Building machine learning muscle in your team & transitioning to make them do machine learning at scale. We also discuss about Spark & other relevant technologies.
Design Patterns for Pods and Containers in Kubernetes - Webinar by zekeLabszekeLabs Technologies
The combination of Docker and Kubernetes is quickly becoming the de-facto standard for building Microservices. Whether you are a developer or an architect you need to know how to bundle your application into Containers and Pods. Docker and Kubernetes give a lot of good features out of the box. To effectively leverage these features, you need to know - how to use them, what are some commonly used Pod design patterns and the best practices.
In this webinar, we will explore various such questions and their answers along with appropriate examples. Some of those questions would be-
1. When and how to build multi-container pods?
2. What are some of the well-adopted design patterns for pods?
3. What are some multi-pod design patterns?
4. How to use Lifecycle hooks, Init Containers and Health probes?
Github repo - https://github.com/ashishrpandey/pod-design-pattern-webinar
What is Serverless?
How it evolved?
What are its features?
What are the tradeoffs?
Should I use serverless?
How is it different from the container as a service?
Our subject matter expert answered these in a technology conference hosted by one of our esteemed client that works in the domain of Marketing Data Analytics.
Agenda
1. The changing landscape of IT Infrastructure
2. Containers - An introduction
3. Container management systems
4. Kubernetes
5. Containers and DevOps
6. Future of Infrastructure Mgmt
About the talk
In this talk, you will get a review of the components & the benefits of Container technologies - Docker & Kubernetes. The talk focuses on making the solution platform-independent. It gives an insight into Docker and Kubernetes for consistent and reliable Deployment. We talk about how the containers fit and improve your DevOps ecosystem and how to get started with containerization. Learn new deployment approach to effectively use your infrastructure resources to minimize the overall cost.
Deploying Anything as a Service (XaaS) Using Operators on KubernetesAll Things Open
Presented by: Jeff Spahr
Presented at the All Things Open 2021
Raleigh, NC, USA
Raleigh Convention Center
Abstract: Kubernetes has long since solved compute as a service, but what if you want to deploy higher level services without reimplementing the finer details of how to scale, cluster, and upgrade those services? Custom Resource Definitions (CRDs) allow users to expand the Kubernetes API to create resources like 'kind: elasticsearch' or 'kind: mariadb'. Operators manage those CRDs and take on orchestration and lifecycle management of those services.
In this talk I'll cover the what and why of Operators on Kubernetes with a focus on what real world problems this solves for Kubernetes end users. I'll walk through deploying operators for common high level services that make up a production application.
The XaaS walkthrough and demo will include some of the following technologies:
* Cloud Services (EC2, S3)
* Databases (MariaDB, Vitess, Elasticsearch)
* Load balancers (F5, NGINX)
* Streaming (Kafka, RabbitMQ)
You'll leave this session with a foundation to start offering XaaS to your end users.
Slides from my presentation on microservices, spring cloud oss, service registry, zuul, hystrix. We also discuss various flavours of service registry for instance when zookeeper, eureka, consul. Then we took a first look on zuul and its key components, hystrix, hystrix dashboard, all accompanied with a demo hosted on github.
Devops Columbia October 2020 - Gabriel Alix: A Discussion on TerraformDrew Malone
Wonder why you would want to use Terraform vs it competitors? Why not stick with CFNs, you ask? CDK should do the trick right? Come enjoy an opinionated take on using Terraform, for the betterment of your sanity. Also, includes a light intro to Terraform for those who are new to it.
