Certificate Course in MLOps with Kubeflow
- 60 Hours Classroom & Online Sessions
- 80 Hours Assignments & Real-Time Projects
- Complimentary ML on Cloud Modules
- Complimentary Python Programming Course
- Complementary Kubernetes for Beginners
- Complimentary DevOps for Beginners
MLOps is an emerging field that is gaining momentum among Data Scientists, ML Engineers, and AI enthusiasts. MLOps is considered as the next destination for Data Scientists. It is used effectively by industries to develop and deploy data models. As per the new research reports, MLOps is predicted to grow rapidly in the coming years and is estimated to reach up to $4.5 billion by the end of 2025. With this tremendous growth companies are looking forward to adopting this innovation for better production. There is an urgent need for efficient skilled individuals in this discipline. Datamaze always strives ahead and tries to bring a positive change in the IT industry by launching first of its kind training programs that help students to foster in their careers and achieve success.
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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.
Who should signup?
- IT Engineers
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- Banking and Finance Analysts
- Marketing Managers
- Supply Chain Professionals
- HR Managers
- Math, Science and Commerce Graduates
The Rise and Rise of Kubeflow
MLOps with Kubeflow program is a natural extension of the other program offered by Datamaze. 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
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.
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.
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.
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.
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.
- Typecasting
- Handling Duplicates
- Outlier Analysis/Treatment
- Zero or Near Zero Variance Features
- Missing Values
- Discretization / Binning / Grouping
- Encoding: Dummy Variable Creation
- Transformation
- Scaling: Standardization / Normalization
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.
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.
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).
Tools Covered
Trends of MLOps Kubeflow in India
The term MLOps or Machine Learning operations started emerging in 2020 and became widely used with Artificial Intelligence and Machine Learning. Many enterprises started adopting AI technologies, which led to the development and high usage of tools and techniques for the processes to work efficiently and effectively develop ML models combined with DevOps. Let’s see some of the trends that organizations expect from MLops and how these help Data Scientists to develop and deploy models from research to production. The emerging trend is that ML models have become more scalable compared to earlier where they were brittle ML pipelines. Models that are deployed by MLOPs are robust and are production-ready with high performance. From Day 1 they work efficiently and continue to grow. ML models because of their varied key product features are being used in major sectors like Healthcare, Security, Banking, etc and they are generating a lot of revenue.
Enterprises like Banking and Insurance companies use hundreds of models, by this they face different kinds of challenges. These challenges arise due to heterogeneity in workflows of ML and there is no route to track the source and assets of models across the organization. This issue can be resolved by MLOPs. MLOps tools like Kubeflow will give the organizations the visibility of the models to avoid duplication and liabilities. MLOPs brings together Data Scientists and the DevOps team to work and bring out significant outputs. . By observing the impact of MLOps on the development and deployment of ML models in various industries, we can certainly stay that it is going to stay and continue in the future creating many opportunities for the aspirants.
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