Trending December 2023 # Kubernetes: Product Overview And Insight # Suggested January 2024 # Top 18 Popular

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See the full list of top container management tools

Users generally praise Kubernetes for its user focus, strong API support and the ability to run it on-premises or in the cloud. It also has attracted a large and strong multi-stakeholder community – meaning its growth will remain robust.

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Clearly, Kubernetes has emerged as a powerful tool for deploying, automating, scaling and managing components. The container control tool defines building blocks and uses them to manage activities related to software development. It runs containers at scale and deploy microservices. It is built into Docker and other container tools, services and platforms, including AWS and Azure. The service offers a robust set of APIs that allow it to work with numerous other tools.

Kubernetes has completely changed how we use applications. While containers helped developers speed up application delivery, Kubernetes made application deployment and day two operations seamless and more programmatic. The fact that Kubernetes can support both stateless and stateful applications is helping organizations embrace cloud native without much of operational investments.

Kubernetes delivers an open source system for managing and orchestrating containers in the cloud. It was developed by Google, but it is now managed by the Cloud Native Computing Foundation.

Powerful controls are at the center of an effective container initiative. Kubernetes delivers an array of features and functions. These include: service delivery and load balancing, storage orchestration, automated rollout and rollbacks, batch execution, automated binpacking, self-healing, horizontal scaling and the ability to update secrets and application configuration without rebuilding an image and exposing any information. The Kubernetes API supports powerful scheduling capabilities through pods, which manage a volume on a local disk or a network drive. This allows users to manage containers and microservices more easily by combining and recombining pods as needed.

Docker and other container tools.

Microsoft Windows, Linux

Kubernetes works across infrastructures and cloud services. It’s nearly ubiquitous because it delivers broad and deep support for container management and orchestration through APIs. It supports nearly every major type of persistent volume, including ASWElastic BlockStore, AzureFile, ZureDisk, NFS and iSCSI.

Powerful scheduling tools that use pods to support clusters, containers and compute resources. Kubernetes also includes experimental support for managing Nvidia and AMD GPUs spread across nodes.

Kubernetes offers Transport Level Security (TLS) for all API traffic. Features API authentication and API authorizations. Numerous other controls.

The control panel provides information and insights into scheduling, APIs, service and cloud management. Kubernetes excels in service discovery and provides strong management capabilities through unique IP addresses and a single DNS name for a set of containers.

The Kubernetes tools is open source and available at no cost. However, when it’s built into commercials solutions the price varies for those solutions.

Features Kubernetes

Supported platforms Supports Docker and other container tools. Windows and Linux.

Key features Supports service delivery and load balancing; storage orchestration; automated rollout and rollbacks; batch execution; automated binpacking; self-healing; horizontal scaling. Powerful scheduling through pods.

High marks for infrastructure management and orchestration. Some complain that the platform and certain features can be difficult to use.

Pricing and licensing Free open source version but some vendors offers proprietary tools at varying costs.

Features Kubernetes

Supported platforms Supports Docker and other container tools. Windows and Linux.

Key features Supports service delivery and load balancing; storage orchestration; automated rollout and rollbacks; batch execution; automated binpacking; self-healing; horizontal scaling. Powerful scheduling through pods.

High marks for infrastructure management and orchestration. Some complain that the platform and certain features can be difficult to use.

Pricing and licensing Free open source version but some vendors offers proprietary tools at varying costs.

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Syntax And Examples Of Kubernetes Kubectl

Introduction to Kubernetes Kubectl

Kubernetes kubectl provides us a command-line interface to interact with Kubernetes clusters. It can be installed on any machine or workstation so that we can manage our Kubernetes cluster remotely. We can manage multiple clusters using ‘use-context’ command from the same machine or workstation. It is also known as ‘Kube Control’. We can manage nodes in the cluster such as draining node for maintenance, update taint on a node, etc. Whenever we run kubectl command it looks for the kubeconfig file in $HOME/.kube folder.