Gabriel is a Cloud Technologist and accomplished Cyber practitioner who has led & built complex workloads across the IC for 20+ years. He's a native New Yorker from Washington Heights, with a boisterous laugh and calm demeanor. Gabriel has built a strong career starting in Federal service and has evolved into CTO and now VP of IC at Applied Insight. In addition to his technical accolades, he's a social leader that believes in building and growing strong teams
Design Patterns for Pods and Containers in Kubernetes - Webinar by zekeLabszekeLabs Technologies
The combination of Docker and Kubernetes is quickly becoming the de-facto standard for building Microservices. Whether you are a developer or an architect you need to know how to bundle your application into Containers and Pods. Docker and Kubernetes give a lot of good features out of the box. To effectively leverage these features, you need to know - how to use them, what are some commonly used Pod design patterns and the best practices.
In this webinar, we will explore various such questions and their answers along with appropriate examples. Some of those questions would be-
1. When and how to build multi-container pods?
2. What are some of the well-adopted design patterns for pods?
3. What are some multi-pod design patterns?
4. How to use Lifecycle hooks, Init Containers and Health probes?
Github repo - https://github.com/ashishrpandey/pod-design-pattern-webinar
What is Serverless?
How it evolved?
What are its features?
What are the tradeoffs?
Should I use serverless?
How is it different from the container as a service?
Our subject matter expert answered these in a technology conference hosted by one of our esteemed client that works in the domain of Marketing Data Analytics.
Agenda
1. The changing landscape of IT Infrastructure
2. Containers - An introduction
3. Container management systems
4. Kubernetes
5. Containers and DevOps
6. Future of Infrastructure Mgmt
About the talk
In this talk, you will get a review of the components & the benefits of Container technologies - Docker & Kubernetes. The talk focuses on making the solution platform-independent. It gives an insight into Docker and Kubernetes for consistent and reliable Deployment. We talk about how the containers fit and improve your DevOps ecosystem and how to get started with containerization. Learn new deployment approach to effectively use your infrastructure resources to minimize the overall cost.
Deploying Anything as a Service (XaaS) Using Operators on KubernetesAll Things Open
Presented by: Jeff Spahr
Presented at the All Things Open 2021
Raleigh, NC, USA
Raleigh Convention Center
Abstract: Kubernetes has long since solved compute as a service, but what if you want to deploy higher level services without reimplementing the finer details of how to scale, cluster, and upgrade those services? Custom Resource Definitions (CRDs) allow users to expand the Kubernetes API to create resources like 'kind: elasticsearch' or 'kind: mariadb'. Operators manage those CRDs and take on orchestration and lifecycle management of those services.
In this talk I'll cover the what and why of Operators on Kubernetes with a focus on what real world problems this solves for Kubernetes end users. I'll walk through deploying operators for common high level services that make up a production application.
The XaaS walkthrough and demo will include some of the following technologies:
* Cloud Services (EC2, S3)
* Databases (MariaDB, Vitess, Elasticsearch)
* Load balancers (F5, NGINX)
* Streaming (Kafka, RabbitMQ)
You'll leave this session with a foundation to start offering XaaS to your end users.
Slides from my presentation on microservices, spring cloud oss, service registry, zuul, hystrix. We also discuss various flavours of service registry for instance when zookeeper, eureka, consul. Then we took a first look on zuul and its key components, hystrix, hystrix dashboard, all accompanied with a demo hosted on github.
Devops Columbia October 2020 - Gabriel Alix: A Discussion on TerraformDrew Malone
Wonder why you would want to use Terraform vs it competitors? Why not stick with CFNs, you ask? CDK should do the trick right? Come enjoy an opinionated take on using Terraform, for the betterment of your sanity. Also, includes a light intro to Terraform for those who are new to it.
Gabriel is a Cloud Technologist and accomplished Cyber practitioner who has led & built complex workloads across the IC for 20+ years. He's a native New Yorker from Washington Heights, with a boisterous laugh and calm demeanor. Gabriel has built a strong career starting in Federal service and has evolved into CTO and now VP of IC at Applied Insight. In addition to his technical accolades, he's a social leader that believes in building and growing strong teams
Kubernetes is much more than a runtime platform for Docker containers. Through its API not only can you create custom clients, but you can also extend Kubernetes. Those custom Controllers are called Operators and work with application-specific custom resource definitions.
