Trending December 2023 # Is Mlops Another Redundant Terminology? # Suggested January 2024 # Top 14 Popular

You are reading the article Is Mlops Another Redundant Terminology? updated in December 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 Is Mlops Another Redundant Terminology?

This article was published as a part of the Data Science Blogathon.


MLOps? Many persons have barely finished digesting the meaning of DevOps, and here come a new term, MLOps. But, those who understand the m hat MLOps is, but you know the meaning of machine learning and DevOps, then just join the two meanings together, and you are good to go. People who will have issues understanding the term MLOps will be those who do not initially grasp the concept of DevOps. Here in this article, I will walk through the idea of MLOps so that you won’t necessarily need the knowledge of DevOps.

Sincerely sometimes, I think the terminologies in tech are becoming too much. Maybe it is because the sphere of tech is artificial, so things easily multiply and get complex compared to the natural world, where even a little mutation must take billions of years. Sometimes I feel maybe if all the terminologies are collected and organized, some will not be necessary as they will be redundant. Tech is a market, so every brand is trying to be unique, causing a swamp of terminologies.

What is MLOps?

MLOps is a short term for Machine Learning Operations. Just see it literally. It is simply the activities (operations) involved in machine learning, except it is carefully designed to meet industry standards more efficiently. It creates a standard method for developing ML models from start to finish to deployment and maintenance. The core reason for MLOps is integration. To not just build models but have a collaboration between ML practitioners and the general tech world.

This union between ML practice and the outside world becomes a very useful approach for creating machine learning and AI solutions. It also now allows data scientists and machine learning engineers to collaborate and improve the way new models are developed and old ones are maintained.

Azure ML + Azure DevOps manages your datasets, experiments, models, and ML-infused applications.

The guidelines and best practices presented in MLOps help provide an atmosphere that improves a usual machine learning life cycle systematically. Since ML is done to improve human life, there has to be a way to introduce it into various works of life. This is where ML engineers shake hands with IT and various other tech fields. It is just a merging of DevOps and ML.

We can also see MLOps as a division of labor. Instead of the ML engineer worrying about building models from start to deployment and even maintenance and industry demands, he just focuses on ML. He joins hands with ‘Ops’ or the usual DevOps system.

Why MLOps?

We can see Machine Learning systems in group activities, including data collection, processing, feature engineering, labeling, model design, training and optimization, deployment, and maintenance. Just the last two stages alone are where the model finally leaves the experimental environment and touches the purpose it was started. Instead of allowing the ML engineer to do it alone, MLOps assists from here to provide smoother transition and maintenance. Imagine the ML engineer also bothering on version control? MLOps ease it all and shorten the time of development.

MLOps covers various disciplines accordingly, assisting enterprises to have an efficient workflow and avoiding many challenges that come with development. According to research, only a few ML companies have used it effectively.

MLOps Challenges

Machine learning models are striving in a very dynamic space. Variables take many possibilities and forms. The data coming in is continuous and needs more attention than hard coding operations. Although this, comparably little attention has been given to the practice of MLOps. Most beginners are still yet to start learning the concepts, and experts are mostly still trying to adapt, making it a challenge to strengthen this new standard.

Benefits of MLOps

The chief benefits of MLOps are simply developing more efficient, scalable, and controlled ML systems. The promises of ML become easily realistic in this standardized operation. It has created a pipeline that fosters data teams to save development time while achieving high-performing systems. Continuous testing, for instance, even deployed systems can be scaled to automatically meet new demands. Let’s try to pinpoint some more benefits of MLOps.

Version Control

Version control is a usual activity in software systems. The data and models developed will also require versioning in this area too. Introducing new data to the system after a model is deployed could also be beautifully versioned as it is either used as new data or merged with the history. This is can be done via metadata and other means. For some systems merging the old data may make sense and in some, it may not. In both cases, MLOps will provide a way out.

Working as a Team

Instead of allowing the ML engineer to work separately and the other integration space to work separately, the two can work more organized. This prevents many problems and enhances efficiency.

