Machine learning at scale - Webinar By zekeLabs

zekeLabs
Machine Learning at Scale
Development to Deployment
Skilling for the Future
www.zekeLabs.com

Visit : www.zekeLabs.com for more details
THANK YOU
Let us know how can we help your organization to Upskill the
employees to stay updated in the ever-evolving IT Industry.
Get in touch:
www.zekeLabs.com | +91-8095465880 | info@zekeLabs.com

Modules
1. Understanding Machine Learning Ecosystem
2. The Machine Learning Pipeline & Product stories
3. Data Challenges
4. Taking Machine Learning to Scale using Spark & Kafka
5. Knowing the Unknowns

Module 1
Understanding
Machine Learning
Ecosystem
● Black box Introduction to Machine Learning
● Types of Machine Learning
● Components of AI
● The AI Timeline

What is not Machine Learning ?
● Rule Based Approach
● Legacy Systems

Learning Algorithm
What is Machine Learning ?
● Solve prediction problem
Input Data
● Logic is learned from examples & not by rules
Training Data
Prediction Function
or
Trained Model

Types of Machine Learning
Machine Learning
ReinforcementUnsupervisedSupervised
Task Driven Data Driven Environment Driven

Spam Mail Detection
● Input - Mail
● Output - Spam or Ham
● Supervised Machine Learning,
● Binary Classification Problem

● Input - Sensor Data
● Output - Failure time
● Regression Problem
Predicting Lift Failure

● Input - Accident details
● Output - Insurance amount
● Regression Problem
Predicting Insurance Amount

● Input - Patient Synopsis (fever,
temperature, BP, etc. )
● Output - Diagnosis
● Multi-class classification Problem
Medical Diagnosis

Question - What is common between them ?

Market Segmentation
● Input - Customer Details
● Output - Clusters
● Unsupervised Machine Learning
● Clustering Problem

Robot playing Football
● Input - Player information,
Rewards
● Output - Action to score
● Reinforcement Learning

Module 2
Machine Learning
Pipeline
● Understanding Machine Learning Pipeline
● User Story - Automating customer support
● Implementation
● User Story - Fast Query Chatbots
● Implementation

Machine Learning Pipeline - Business Understanding
● Business understanding includes clarity what you are trying to achieve.
● Machine learning is not possible with small data size.
● Consolidating data pipeline to channelize continues flow of data.
● Web scraping, data lakes access, REST etc.

Machine Learning Pipeline - Data Wrangling
● Production data is never clean.
● It needs a major effort ( around 70% of total effort ) to make it ready for next stage.
● Transforming & mapping data from raw format to another format ready for next stage.

Machine Learning Pipeline - Data Visualization
● Visualization makes it easy to grasp difficult concepts
● Find useful pattern in the data
● Interactively drill down into charts for deeper details

Vectors - Fixed length array of numbers
● Text documents
● Image files
● CSV
● Audio
● Video
● Time Series data
● Many more ...
Machine Learning Pipeline - Data Preprocessing
Feature Extraction

Machine Learning Pipeline - Model Training
Learning Algorithm
Regression/Trees/SVM/Naiv
e Bayes/Neural Networks/
Prediction Function
or
Trained Model

● Linear Regression
● Logistic Regression
● Naive Bayes
● Nearest Neighbors
● Decision Trees
● Ensemble Methods
● Clustering
● Support Vector Machines
● Neural Networks
● CNN
● RNN
● GAN
Machine Learning Pipeline - Learning Algorithms

Prediction
Prediction Function
or
Trained Model

Machine Learning Pipeline - Model Validation
● Training different learning method will give you different trained model.
● Also, each model have huge possibilities of configuration (hyper-parameters).
● Finding the best model among all possibilities & best configuration for it is done as a part
of Model Validation.
● If results are not satisfactory, one has to go back in the chain & fix a few things.