Not only can you write those Kubernetes operators in Go, but you can also do this in Java. Within this talk, you will be guided through setting up and your first explorations of the Kubernetes API within a plain Java program. We explore the concepts of resource listeners, programmatic creation of deployments and services and how this can be used for your custom requirements.
The recent constraints on businesses have pushed organizations to accelerate their plans for moving operations to the digital world—often shrinking timelines from years to months. Microservice architecture (MSA) is critical to accomplish fast innovation and the APIs exposed from microservices should be secured, managed, observed and monetized. All these steps require significant time.
Kubernetes is designed for automation. The Operator pattern captures how you can write code and extend the Kubernetes cluster to automate a task going beyond its out-of-the-box capabilities. In this session, Lakmal will demonstrate and share his experience of how to automate microservice to API by introducing a Kubernetes Operator that works together with an API Management system while enhancing the developer experience.
Serverless Functions: Accelerating DevOps AdoptionAll Things Open
Presented by: Daniel Oh
Presented at the All Things Open 2021
Raleigh, NC, USA
Raleigh Convention Center
Abstract: Serverless functions are driving the fast adoption of DevOps development and deployment practices today. To successfully adopt serverless functions, developers must understand how serverless capabilities are specified using a combination of cloud computing, data infrastructure, and function-oriented programming. IT Ops teams also need to consider resource optimization (memory and CPU) and high-performance boot and first-response times in both development and production environments for faster time to market/service. What if we didn’t have to worry about all of that?
In this session, I’ll be speaking about what kinds of open source projects and tools enable you to write a serverless function with superfast boot and response times and built-in resource optimization. Then, you’ll understand how these capabilities take you to advanced DevOps practices as well as business acceleration. Furthermore, developers can avoid the extra work of developing a function from scratch, optimizing the application, and deploying it to Kubernetes.
This presentation was made by Mangesh Patankar (Developer Advocate - IBM Cloud) as part of Container Conference 2018: www.containerconf.in.
"How do we make microservices resilient and fault-tolerant? How do we enforce policy decisions, such as fine-grained access control and rate limits? How do we enable timeouts/retries, health checks, etc.?
A service-mesh architecture attempts to resolve these issues by extracting the common resiliency features needed by a microservices framework away from the applications and frameworks and into the platform itself. Istio provides an easy way to create this service mesh."
This presentation was made as part of the Container Conference 2018 - www.containerconf.in
"Containers have gained lot of attention ever since it came into existence. And why not? With the speed and ease it provides for running user application, it is definitely the most preferred solution for many of the real world use cases.
OpenStack, on the other hand is a cloud solution which has always evolved in supporting newer technologies. OpenStack have many projects around containers that tries to cater the practical use cases. Some of the real world use cases that OpenStack fulfils are:
OpenStack deployment could be very complex and so is its upgrade. OpenStack Helm, Triple-O and Kolla uses Kubernetes, Docker that helps its users to easily deploy and upgrade their cloud.
Containers lacks the security as compared to VMs, so many users want to run their application on secure environment. OpenStack Zun enables Clear Containers and Kata Containers that provides the security of VMs and speed of containers.
Other use cases include running Kubernetes cluster on OpenStack, CI/CD, managing applications using microservices which can be done by Magnum, Zuul, Zun respectively. In this presentation, we will talk about the practical use cases where containers can help us and what OpenStack provides to fulfill those requirements."
A Look into the Mirror: Patterns and Best Practices for MirrorMaker2 | Cliff ...HostedbyConfluent
From migrations between Apache Kafka clusters to multi-region deployments across datacenters, the introduction of MirrorMaker2 has expanded the possibilities for Apache Kafka deployments and use cases. In this session you will learn about patterns, best practices, and learnings compiled from running MirrorMaker2 in production at every scale.