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Syntax and Parameters of Kubernetes Kubectl


kubectl [command] [TYPE] [NAME] [flags]


let’s understand each component of the syntax:

command: it defines what action or operation we want to perform on any object or resource of the cluster like get, describe, delete, etc.

type: it defines on what type of resource we want to perform the action like pods, deployments, services, etc. We can use singular, plural or abbreviated forms, for example, to list a pod we can use type as pod, pods or pod. It is also case-insensitive.

Name: it defines on which resource we want to perform the operation and it is case-sensitive which means POD1 and pod1 is two different resources in Kubernetes cluster.

Examples of kubernetes Kubectl

Below are the examples given:

kubectl get pods kubectl get pods nginx-6db489d4b7-hzvwx

Explanation: In the above example, the first command listed all pods running under default namespace. To get specific pod we need to give the name of the resource, here pod name is “nginx-6db489d4b7-hzvwx”. If we want to list all pods in all namespaces, we use “–all-namespaces” flag as below: –

kubectl get po --all-namespaces

Kubectl has a good documentation and we can know all commands it has, using ‘–help’ flag as below: –

kubectl --help

If we see the above snapshot, what we understand is that commands are divided into groups like basic commands, deploy commands, cluster management commands, troubleshooting commands etc. and most of the commands are self-explanatory.

Basic Commands of Kubernetes Kubectl with Examples

Let’s explore some of the basic but essential commands:

1. create

This command can be used to create a new resource from a file mostly it is a yaml file or from a stdin mostly from the terminal.



kubectl create -f my-nginx.yml

Here is the code of chúng tôi file:

apiVersion: v1 kind: Pod metadata: name: my-nginx labels: app: nginx spec: containers: - name: my-nginx image: nginx port: - containerPort: 80

Explanation: In the above example, it created a pod with name my-nginx.

2. get

We use ‘get’ command to know the status of any resources like Pods, Deployments, Services, etc. We have just created a pod and we want to know the status of the pod, we can use get command as below: –



$kubectl get pods $kubectl get pods my-nginx

Explanation: In the above snapshot, the first command is used to list all pods running under default namespace. If the pods are running under a different namespace, we need to specify the namespace as well. The second command displays the status of specific pods.

3. expose

It is used to expose our deployment, pods, replicaset, service, and replication controller as a Kubernetes service to access it from the host. For example, we have created an nginx pod and now want to access it from our host, we need to create a service using expose command as below: –



kubectl get svc kubectl expose pod my-nginx --port=80 --target-port=80 --name=my-nginx-svc

Explanation: In the above snapshot, the first command is used to list available services and we can see only one service is there. Second command is used to expose the newly created ‘my-nginx’ pod and source and destination port is the same and given a name of the service called ‘my-nginx-svc’ however service name is optional here, if we don’t provide service name it will pick pod name as service name by default. Also you can expose the same pod multiple times by changing the service name. When we run the first command second time, we can see there is a new service called ‘my-nginx-svc’ is visible now and if we curl the IP of that service we can access our nginx pod from the host itself.

Note: Pod must have at least one label to it otherwise you will get an error.

4. run

It is used to run any image in the cluster. When we use ‘run’ command it creates a deployment by default and runs a pod under this deployment and by default it sets replicas equal to 1. If we delete the pod running with that deployment, deployment is going to create a new pod and it will continue. We need to delete the deployment if we have to delete the pod running under this deployment.


kubectl run test-nginx --image=nginx kubectl run --generator=run-pod/v1 test-nginx2 --image=nginx

Explanation: In the above snapshot, we run annginx image and by default, Kubernetes creates a deployment with run command however it is deprecated. This command might not work in future versions. If we have to create only a pod using ‘run’ command, we need to use ‘–generator=run-pod/v1’ option or else use ‘create’ command to create pods or any resources from a file or stdin.

