Certificate Course in

MLOps with Kubeflow Training

MLOps is the new emerging technology that helps Data Science leaders to build, deploy, and monitor data models. MLOps, at its best, incorporates many efficient tools to speed development and foster production.

60 Hours Classroom & Online Sessions
80 Hours Assignments & Real-Time Projects
Complimentary Python Programming Beginners Course
Complementary Kubernetes for Beginners
Complimentary DevOps for Beginners

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Academic Partners & International Accreditations

Calendar-for-Virtual Interactive Classes

January

Start Date

01^st

January
TBC

Timings
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MLOps with Kubeflow

Total Duration

2.5 Months

Prerequisites

Data Science - Traditional ML algorithms & DL (Neural network) algorithms
Programming - Beginner to Intermediate

MLOps with Kubeflow Course Overview

Machine Learning Operations a.k.a MLOps is fast gaining steam as one of the most sought after skills in the Data Science and Artificial Intelligence domain. The MLOps with Kubeflow course is a first-in-the-industry offering to help Data Scientists and ML Engineers deploy ML models into production at scale and efficiently. This course focuses on the best in class tools and frameworks such as Kubernetes, Kubeflow, Istio, Tensorflow Extended, and Apache Beam among others.

A few years ago, if a professional knew about machine learning, he would have easily got a job in any company of choice. It may still be the case that ML Engineers and Data Scientists are in demand, but there is also an increasingly available supply which will make it difficult to stand out from the competition. Also, enterprises across all industries now have some capability in Data Sciences and are investing in machine learning technologies. However, the industry currently is struggling not to create models, but deploy them into production and monitor them efficiently with optimal use of resources. This has given rise to the intriguing skill called Machine Learning Operations which is quite simply, DevOps for Machine Learning. While it may sound very trivial to perform DevOps for ML Models, it rarely is. MLOps differs prominently from traditional DevOps in the following ways-

1. As part of Continuous Integration (CI), MLOps requires that testing and validation be performed not just on code and its components but also on the train and validation datasets, the data schemas, and the models themselves.

2. Continuous Delivery for MLOps also means that the effort not just applies to a sole software or service but an entire ML pipeline which in turn could be automatically deployed to another microservice.

3. Continuous Training - a trait that is unique to MLOps which focuses on automatically retraining the models periodically and guides how the models are served.

The Rise and Rise of Kubeflow

MLOps with Kubeflow program is a natural extension of the other program offered by 360DigitMG. Previously MLOps courses were being developed using TensorFlow Extended (TFX). Kubeflow is now emerging as the de facto implementation and orchestration mechanism of machine learning model deployment. Kubeflow started as a simple mechanism to facilitate basic ML infra up and running on Kubernetes. Kubeflow’s development was majorly accelerated by two driving forces - the meteoric rise of ML across enterprises and the emergence of Kubernetes as the gold-standard in the infra management layer.

MLOps with Kubeflow Learning Outcomes

MLOps with the Kubeflow is the culmination of years of experience and months of hard work to put together a course that could serve as a guide for production-grade model deployment. As such the participants can expect to know about Machine Learning Life Cycle, common pitfalls while attempting to deploy them effectively, and how to address them. Participants are expected to have a working knowledge of Machine Learning algorithms, lifecycle, and intermediate level programming skills. After completing this course the participants should be able to clearly articulate the need for a robust MLOps strategy and be able to architect, design, and deploy them on on-premise and cloud infrastructure using Kubeflow. As a bonus interested participants will also be exposed to other popular frameworks like MLflow and Apache Airflow.

The need for a MLOps strategy and why organizations need to succeed

Understand containers and get a better understanding of Docker

The role of Kubernetes and how to set up, configure, and operate Kubernetes pods

An introduction to Tensorflow Extended (TFX) and how it forms the foundation of Kubeflow

Understand Kubeflow and its role in the ML Model Development Lifecycle (MLDC)

Install and configure Kubeflow pipelines on-prem and cloud- based solutions

Build pipelines for data ingestion, data preprocessing and feature extraction/engineering using Kubeflow

Deploy and Monitor ML models on major cloud platforms like GCP and AWS using Kubeflow

Block Your Time

60 hours

Classroom Sessions

40 hours

Assignments

40 hours

Live Projects

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Who Should Sign Up?