Advanced dev ops governance with terraformJames Counts
DevOps project sprawl is real! Large organizations with many teams need to support a variety of configurations from infrastructure governance to domain-specific app deployments, all while enforcing good security practices like least privilege for each team. Maintaining these controls by hand leads to complexity, stagnation, and insecure shortcuts. In this session, you'll learn how Terraform can automate this configuration--using Terraform--and make doing the right thing easy!
Breaking the Monolith: Organizing Your Team to Embrace MicroservicesPaul Osman
Microservices are becoming an increasingly popular way to build software systems. Thanks to evangelism from companies like Netflix, Amazon, Gilt, ThoughtWorks and SoundCloud, more organizations are considering whether or not they should adopt this practice.
In this talk, I’ll discuss our experiences evolving 500px from a single, monolithic Ruby on Rails application to a series of composable microservices written in Ruby and Go. I’ll talk about the challenges we faced from a business, engineering, QA and operations perspective and how moving to microservices encouraged (or required) change in our organizational structure and culture.
In this talk, you’ll learn how a change in how we develop software affected team structures, development environments, testing infrastructure and encouraged us to explore moving to cloud hosting and to move closer to continuous delivery. You’ll also learn about the pitfalls, both expected and unexpected that we experienced along the way.
By sharing some of our experiences, I hope to provide some guidance to engineering teams considering whether or not to adopt microservices.
The combination of StackPointCloud with NetApp creates NetApp Kubernetes Service, the industry’s first complete Kubernetes platform for multi-cloud deployments and a complete cloud-based stack for Azure, Google Cloud, AWS, and NetApp HCI. Further, Trident is a fully supported open source project maintained by NetApp, designed from the ground up to help meet the sophisticated persistence demands of containerized applications.
Cost-effective Compute Clusters with Spot and Pre-emptible Instances - KubeCo...Platform9
Kubernetes and Spot/Pre-emptible Instances (SPIs) are arguably a match made in heaven. Traditionally, the uncertainty of SPIs (they can be terminated at any time due to price fluctuations) have made managing them tricky, and restricted them to specific workloads and use cases.
Kubernetes, in contrast, not only handles node failure very well, it has trained developers and architects to design applications to tolerate and even embrace failure. The prospect of Kubernetes abstracting the complexities of SPIs is now a reality, enabling applications to take advantage of low-cost compute across different clouds and possibly vendors.
The purpose of this talk is to educate the audience on strategies for making the most out of this powerful combination. Specifically, we will discuss these topics:
1. What are spot bidding strategies, and what is their cost vs. predictability trade-off?
2. What class of Kubernetes applications would benefit the most from SPIs?
3. Available Kubernetes mechanisms (e.g taints/tolerations, affinity, availability zones) for placing applications based on their tolerance with SPIs
3. Implementation strategies (e.g. blending multiple autoscaling groups to satisfy both SPI-optimized applications vs. applications that are more mission-critical or stateful)
4. What out-of-the box solutions exist, either free or commercial?
5. How to take abstract away clouds from different regions and vendors, allowing workloads to always take advantage of the best available pricing?
The talk concludes with real-world test results involving multiple use cases and configurations, giving the audience an idea of the potential cost savings and trade-offs (if any) of combining Kubernetes and SPIs.
Kubernetes is much more than a runtime platform for Docker containers. Through its API not only can you create custom clients, but you can also extend Kubernetes. Those custom Controllers are called Operators and work with application-specific custom resource definitions.
Not only can you write those Kubernetes operators in Go, but you can also do this in Java. Within this talk, you will be guided through setting up and your first explorations of the Kubernetes API within a plain Java program. We explore the concepts of resource listeners, programmatic creation of deployments and services and how this can be used for your custom requirements.
The recent constraints on businesses have pushed organizations to accelerate their plans for moving operations to the digital world—often shrinking timelines from years to months. Microservice architecture (MSA) is critical to accomplish fast innovation and the APIs exposed from microservices should be secured, managed, observed and monetized. All these steps require significant time.
