 Incorporating AI/ML into Your Application Architecture Norman Sasono CTO & Co-Founder, /in/normansasono

2 AI/ML can do wonders. But it has been too hyped up. As Architects/Developers, we need to cut through the hype, and understand how to incorporate AI/ML into our App Architecture, and solve real problems. As Data Scientists, we need to understand how Architects/Developers will use your Model.

3 What has happened? AI/ML is now accessible to many more people Back Then: A few highly specialized individuals Today: Every Developer and Data Scientist

4 Why now? AI/ML has been democratized Convergence of: Algorithmic Advancements Data Explosion Cloud Computing (Computing Power and Storage)

5 AI The ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity. Examples of technologies that enable AI to solve business problems are robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning

6 ML Machine-learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time.

7 AI > ML AI ML

8 ML is a Function Creating Algorithms by training those Algorithms with data. The training will result Predictive Model that provides an estimated output based on given input. The techniques in ML can create decision logic that would be impractical or impossible to build using traditional application development tools and algorithms.

9 Types of Analytics

10 ML: Supervised Learning Linear Regression An algorithm uses training data and feedback from humans to learn the relationship of given inputs to a given output Logistic Regression Linear Quadratic/Discriminant Analysis Decision Tree Naive Bayes Support Vector Machine Random Forest AdaBoost Gradient-Bossting Trees Neural Network

11 ML: Unsupervised Learning An algorithm explores input data without being given an explicit output variable (eg, explores customer demographic data to identify patterns) K-Means Clustering Gaussian Mixture Model Hierarchichal Clustering Recommender System

12 ML: Deep Learning Convolutional Neural Network: A multilayered neural network with a special architecture designed to extract increasingly complex features of the data at each layer to determine the output Recurrent Neural Network: A multilayered neural network that can store information in context nodes, allowing it to learn data sequences and output a number or another sequence

13 Software Development Cycle v ML Development Cycle Note that the ML-Model still needs to be implemented in Production Grade

14 ML Development requires Data Scientist. It requires skills in Data Analysis & Manipulation, Mathematics, Statistics, ML Algorithms and Patterns. Not just Software Development.

15 Architects/Developers, let s say now you need ML in your App. What can you do?

16 Treat ML-based functionality like any other component: Modularize it Keep it loosely coupled

17 The scope of ML models in app architectures is commonly very localized. They perform specific functions. The ML models may be incredibly complex. However, from an app architect's perspective, they can be encapsulated by simple interfaces that take the input data to be processed and return a prediction from the model. Hence, API (in module runtime or remote API).

18 Some Examples of Integrating ML into App Architecture Your App Your App Your App ML Serving Framework Your App API Cloud ML Service Provider- Trained ML Model Using General Purpose ML-Based APIs API Your Own ML Service ML Serving Framework Your- Trained ML Model Using Custom ML APIs ML Serving Framework Your- Trained ML Model Embed Model in App At Build Your- Trained ML Model Your- Trained ML Model Load/Update Model At Runtime

19 Options for Sourcing ML-Based Capabilities Use ML-Based APIs Use 3rd Party Software with Embedded ML Capabilities Use Pre- Trained ML Model Refine/Re- Traine Pre- Trained ML Model Create & Train New Model

20 Options for Sourcing ML-based Capabilities Use ML-based APIs - the ML behind the APIs are managed by the ML Provider (Microsoft, Amazon, Google, etc, for NLP, Image Processing, Audio Processing, Voice Processing, etc) Use 3rd Party Software with Embedded ML Capability - a bundled ML capabilities in Software or Open Source Project (ML capabilities in Mobile Phones, CRM Tools, Productivity Tools, etc) Using a Pre-Trained Model (with an ML Framework) - reuse pre-trained model (Tensorflow Repo, Keras Repo, etc) Refining/Re-Train Pre-Built Model (with an ML Framework) - retrain a model with a specific Data Sets Create & Train New Model (with an ML Framework) - create and train custom ML Model if your data and reqs are proprietary

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24 Data Architecture

25 Key Takeaways Modularize and decouple ML Models as discrete services or components, be it consuming 3rd party ML-based APIs or your own, then compose these into your architecture. Learn and understand basic ML vocabularies. Start with consuming available off-the-shelf ML-based APIs, then custom build your own model later as needed. If you really going to need train an ML Model or create your own custom ML Model, start to build your Data Science team. Data Scientists will work with Software Engineers too for Production Grade model Implementation. Start with Regression, not Deep Neural Network :)

26 Thank You From various sources: Gartner, McKinsey, Microsoft, AWS, /in/normansasono