5. edit

it is used to edit any existing resource in the cluster. It opens a yaml file of that resource. We need to just make the changes to the file and save it. It will be applied to the resource. For example, if we want to make changes in our running ‘my-nginx’ pod, we can use the ‘edit’ command as below.



kubectl edit pod my-nginx

kubectl get pod my-nginx --show-labels

Explanation: In the above snapshot, we edited the ‘my-nginx’ pod and changed the environment label from ‘production’ to ‘test’.

6. describe

It is used to know all about any resources like pod, deployment, services etc. It is very useful for troubleshooting. For example, if we want to know more about our ‘my-nginx’ pod as we get very less information using ‘get’ command. We can use ‘describe’ command as below: –



$kubectl describe pod my-nginx

Explanation: In the above snapshot, we get all details of our ‘my-nginx’ pod starting from name to containers, mounts, networks, events, etc. Events are very useful to troubleshoot any issue in the pod.

7. scale

This command is used to scale our deployment, replica set or replica controller as per our requirement. For example, we have a deployment called ‘test-nginx’ running with 1 replica and want to scale the deployment to run 3 replicas of that pod, we can use scale command as below: –


kubectl get deploy kubectl scale deployment test-nginx --replicas=3 kubectl get pods

Explanation: In the above snapshot, we have only one replica of the deployment and after increasing the replicas count from 1 to 3, we can see 3 pods are running now.

8. drain

It is used to drain the node for maintenance activity as if there is any hardware failure on that node and we don’t want to schedule a pod on that until maintenance has been performed on it.



kubectl get nodes kubectl drain node01 --ignore-daemonsets

Explanation: In the above snapshot, we have 2 nodes cluster with 1 master and 1 worker node. We can see after draining the node status has been changed from ‘Ready’ to ‘Ready,SchedulingDisabled’ that means Kubernetes controller is going to schedule any pod on it. Existing pods will be migrated to other available nodes in the cluster. Here we have only one worker node so we need to use ‘–ignore-daemonsets’ option to drain the node.

9. taint

It is used to taint a node. Node affinity is used to attract a pod to schedule on the specific node whereas taint is used to repel a set of pods to not schedule on the node. It is used for dedicated nodes like nodes that are dedicated to a specific group of users or if nodes have special hardware like nodes with GPUs or to evict the node as per taint. It uses key and value with taint effect NoSchedule. In this case, no pods will be scheduled on those nodes other than pods having tolerations.



kubectl taint nodes node01 key=value:NoSchedule

10. version

It is used to check the client and server version of the Kubernetes cluster.


kubectl version


Kubectl has multiple commands and some of them are self-explanatory and some of them are not used in day to day management of the Kubernetes cluster. Here, we have discussed the most important and daily used commands to manage and troubleshoot our Kubernetes cluster.

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We hope that this EDUCBA information on “Kubernetes Kubectl” was beneficial to you. You can view EDUCBA’s recommended articles for more information.

Overview And Alternatives Of Cloudera Competitors

Introduction to Cloudera Competitors

Cloudera Competitors does mean comparing competitors in various categories such as contracting and evaluation, deployment and integration, service and support, and specific product capabilities. Cloudera is not the only platform for Big Data Integrations. This Big Data Integration platform is one of the widely used technologies. Most people seek secure and top-rated software solutions with machine scaling, Hadoop integrations, and Cloud processing. Other factors also must be considered when looking for alternatives or competitors, including analytics and features.

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Overview of Cloudera Competitors

Based on the user review data and current and previous users’ data from industries like IT and Services. We shall find the best Competitor for Cloudera for telecommunications and computer software. One of the best Cloudera Data Platforms as an Alternative for Smaller Businesses is Google BigQuery. Alternative for Medium-Sized Companies is the Cloudera Enterprise Data Hub, Amazon Redshift, Google BigQuery, IBM Db2, Azure SQL, Apache HIVE, and Treasure Data. And the best for Enterprises are Google BigQuery, Enterprise Data Hub, Amazon Redshift, Exadata, SAP BW/4HANA, Treasure Data, Oracle Data Warehouse, and Oracle’s Autonomous Data Warehouse.