Kubernetes is designed for automation. The Operator pattern captures how you can write code and extend the Kubernetes cluster to automate a task going beyond its out-of-the-box capabilities. In this session, Lakmal will demonstrate and share his experience of how to automate microservice to API by introducing a Kubernetes Operator that works together with an API Management system while enhancing the developer experience.
Serverless Functions: Accelerating DevOps AdoptionAll Things Open
Presented by: Daniel Oh
Presented at the All Things Open 2021
Raleigh, NC, USA
Raleigh Convention Center
Abstract: Serverless functions are driving the fast adoption of DevOps development and deployment practices today. To successfully adopt serverless functions, developers must understand how serverless capabilities are specified using a combination of cloud computing, data infrastructure, and function-oriented programming. IT Ops teams also need to consider resource optimization (memory and CPU) and high-performance boot and first-response times in both development and production environments for faster time to market/service. What if we didn’t have to worry about all of that?
In this session, I’ll be speaking about what kinds of open source projects and tools enable you to write a serverless function with superfast boot and response times and built-in resource optimization. Then, you’ll understand how these capabilities take you to advanced DevOps practices as well as business acceleration. Furthermore, developers can avoid the extra work of developing a function from scratch, optimizing the application, and deploying it to Kubernetes.
This presentation was made by Mangesh Patankar (Developer Advocate - IBM Cloud) as part of Container Conference 2018: www.containerconf.in.
"How do we make microservices resilient and fault-tolerant? How do we enforce policy decisions, such as fine-grained access control and rate limits? How do we enable timeouts/retries, health checks, etc.?
A service-mesh architecture attempts to resolve these issues by extracting the common resiliency features needed by a microservices framework away from the applications and frameworks and into the platform itself. Istio provides an easy way to create this service mesh."
This presentation was made as part of the Container Conference 2018 - www.containerconf.in
"Containers have gained lot of attention ever since it came into existence. And why not? With the speed and ease it provides for running user application, it is definitely the most preferred solution for many of the real world use cases.
OpenStack, on the other hand is a cloud solution which has always evolved in supporting newer technologies. OpenStack have many projects around containers that tries to cater the practical use cases. Some of the real world use cases that OpenStack fulfils are:
OpenStack deployment could be very complex and so is its upgrade. OpenStack Helm, Triple-O and Kolla uses Kubernetes, Docker that helps its users to easily deploy and upgrade their cloud.
Containers lacks the security as compared to VMs, so many users want to run their application on secure environment. OpenStack Zun enables Clear Containers and Kata Containers that provides the security of VMs and speed of containers.
Other use cases include running Kubernetes cluster on OpenStack, CI/CD, managing applications using microservices which can be done by Magnum, Zuul, Zun respectively. In this presentation, we will talk about the practical use cases where containers can help us and what OpenStack provides to fulfill those requirements."
A Look into the Mirror: Patterns and Best Practices for MirrorMaker2 | Cliff ...HostedbyConfluent
From migrations between Apache Kafka clusters to multi-region deployments across datacenters, the introduction of MirrorMaker2 has expanded the possibilities for Apache Kafka deployments and use cases. In this session you will learn about patterns, best practices, and learnings compiled from running MirrorMaker2 in production at every scale.
Advanced dev ops governance with terraformJames Counts
DevOps project sprawl is real! Large organizations with many teams need to support a variety of configurations from infrastructure governance to domain-specific app deployments, all while enforcing good security practices like least privilege for each team. Maintaining these controls by hand leads to complexity, stagnation, and insecure shortcuts. In this session, you'll learn how Terraform can automate this configuration--using Terraform--and make doing the right thing easy!