Machine Learning Pipeline - Deployment
Trained Model
Or
Interface Model
Consumers RESTful Interface

1. User Story : Customer Service Industry

1. Reduce manual
effort of classifying
reviews.
2.Channelizing data
from Web server to
Analytics Engine.
1. Getting
data ready for
visualization.
2. Historical
data shows
past trends.
Visualization
of trend
Text needs to
be tokenized
& vectorized
Different
models were
trained.
Naive Bayes,
SGD Classifier
Choose the
best model
with best
hyper-
parameter
Naive Bayes
(MultinomialNB)
was chosen & put
in deployment
1. Implementation : Customer Service Industry

2. User Story : Fast Query Chatbots

2. Implementation : Fast Query Chatbots
1. Reduce manual effort
understanding the text
query
2. Waiting for BI has a
long turnaround time
3. We are trying to do this
using chatbot
1. Getting data
ready for
visualization.
2. Historical
data shows
past trends
Visualization
of trend of
text & sql
Text cannot
be used for
ML
Needs to be
tokenized &
vectorized
Deep learning
models with
different layer
configuration
Choosing the
best model
with best
hyper-
parameter
Model with best
config was chosen
& put in
deployment

3. User Story : Preventing System Failure

Module 3
Data Challenges
● Optimal data size
● Identify data sources
● Identify what is useful in data
● Cleaning data to extract useful information
● Tools & Libraries to clean & extract useful information

Optimal Data size for AI product
● Expectation from a predictor -
Moderate Bias & Moderate
Variance.
● Predictor validation is important.
● The more the data better the
model becomes to a limit.

Identify Data Sources
● No specific order in identifying problem statement & data sources.
● Innovation in this space can happen in both ways - Top-Down & Bottom’s-
Up.
● Data can be historical batch data stored in RDBMS & NoSQL DBs.
● Live streamed data using Kafka.

Identify what is useful in data

Cleaning data to extract useful information

Tools vs Libraries
● Data cleaning tools available in market.
● Why they don’t work in long run?
● Data cleaning libraries available.
● Why are more and more enterprises are embracing libraries?

Changes with change in volume of data

Spark vs Other technologies
● Big Data Compute Framework
● Do data cleaning at scale with unbounded performance
● Talk to different data sources

Module 4
Machine
Learning Pipeline
at Scale
● Machine Learning Pipeline using Spark
● Spark - A very social technology
● Spark for Big Data Cleaning & Wrangling
● Spark for building ML models at Scale
● Validation & monitoring of models
● Deployment using REST interface using Apache Livy

Machine Learning Pipeline using Spark

Spark - A very social technology

Preprocessing Data at Scale
● Scaling
● CountVectorizer
● Binning
● … many things can be done at scale using Spark

Training Models using Spark
● Distributed Model Training using Spark
● Regression
● Classification
● Clustering
● Recommendation Engine

Building Data Pipeline in Spark
● Spark provides in-built Transformers & Estimators.
● Pipeline can be built to connect transformers & estimators.
● Machine Learning Pipeline can be automated.

Module 5
Knowing
the
Unknowns
● Implementing Transformers & Estimators on Spark
● Deep Learning using Spark
● Are model retrainable?
● The skilling journey
● Introducing Apache Beam

Transformers & Estimators on Spark
● Building Custom Transformers
● Building Custom Estimators

What is Deep Learning ?
● Specialized Learning Technique.
● Rather than we choosing features for learning, this technique finds
important feature derivatives.
● Objective is to learn best derived features for prediction.
● It mimics the way our brain learns.
● Very useful for natural language, computer vision, audio, video etc.

Do you always need Deep Learning ?
● More data is required for Deep Learning
● More Compute Power
● Models less interpretable
“Don’t kill a mosquito with a cannon ball”
Don’t use Deep Learning if you don’t need to

Deep Learning using Spark
● Which one to choose - Distributed TensorFlow & DL using Spark.
● Libraries like - spark-dl & elephas

Are models re-trainable ?
● Online learning models in scikit - SGDClassifier, Multinomial Naive Bayes
● Spark ML models are not online learning models

Apache Beam - Probably our next webinar
● Apache Beam is an evolution of the Dataflow model created by Google to
process massive amounts of data.
● The name Beam (Batch + strEAM) comes from the idea of having a unified
model for both batch and stream data processing.
● Programs written using Beam can be executed in different processing
frameworks (via runners) using a set of different IOs (Spark, Flink etc.).

Components of any AI product
Data Compute Talent

Where AI got into in business?

Imp : Advice to executives about AI
● Everybody should embrace modern capability of AI, on other they should
also think about business specific problems. Not every single tool that AI
community can develop can suit them correctly.
● Biggest challenge is people change not technology change, biggest gap
now is people who can map technology to business problem.
● Insourcing vs outsourcing. Building Team vs using enterprise solutions.
● AI will change everything in next few decades. Be a part of it.