Top Cloudera Competitors Alternatives 1. Google BigQuery

Google BigQuery is a part of the GCP (Google Cloud Platform), Database-As-A-Service that supports querying and rapid enterprise data analysis.

Google BigQuery needs analysis of Big Data in the cloud; it is rapid and queries similar to SQL against the multi-terabyte in seconds.

It is scalable and easy to use; BigQuery gives real-time insights about data.

Compared to all users, Admin access is more accessible, and easier to do business with IT.

2. Snowflake

It is a database management tool that assists with data warehousing, analytics, data lakes, etc.

Snowflake has made it easier to understand and learn. And the ability to handle several columns is at its best.

Snowflake has enabled a new architecture based on rapid and budget-friendly performance.

It develops a complete experience across multiple public clouds.

The firm uses various business tools to pull data, analyze, and report, allowing users to spend as per the data growth.

3. Databricks Lakehouse Platform

Databricks are wealthy in detailed documentation, working sample codes, community forums, working example notebooks, Q&A, Support, and well-written blogs that are accessible to empower users and be productive.

Notebook and UI provide first-class experience in transforming and visualizing data at any scale.

It allows users to explore potential valuable features for the task without much effort in context switching to access datasets.

4. Qubole

It delivers Self Service Platform for Big data analytics built on Microsoft, Amazon, and Google Clouds.

It is a highly available platform that supports data science and data engineering use cases.

Qubole also provides several cost optimization opportunities that ensure low-cost data processing.

It requires less employee training for users, and hence marketing the data product at a faster pace.

Qubole is a consistent, highly available, scalable, and reliable data service.

5. Amazon Redshift

It is a rapid and fully managed data warehouse that makes it simple and cost-effective to analyze the data using standard SQL and existing Business Intelligence Tools.

It is a petabyte-scale data processing and a cloud-based, fully manageable data warehouse service.

It also enables users to expand data warehouse queries to the data lake. It works on massively parallel processing techniques that will allow it to provide faster performance.

It also collaborates with Amazon Web Services S3, which allows one to query against an exabyte of data stored in S3.

Redshift is fault-tolerant, i.e., it takes a backup of users’ data in S3 so that users have the data easily available.

6. Confluent

Confluent is a cloud-native stream data platform that is better in case of support and is easier for admin access.

Confluent enables businesses to process data streams with open-source technology acting as a real-time messaging system.

The confluent platform has been adapted for cases ranging from collecting user activity logs, data, application metrics, device instrumentation, and stock ticker data.

7. IBM Db2

It has been optimized to deliver industry-led performance, which can lower costs.

It has been provided with built-in utilities to make DBAs work easier and with good customer support.

Netezza appliance has an event configuration feature that sends notifications to hardware components for timely replacement.

8. Microsoft SQL Server

Microsoft SQL Server 2023 brings the power of SQL Server to Linux, Windows, and Docker containers for the first time enabling the developers to build applications using their preferred language and the environment.

It has the experience of industry-leading performance and assured security features, transforming users’ businesses with built-in AI and delivering insights where users are with mobile Business Intelligence.


It is a programming, modeling, and simulation tool that MathWorks develops.

10. Azure Databricks

It accelerates innovation by enabling data science with high-performance analytics optimized for Azure.

Azure Databricks is easier to use and set up based on user reviews.


We have seen what Cloudera’s Competitors mean and the overview of all these listed competitors above. They range from Smaller Businesses to Medium-sized companies to Enterprises. We have also listed the top 10 Competitors or the Alternatives to Cloudera above.