Breaking the Monolith: Organizing Your Team to Embrace MicroservicesPaul Osman
Microservices are becoming an increasingly popular way to build software systems. Thanks to evangelism from companies like Netflix, Amazon, Gilt, ThoughtWorks and SoundCloud, more organizations are considering whether or not they should adopt this practice.
In this talk, I’ll discuss our experiences evolving 500px from a single, monolithic Ruby on Rails application to a series of composable microservices written in Ruby and Go. I’ll talk about the challenges we faced from a business, engineering, QA and operations perspective and how moving to microservices encouraged (or required) change in our organizational structure and culture.
In this talk, you’ll learn how a change in how we develop software affected team structures, development environments, testing infrastructure and encouraged us to explore moving to cloud hosting and to move closer to continuous delivery. You’ll also learn about the pitfalls, both expected and unexpected that we experienced along the way.
By sharing some of our experiences, I hope to provide some guidance to engineering teams considering whether or not to adopt microservices.
The combination of StackPointCloud with NetApp creates NetApp Kubernetes Service, the industry’s first complete Kubernetes platform for multi-cloud deployments and a complete cloud-based stack for Azure, Google Cloud, AWS, and NetApp HCI. Further, Trident is a fully supported open source project maintained by NetApp, designed from the ground up to help meet the sophisticated persistence demands of containerized applications.
Cost-effective Compute Clusters with Spot and Pre-emptible Instances - KubeCo...Platform9
Kubernetes and Spot/Pre-emptible Instances (SPIs) are arguably a match made in heaven. Traditionally, the uncertainty of SPIs (they can be terminated at any time due to price fluctuations) have made managing them tricky, and restricted them to specific workloads and use cases.
Kubernetes, in contrast, not only handles node failure very well, it has trained developers and architects to design applications to tolerate and even embrace failure. The prospect of Kubernetes abstracting the complexities of SPIs is now a reality, enabling applications to take advantage of low-cost compute across different clouds and possibly vendors.
The purpose of this talk is to educate the audience on strategies for making the most out of this powerful combination. Specifically, we will discuss these topics:
1. What are spot bidding strategies, and what is their cost vs. predictability trade-off?
2. What class of Kubernetes applications would benefit the most from SPIs?
3. Available Kubernetes mechanisms (e.g taints/tolerations, affinity, availability zones) for placing applications based on their tolerance with SPIs
3. Implementation strategies (e.g. blending multiple autoscaling groups to satisfy both SPI-optimized applications vs. applications that are more mission-critical or stateful)
4. What out-of-the box solutions exist, either free or commercial?
5. How to take abstract away clouds from different regions and vendors, allowing workloads to always take advantage of the best available pricing?
The talk concludes with real-world test results involving multiple use cases and configurations, giving the audience an idea of the potential cost savings and trade-offs (if any) of combining Kubernetes and SPIs.
Lessons learnt and system built while solving the last mile problem in machine learning - taking models to production. Used for the talk at - http://sched.co/BLvf
From data ingestion, processing, model deployment to prediction - machine learning is hard! Join me to learn how serverless can make it all easier so you can stop worrying about the underlying infrastructure layer, and focus on getting the most value out of your data and development time.
While the adoption of machine learning and deep learning techniques continue to grow, many organizations find it difficult to actually deploy these sophisticated models into production. It is common to see data scientists build powerful models, yet these models are not deployed because of the complexity of the technology used or lack of understanding related to the process of pushing these models into production.
As part of this talk, I will review several deployment design patterns for both real-time and batch use cases. I’ll show how these models can be deployed as scalable, distributed deployments within the cloud, scaled across hadoop clusters, as APIs, and deployed within streaming analytics pipelines. I will also touch on topics related to security, end-to-end governance, pitfalls, challenges, and useful tools across a variety of platforms. This presentation will involve demos and sample code for the the deployment design patterns.