Data Scientists
Data and Analytics Manager
Business Analysts
Data Engineers
DevOps Engineers
Machine Learning Architects
Model Risk Managers/Auditors

Modules for MLOps with Kubeflow Course

The course modules are designed in a step by step manner to ensure the participants gain a deeper understanding of the MLOps concepts. Firstly, the initial modules will focus on the ML Model Development Lifecycle (MLDC) and why MLOps is necessary. Participants will also be able to understand the project management methodology which is based on the Cross-Industry Standards for Data Mining (CRISP-DM) framework. Then the participants will be introduced to Kubernetes clusters and their inner workings. Participants will slowly work up their way towards Kubeflow and understand how to install and configure it in different environments such as cloud- native, on-prem, hybrid, etc. They will also be introduced to multiple other lower -layer abstractions like Istio, KNative (which are part of the Kubeflow framework) to gain a deeper understanding of Kubeflow operations. Finally, the participants will deploy Kubeflow pipelines across various cloud platforms such as AWS, GCP, etc.

1. The Enterprise Case for MLOps

In today’s world data science has penetrated across all industries and domains and has become ubiquitous. Most data scientists and machine learning engineers are able to come up with amazing models as proof of concepts but they are unable to deploy them in production and at scale. This has given rise to something called MLOps which basically means DevOps for Machine Learning. This chapter dwells deep into this need.

2. Kubernetes Primer

This module offers a complete overview of everything you need to know about Kubernetes. This module introduces Kubernetes and then explains why containers are required. The module explains the basic building blocks of Kubernetes such as pods and how they can be used in applications and finally wraps it up with an explanation of the Kubernetes API.

3. Introducing Kubeflow - How to plan and design a Kubeflow installation

This module will introduce the need for Kubeflow even when Kubernetes is already existing. This module will also attempt to answer how Kubeflow should be installed. It details the security constraints, infrastructure requirements, scalability, and reliability.

4. Kubeflow Security and Networking

This module will touch up some concepts that may be familiar to users in the DevOps space. It will introduce concepts such as public key infrastructure (PKI), authentication, authorization, and role- based access control, (RBAC), Kerberos, and transport layer security (TLS). It will also introduce service mesh management with Istio.

5. Introduction to Kubeflow Pipelines

This module will begin with an introduction of Kubeflow pipelines - which is a platform that comprises:

A user interface that tracks, manages, and executes pipelines
A pipeline execution scheduling engine
Python SDK for managing these pipelines
Leveraging Jupyter Notebooks for using the Python SDK

6. Data Preprocessing and Feature Engineering using Kubeflow Pipelines

Data preprocessing is a multi-stage process which consists of collecting data from disparate sources, augmenting it, calculating basic statistics, handling missing values, and outliers. Feature engineering is the process of deriving additional features or removing unnecessary features to add more predictive power to the ML model. This chapter will introduce participants to the methods provided by Kubeflow to construct easily repeatable data processing and feature engineering pipelines.

7. Training an ML model

Model training is the process of creating logical relationships between ‘training data’ and using it to make predictions on ‘unseen’ data. This chapter will focus on how to train models on Kubeflow using two different frameworks - Tensorflow and Scikit-learn.

8. Model Serving, Monitoring, and Updating

This chapter discusses how to deploy, serve models, and continuously monitor and update them. Model serving means hosting the model which can be interfaced via a service. The models can be served through two approaches - embedded serving and model serving as a service (MaaS).

Trends of MLOps Kubeflow in Malaysia

We can expect that MLOps technology will expand and become more important in the coming years as many organizations are willing to adopt ML into their production and utilize MLOPs applications to scale Machine Learning in their operations. Let’s look at a few trends of MLOps in the near future. One of the trends is the recognition of ML bugs which are different from software bugs. For Detecting Machine Learning bugs, special techniques are required like Model performance predictors, visual debugging tools, etc. Giant companies like Facebook and Uber have emphasized the importance of Machine Learning specific metrics in largescale that range from health checks to ML specific resource utilization metrics. Another trend is the usage of Cloud-based services and SaaS, which makes ML production simple and easy.

To deploy ML models at the beginning is quite difficult, even though many open source tools are available. Cloud services play a critical role in this situation. They are used to optimize accelerators. Cloud services also help organizations to develop ML deployment models without in-house large infrastructure. The availability of open-source tools for the deployment of ML models is another emerging trend. Many vendors are depending on these tools to provide total solutions to their customers. Some of the important tools are Tensorflow, Apache Atlas which is used for Governance, Kubeflow for MLops on Kubernetes, etc. Many big and small companies started adopting these tools for their ML production. MLOps is creating a wave, and many AI-based models are going to depend on MLOps for the successful deployment of models and effective results. This proves that there is much demand for MLOps professionals in the market where they can have a promising career.