Recommended Articles

This is a guide to Cloudera Competitors. Here we discuss the introduction, overview, and top Cloudera competitor’s alternatives. You may also have a look at the following articles to learn more –

Gionee Elife E7 Mini Unboxing, Hands On Review And Overview

Gionee Elife E7 Mini Quick Specs

Display Size: 4.7 Inches touch screen with 720 x 1280 resolution

Processor: 1.7 GHz Mediatek True OctaCore with Mali 450 MP4 GPU


Software Version: Android 4.2 (Jelly Bean) Upgradeable to KitKat

Primary Camera: 13 MP AF Swivel camera with LED flash

Secondary Camera: Same camera doubles as a Selfie Shooter as well

Internal Storage: 16 GB

External Storage: No

Battery: 2200 mAh battery Lithium Ion

Connectivity: 3G, Wi-Fi 802.11 b/g/n, Bluetooth 4.0 with A2DP, aGPS, 3.5mm audio jack, FM Radio

Others: OTG Support – Yes, Dual SIM – Yes (Micro SIM),

Sensors: Accelerometer, gyro, proximity, light,  magnetic

SAR: 0.612 W/Kg @ 1g Head 0.524 W/Kg @ 1g Body

Gionee Elife E7 Mini Unboxing, Full Review, Camera, Price, Gaming, Benchmarks and Overview [Video]

Design, Build and Display

The Unibody design looks quite premium when held. The Elife E7 Mini is about 8.3 mm thick and feels quite slim with well balanced weight. The highlight of the design is the Oppo N1 like Swivel camera which is quite smooth in operation and also houses in call speakers on both sides. The matte finish back cover provides with good build quality.

The5 point multitouch Display is also very good and easily manageable with one hand owing to sweet 4.7 inches of diagonal length. The display flaunts 1280 X 720 pixels with good viewing angles and colors. It is not the sharpest display but works very well with the device. Outdoor visibility is good and auto brightness works well.

Processor and RAM

Inside the nicely built device you will find the prowess of 8 Cortex A7 cores at work in MT6592 Mediatek Octa core chipset backed by only 1 GB RAM. Amigo ROM is not much to our liking and also accountable for slight amount of UI lag, but the device functioned smoothly with third party launchers.

Elife E7 Mini scored 25927 on Antutu and gaming performance was smooth without any issue, even with graphic intensive games. We are not sure if that will hold true after few months of carefree usage because of RAM limitations. The device got heated after around 20 minutes of gaming but not as much to make you uncomfortable. Amigo ROM also comes with an accessible cache cleaner to manage your RAM.

Camera and Internal Storage

The rear 13 MP camera is the primary reason to recommend this phone. The camera works great in day light and artificial light. Macro shots are good as well. Several low light photos were grainy but certainly above average. It can also record good quality full HD and HD videos. Unlike Elife E7, the camera app does not have a pro mode, but that’s not something we are missing on this device. The Camera app fires whenever you swivel the camera module and this is something we like. The camera will be ideal for those who intend to take lots of selfies.

User Interface and Battery

Gionee Elife E7 Mini comes with Android 4.2 jelly bean based Amigo ROM out of the box but you can update it to 4.4.2 KitKat by taking your device to the service center. There is gesture support, a good SIM card manager and an App cache cleaner. Continuous input is not supported on default keyboard, but since we have several efficient third party keyboards, it won’t be a problem. You can specify apps which you wan’t to run on boot up which can be used to save significant amount of battery.

Battery capacity is better than what we initially expected. The device can comfortably last one day with moderate usage. If you engage with graphic intensive gaming and other more demanding apps you can get around 5 hours of usage.

Sound, Video Playback and Connectivity

Loudspeaker has average loudness, nothing extraordinary. The handset which comes in the box are good in quality but bass levels are not too great. You can play HD and Full HD videos on this device but it does gets heated up after around 30 minutes of viewing HD video files. GPS locking is fast, which is not something we get to say very often for MediaTek devices.