Introduction to Machine Learning - WeCloudDataWeCloudData
In this talk, WeCloudData introduces the lifecycle of machine learning and its tools/ecosystems. For more detail about WeCloudData's machine learning course please visit: https://weclouddata.com/data-science/
Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning ModelsAnyscale
Apache Spark has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do I deploy these model to a production environment? How do I embed what I have learned into customer facing data applications?
In this webinar, we will discuss best practices from Databricks on
how our customers productionize machine learning models
do a deep dive with actual customer case studies,
show live tutorials of a few example architectures and code in Python, Scala, Java and SQL.
Feature Store as a Data Foundation for Machine LearningProvectus
Looking to design and build a centralized, scalable Feature Store for your Data Science & Machine Learning teams to take advantage of? Come and learn from experts of Provectus and Amazon Web Services (AWS) how to!
Feature Store is a key component of the ML stack and data infrastructure, which enables feature engineering and management. By having a Feature Store, organizations can save massive amounts of resources, innovate faster, and drive ML processes at scale. In this webinar, you will learn how to build a Feature Store with a data mesh pattern and see how to achieve consistency between real-time and training features, to improve reproducibility with time-traveling for data.
Agenda
- Modern Data Lakes & Modern ML Infrastructure
- Existing and Emerging Architectural Shifts
- Feature Store: Overview and Reference Architecture
- AWS Perspective on Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data architects & analysts, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Gandhi Raketla, Senior Solutions Architect, AWS
- German Osin, Senior Solutions Architect, Provectus
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-feature-store-as-data-foundation-for-ml-nov-2020/
BigQuery ML - Machine learning at scale using SQLMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery.
We will see how can we use CREATE MODEL sql syntax to build different models such as:
-Linear regression
-Multiclass logistic regression for classification
-K-means clustering
-Import TensorFlow models for prediction in BigQuery
We will see how we can apply these models on tabular data in retail and marketing use cases.
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
This presentation covers an overview of Analytics and Machine learning. It also covers the Microsoft's contribution in Machine learning space. Azure ML Studio, a SaaS based portal to create, experiment and share Machine Learning Solutions to the external world.
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
The Data Science Process - Do we need it and how to apply?Ivo Andreev
Machine learning is not black magic but a discipline that involves statistics, data science, analysis and hard work. From searching patterns and data preparation through applying and optimizing algorithms to obtaining usable predictions, one would need background and appropriate tools.
But do we need it, when there is already available AI as a service solution out there? Do we need to try hard with artificial neural networks? And if we decide to do so, what tools would be a safe bet?
In this session we will go through real world examples, mention key tools from Microsoft and open source world to do data science and machine learning and most importantly - we will provide a workflow and some best practices.
Containerization of your application is only the first step towards modernizing your application. Building cloud-native application requires other tools like Container orchestration platform, Service Mesh tool, Logging & Alert Monitoring tool and Visualization tools.
Real cloud-native platforms need to be equipped with the necessary tool-stack like Kubernetes, Istio, Prometheus, Grafana, and Kiali.
In this webinar, we will cover building a cloud-native platform from zero.
Take home from the webinar -
- What and Why of a cloud-native application
- Steps to build a cloud-native platform from scratch and its challenges
- A high-level overview of Istio, Prometheus, Grafana, and Kiali
- Integrating your cloud-native application with Istio, Prometheus, Grafana, and Kiali
- Live Demo - Deploy, Monitor, and control a full-fledged Microservice-based application.
Information Technology is nothing but a reflection of the needs of Business.
Before Industry 4.0, as IT professionals we were just 'coding' or 'decoding' the trend of Business. Any change in the Business scenario would shake the IT sector but the reverse was not true.
But now, after the Industry 4.0, due to High-Speed Internet boom, omniChannel presence of consumer needs, market consolidation, and above all - consumer psyche, the business service providers cannot wait for long to see their product in the market.
This is where there is a call for Process Change - from Waterfall to Agile.