Challenges - Data & Security
● Volume of data - Machine learning
on smaller data is infeasible.
● Accessibility of data - Important
data is not accessible & may be in
encrypted format.
info@zekeLabs.com | www.zekeLabs.com | +91

Compute, Storage & Network Power
● AI products needs data gathering from sensors, servers etc.
● Once gathered, data needs to be stored for further processing.
● Learning algorithms & data processing activities need lot of compute
power.

Infrastructure for development
● Finding the best model is an iterative
process.
● More experiments leads better model.
● Hyper-parameter Tuning
● Scaled infrastructure for developer is
important.

Infrastructure for deployment
● Speedy Deployment.
● Easy deployment
● Fluctuating Demand.
● Need of Elastic infrastructure.
● Cost optimization.

Cost optimization:
● Use Open Source alternatives
● Infrastructure optimization
● Don’t reinvent the wheel

Module 3
Impact of AI
● Will AI benefit human ?
● AI in human computer interaction
● Impact of AI on business
● Impact on workplace
● Impact on society
8095465880

AI benefit human - social, environmental
● Predicting diseases
● 60% People would prefer AI assistance over humans as financial advisors
or tax preparers
● 71% people believe that AI will help humans solve complex problems and
help live more enriched lives

AI assistants
● Saves Time
● Calendar events reminder
● Helps get things done

AI advisor & manager at workplace

Impact on Decision Makers
● Adoption of AI advisors

What can be outsourced to AI assistant

Impact of artificial intelligence on society
● People are averse to the idea of availing annual health check-
ups at home with a robotic smart kit (77%) or having chatbot
assistant teachers in universities/ colleges that lower the cost
of overall tuition (61%).
● Responsible AI ensures that its workings are aligned to ethical
standards and social norms pertinent within its scope of
operations.
● Explainable AI is responsible for building AI models with
accountability and the ability to describe or depict why a certain
decision was made by the algorithm.

Module 4
Identify right tools
● Programming Language
● Open source libraries
● Infrastructure Optimizations
● Other alternatives
8095465880

Choose the right Programming Language

Why Python makes life easy ?
● Easy to learn for ETL developers
● Integrates very well with other technologies
● Full-stack development -
○ Dashboard using bokeh,
○ Web application using django,
○ Machine learning models using scikit,
○ Scaling using PySpark

Choose appropriate Libraries
- Statistical Modeling & Data Processing

- Visualization

- Machine Learning or Deep Learning

Infrastructure Optimization
Monolithic or Serverless

Monolithic Infrastructure - Preallocated Infra
Model Training
● Developers request access
whenever required
● Might incur delay in peak
working hours.
● Idle in non-working hours
Model Interfacing
● Idle in non-peak hours.
● May fall short in spikes.
● Pay even if infra is not used

Serverless Infrastructure - Elastic Allocation
Model Training
● No-preallocation
● Pay only for what you use
● Absolute no idle time for infra
● No wait time for developers
Model Interfacing
● Allocate infra only when required
● Scales down during non-peak
hours
● Improved customer experience
even in peak hours

Serverless Infrastructure Solutions
● Open Function as a Service (OpenFaas)
● AWS Lambda
● Google Cloud Function
● Azure Function

Distributed Machine Learning using Spark
● Apache Spark is a distributed data
processing framework.
● Many machine learning algorithms are
implemented in Spark.
● Most of the API’s are same that of scikit-
learn
● Scaled ETL & Machine Learning can be done
using Spark

Other alternatives
Google Cloud AI

Module 5
Build AI Team
● Adoption of AI
● Skills
● Hiring or upskilling
● Upskilling workforce
8095465880

Adoption Strategy
Build Business Case Scale Efficiently
Create Data
Driven Culture

Talent Acquisition
● Upskill your current team ?

Upskilling workforce
● It’s possible to make use of the people who have delivered for you in the
past.

Q & A

Repositories
● https://github.com/zekelabs/machine-learning-for-beginners
● https://github.com/zekelabs/tensorflow-tutorial/
● Dog breed prediction -
https://www.edyoda.com/resources/watch/54AEA4CDC35394F1183A9D
D17AA47/
● Python learning course -
https://www.edyoda.com/resources/videolisting/98/

Machine learning at scale - Webinar By zekeLabs

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