Gionee Elife E7 Mini Photo Gallery

Conclusion and Price

Gionee Elife E7 Mini is selling for around 17,000 INR which sounds like a good deal for this device. In case you have already given up on chasing after Xiaomi Mi3, you could consider it as a good camera smartphone with powerful CPU, Good display and not so great software.

Power Automate Logical Functions: An Overview

In this tutorial, we’ll be looking at some Power Automate Logical Functions that we can use in our workflows. We won’t be needing the Condition connector when using these functions.

Power Automate logical functions compare values and expressions which either return true or false in our flows.

First, we’ll discuss the if function. This is the most common among Power Automate logical functions because it’s similar to the if function in MS Excel or in coding. In MS workflows, this function checks whether an expression is true or false, and based on the result it will return a specified value.  

The first argument of an If statement is an expression. The second one specifies what we want the function to return when the expression is true and when it’s false.

For this one, we’ll be using the manually triggered sample flow that I previously created.

The Condition in this flow is set to check if any of the words in Input 4 contains the word “problem”. If the input from the user meets the condition, the expression becomes true. Hence, the flow will go to the If yes pathway. Otherwise, it’ll go to the If no pathway.

By using the If statement, we can replace the Condition control in our flow. To do that, let’s drag the Post message 2 action.

Then place it in between the Post message action and the Condition block.

Let’s add a comma (,) followed by a space in between a pair of single quote marks (‘’). The space will serve as the content separator of the array items.

Remove the excess closing bracket before the word split.

Let’s add a comma (,), a space, and type in the word “problem” enclosed in single quotation marks (‘). Then add a closing bracket ()), another comma (,), and a space after that.

So, if there is an element containing the word “problem”, it’ll return true. If not, it’ll return false. If it returns true, we’d like the message to be “Input 4 contains the word problem”. To do this, let’s type the text “Input 4 contains the word problem” at the end of the statement.

If it returns false, we’d like the message to be just a period (.). To do this, let’s add a comma (,) right after the closing quotation mark of the word ”problem”. Add a space, then type the period (.) enclosed in single quotation marks (‘).

We can now delete the initial value of the Message Text field.

Let’s also delete the Condition block.

Let’s now test the flow and see how it works.

After that, we’ll see the result of our successful flow run.

Upon checking the general channel in Slack., we’ll see that the message “Input 4 contains the word problem” was displayed successfully.

Therefore, we’ve successfully replaced a Condition control with an If statement which both work exactly the same.

There are 3 other logical comparison functions we’ll go through which are also heavily used—the and, equals, and or functions.

Let’s check the equals function. 

This function is fairly simple and we’ve already used this in previous tutorials. It evaluates two objects which can either be strings, integers, collections, arrays, or others. If the values of the two objects are equal, it’ll return true; and if they’re not, it’ll return false. An example is provided in the documentation as well.

Next is the and function.

This function can be easily used if we have multiple expressions to evaluate. We just put in multiple expressions and they’ll return true if every single expression is true.

For example, let’s go and edit our flow.

We’ll set the condition to “Input 4 is equal to problem”. 

In the additional row, we’ll set the condition to “Input 4 is equal to issue”.

Whatever we input here won’t work because we need to use the Input 4 variable (a dynamic content) instead of just typing Input 4 as strings. But this is only to show you how it looks, which is similar to using the and function in an expression.

In this example, the flow is going to check if the Input 4 is equal to “problem” and if it’s also equal to “issue”. Hence, if both expressions return true, it’ll go down the If yes pathway.

The last one we’ll look at is the or function.

The or function is very similar to the and function. The big difference is that for this function to return true, only one of the expressions needs to be true.

Then, we’ll see that the or function was used in this previous flow. In this example, it evaluates if the tweet contains the word “issue”, or “problem”. If one of these two conditions is true, it’ll perform the action within the If yes pathway which creates a Trello card. If both are false, it’ll do the action that we set for the If no pathway.