WHAT THIS WEBINAR IS ALL ABOUT:
1. Discuss the macroscopic view of Business & Technology and how they beautifully merge together
2. How Agile is becoming more relevant to the current trend
3. What preparatory works are needed to get into an Agile perspective
4. The Agile StoryBoard - a walkthrough of concepts and terminologies
5. Do's and Don'ts of 'Team Agile'
6. Next Steps
The slides talk about Docker and container terminologies but will also be able to see the big picture of where & how it fits into your current project/domain.
Topics that are covered:
1. What is Docker Technology?
2. Why Docker/Containers are important for your company?
3. What are its various features and use cases?
4. How to get started with Docker containers.
5. Case studies from various domains
Terraform is an Infrastructure Automation tools. This can work equally good for on-premises, public cloud, private cloud, hybrid-cloud and multi-cloud infrastructure.
Visit us for more at www.zekeLabs.com
Terraform is an Infrastructure Automation tools. This can work equally good for on-premises, public cloud, private cloud, hybrid-cloud and multi-cloud infrastructure.
Visit us for more at www.zekeLabs.com
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
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7. What is not Machine Learning ?
● Rule Based Approach
● Legacy Systems
8. 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
9. Types of Machine Learning
Machine Learning
ReinforcementUnsupervisedSupervised
Task Driven Data Driven Environment Driven
10. Spam Mail Detection
● Input - Mail
● Output - Spam or Ham
● Supervised Machine Learning,
● Binary Classification Problem
11. ● Input - Sensor Data
● Output - Failure time
● Supervised Machine Learning,
● Regression Problem
Predicting Lift Failure
19. Module 2
Machine Learning
Pipeline
● Understanding Machine Learning Pipeline
● User Story - Automating customer support
● Implementation
● User Story - Fast Query Chatbots
● Implementation
21. 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.
22. 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.
23. 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
24. 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
25. Machine Learning Pipeline - Model Training
Learning Algorithm
Regression/Trees/SVM/Naiv
e Bayes/Neural Networks/
Prediction Function
or
Trained Model
26. ● 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
28. 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.
31. 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
33. 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
35. 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
36. 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.
37. 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.
40. 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?
42. Spark vs Other technologies
● Big Data Compute Framework
● Do data cleaning at scale with unbounded performance
● Talk to different data sources
43. 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
47. Preprocessing Data at Scale
● Scaling
● CountVectorizer
● Binning
● … many things can be done at scale using Spark
48. Training Models using Spark
● Distributed Model Training using Spark
● Regression
● Classification
● Clustering
● Recommendation Engine
49. 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.
51. Module 5
Knowing
the
Unknowns
● Implementing Transformers & Estimators on Spark
● Deep Learning using Spark
● Are model retrainable?
● The skilling journey
● Introducing Apache Beam
53. 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.
54. 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
55. Deep Learning using Spark
● Which one to choose - Distributed TensorFlow & DL using Spark.
● Libraries like - spark-dl & elephas
56. Are models re-trainable ?
● Online learning models in scikit - SGDClassifier, Multinomial Naive Bayes
● Spark ML models are not online learning models
58. 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.).
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64. 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.
65. 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.
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66. 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.
67. 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.
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68. Infrastructure for deployment
● Speedy Deployment.
● Easy deployment
● Fluctuating Demand.
● Need of Elastic infrastructure.
● Cost optimization.
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70. Cost optimization:
● Use Open Source alternatives
● Infrastructure optimization
● Don’t reinvent the wheel
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71. 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
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72. 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
79. 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.
80. Module 4
Identify right tools
● Programming Language
● Open source libraries
● Infrastructure Optimizations
● Other alternatives
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82. 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
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87. 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
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88. 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
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89. Serverless Infrastructure Solutions
● Open Function as a Service (OpenFaas)
● AWS Lambda
● Google Cloud Function
● Azure Function
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90. 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
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92. Module 5
Build AI Team
● Adoption of AI
● Skills
● Hiring or upskilling
● Upskilling workforce
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