Logical comparison functions are very similar to the Condition connector in Microsoft Power Automate. However, I still recommend using the Condition connector over Logical comparison functions because it’s much easier to use in flow diagrams.

If you’d like to use the logical functions, that’s totally fine especially if you have a coding background. In addition, most Logical comparison functions can be replicated using Condition control connectors instead of using codes.

But of course, it’s still totally up to you on how you’d configure and run your flow. Just keep in mind that if your goal is to become a certified Power Automate Administrator, you should know these functions as well.

All the best,


How To Get More Insight From Your Analytics Software

But using these analytics programs – to their fullest extent – is still an emerging discipline. As important as their insights are, actually gleaning those insights requires surmounting several challenges. These include everything from lack of training to inability to formulate an effective query. 

To provide insight on better strategies for using analytics, I spoke with Sarah Gates, Global Product Marketing Manager, SAS

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See transcribed highlights below.

Gates: “I think what you’re seeing right now, is that organizations have been spending a lot of money, a lot of time, getting their hands around their data, building lots of models, leveraging their data scientists, being very, very creative. But where they’re really stuck is: how do we get those models into production, where they’re going to deliver business value?

“So the way I look at analytics is, it’s a continuum. Really, the industry has continued to evolve from the most descriptive basic, ‘What is’ type of analysis to…the other end of the extreme, where you’re talking about artificial intelligence, deep learning computer vision, those leading edge algorithms. It’s all part of that continuum where you’re getting more sophisticated, you need more complex data, or you’re answering tougher questions.

“Most people are focusing on machine learning when they talk about artificial intelligence. That is by far the most predominant use of artificial intelligence today. But there are many other types of techniques, such as computer vision, which is incredibly useful in say, a manufacturing space, text analytics, which can allow you to look at unstructured text data, and get insights out of that, and have models built on that.


Gates: “As you try to scale that beyond one or two models, you really have to have processes in place that help you standardize and automate the process of going from model creation to model deployment. And that gets complicated, especially when you have multiple languages in play. So let’s say you have users building models in Python, and building them in R, and using commercial software, like SAS. Those models then have to be translated into the language that they’re going to be deployed in. And if you’ve got lots of different languages that can make it more complicated unless you have tools that help you do that.

“The other problem that they have is: turnover in data scientists remains very, very high, the tenure is still not much over a year. So again, if you don’t have those systems and processes in place, you’re gonna have this great model, that Joe built, and Joe left, and we don’t know what to do with it and what it means.

“One of the biggest things that we see, is you bring all those factors together, and it’s a common challenge that even the application development community faced about 10 years ago. They had this problem of getting their applications into production in an iterative agile way and they developed this process called DevOps. It’s the practice around: how do you efficiently do that, breaking down silos, handing what the developers create over to the operations teams in a way that they can quickly leverage it. And then put it into production and test it.

“We see that what organizations are starting to think about now is that there needs to be something similar for analytics, there needs to evolve a practice…called ModelOps, sometimes you see it as MLOps, AIOps, DeepOps, the term is still varying across the industry but we use the term ModelOps. And it’s really about how do we change that culture, the practices, our procedures and have the enabling technology in place that allows that to happen effectively, and in a repeatable process at scale.

“So the lack of that is probably one of the biggest challenges facing organizations today. And it’s interesting, a lot of the statistics that are out there, you hear about anywhere, 50% approximately, depending on the study of models never make it into production. They just get built, and they never go anywhere.

“And then in a study we did last year, we found that it took over 90% of models that were put into production took over three months — and over 40% took more than seven months. You think about that latency, especially with more techniques leveraging fast moving data that may change frequently, you could end up in a situation where we’ve deployed the model – ‘Oh it’s no longer performing and we’ve gotta start all over again.’”


Gates: “I’ve got three top guidelines I think would be really helpful.

“So first, as we’ve been talking about getting models into production, focus on: how do you put in place that culture, the processes, the enabling technology that allows you to shorten that cycle of going from data to decision.

“Look at that funnel of models that are being built, how do you eliminate that pinch point at the bottom? And there’s a lot of reasons why that pinch point exists. And so you need to be thinking about, are they too complicated? Can we not make the data transformation? There are too many pieces having to be re-coded – how do we get rid of those problems?

“Second, ensure that the models that are being developed by your data scientists are focused on your high priority needs. So those decisions that will have the largest ROI on your organization and places that you’re ready to incorporate analytics into your business process.

“Because if you aren’t using the analytics in the decision process, you’re not getting any value. So ensure that the work that is being done is not just on my favorite project or something that isn’t ready for analytics, focus it on high-priority projects. And then related to that is, look at how you’re going to embed the analytics into a decision-making process, is it either going to augment or automate a decision process?

“And what that does is it allows you to maximize the return by driving the best possible decision every time. And think about it, I just mentioned augmenting and automating; augmenting – just to give a definition – would be where you’re taking insights from that model and they’re being served up to a person who will make the decision ultimately taking that into consideration, and a great example would be a call center. So I call in, and I’m complaining about my cable service, like I do on a regular basis.

“And they know, based on the analytic score they’re getting back that ‘Sarah is probably not gonna defect, she just likes to complain.’ So they’ll do something to make me happy, but they’re not gonna give me this great offer to retain me because they know I’m not going anywhere. But a human made the decision.

“An automated decision would be, say, loan approval processes where you can fully automate it based on the data that you have about that person, and you can then serve up a Yes or No approve, or deny. Or here’s the interest rate or the terms based on your analytics, completely independently, and that allows you to shorten your cycle time down.


Gates: “Again, three key things that I’d like to point out to be thinking about.

“Data is going to continue to evolve, that’s the first one. You’re seeing more and more streaming data, that’s high volume, high speed coming off of sensors, that’s gonna continue to evolve, whether it’s video, or photos, or sound, or whatever it will be. How are you going to capture that? How are you going to store that? How are you going to prepare it for analytics? Do you want it in the cloud? Do you want it on-premise? What are the implications of those decisions?

“So, I think that that’s a key one, because data is the fuel for all analytics. So continuing to be aware of that and preparing your organization for how you wanna do that. Data privacy comes into play as well on that, because you need to ensure that you’re keeping data appropriately secured.

I think another key is that we’re just at a tip of the iceberg with artificial intelligence. It’s a hugely hyped term. Everybody wants to be doing AI, just like five years ago, everybody used, ‘I’m doing Big Data.’

“So the way to prepare for that is just like with any analytics and some of the other things we talked about earlier, prepare about: where should we apply it? What governance do we need to have in place around our data, around our analytics, around our decisions to ensure that the results are trusted, that we are not including bias into the results that we’re getting about it.

“How do we understand what these models mean? AI models are very complex, just the concept neural network. They’re so much harder than a basic regression model to understand what is really happening. So how do we ensure that we can explain what we’re doing, whether it’s to regulators, to citizens, to customers, to us employees so that we can trust the results. And ensure that we’re not gonna have a problem down the road.

“I think the third thing that organizations need to be thinking about is, most of them are going through a digital transformation of some sort. And analytically, the concept of analytically driven decision-making is a key enabler of that. That’s the only way you’re going to differentiate yourself to be able to move at that speed of human as opposed to speed of organization, which is where so many organizations are today.

“So think about: how are we going to build out the ability to rapidly deploy analytics into our decision-making processes, measure their efficacy, improve upon them, continue to add new analytic capabilities. It takes some infrastructure, it takes some thinking, but it’s definitely where they’re going to have to go as they want to be successful in their digital transformation.

“So those are the three things, data, think about your AI strategy and your decisions. How are you evolving your decision-making as part of your digital transformation? That’s where I think they should look.”

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