Data and Model Validation

The scientist now has a bigger team to verify and validate the results of models before they are deployed, when they are deployed, and after they are deployed. Validation doesn’t only figure out the truth. It can also bear new possibilities. Even the data presented to ML engineers can properly be validated by the MLOps.

ML Pipelines

Let us see the machine learning pipeline from this point and how MLOps comes in. MLOps sees ML more from the user end how it can be used best in production.

Data Collection and Problem Scope

This involves the first step of ML. The data has to be carefully recognized, not just as data but also based on the scope or problem domain. Apart from just the usual benefit of this step in ML, this can be further improved to save other issues like avoiding data swamps. Though lots of data is required for ML, we now handle this need carefully and avoid other challenges.

Data Verification and Validation

In verification, we check if the data is complete, in a good format, organized, clean, etc.; in validation, we still confirm if it meets the needs of the outside world. These are distinctly important to avoid waste of time.

Feature Specification and Extraction

Instead of just consuming, we can select the features that best fit the goals. Not everything may be needed for the final prediction, clustering, or optimization. Here you may discover the need to clean the data further.

Internal Configuration

A robust system, which is the goal of MLOps, should have efficient communication between its subsystems and external systems.


The codes should also follow best practices. Instead of just coding as a natural ML model, we consider other arts that will also support the goal MLOps. The coding way should facilitate good documentation and automatic testing and integration.

Project Management

here we follow a process of ensuring the ML pipeline achieves all project goals within the given constraints. This is done through project documentation and all other report writings created at the beginning of the development process and throughout the project lifecycle.


Lastly is the monitoring phase. The model is attached with a subsystem for monitoring to ensure enough platform is set to oversee the deployed model and the domain it serves. Below is an instance of a dataset monitor called Shapash.

Shapash Monitor


Key takeaways:

MLOps is a short term for Machine Learning Operations. Just see it literally. It is simply the activities (operations) involved in machine learning, except it is carefully designed to meet industry standards more efficiently.

MLOps covers various disciplines accordingly, assisting enterprises to have an efficient workflow and avoiding many challenges that come with development. According to research, only a few ML companies have used it effectively.

Machine learning models are striving in a very dynamic space. Variables take many possibilities and forms. The data is continuous and needs more attention than hard coding operations.

The chief benefits of MLOps are simply developing more efficient, scalable, and controlled ML systems.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


You're reading Is Mlops Another Redundant Terminology?

How To Fix Skype Error: ‘Your Webcam Is Being Used By Another Application’.

If you are trying to make a voice or video call in Skype but can’t because you are receiving the following error message ‘Your webcam is being used by another application’. This article will show you how to get Skype working normally again so you can make voice and video calls.

How to Fix Quick Access Not Showing Recent Files on Windows 10. (Recent Files Missing From Quick Access)

Skype has been around for a long time now and is still the most well known, reliable, user-friendly voice and video communication service on the Internet. It’s also available on a range of different devices and in a range of different formats. For example, you can use Skype from the Android or iOS app on mobile devices, use the PC client on Windows and macOS or if you prefer, simply use it straight from the web using your favourite browser.

As reliable as Skype is, however, there is one particular issue you may come across when accessing Skype from your Windows 10 PC. ‘Your webcam is being used by another application’. This frustrating error message prevents all voice and video conversations from taking place on Skype until it is addressed. If a simple system Restart hasn’t fixed the issue for you (which I’m sure you’ve already tried). This article will take you through several other troubleshooting steps that will remedy ‘Your webcam is being used by another application’.

Related: How to Enable Blurred Backgrounds in Skype Video Calls.

How Do You Fix ‘Your webcam is being used by another application’ in Skype on Windows 10?

First up, make sure you have Restarted your computer, nine times out of ten this will fix the issue, if it doesn’t, you can try the following. Do a full system antivirus and antimalware scan. It’s possible malware or a virus of some sort is accessing or using your webcam, thus preventing it from being used by Skype. This could be audio only, video only or both. If a scan comes back clean, try some of the other steps below.

Reset the Windows 10 Camera App to Fix ‘Your webcam is being used by another application’ in Skype on Windows 10?

Make Sure Your Camera Has the Correct Permissions.

You should also double check your camera has the correct permissions. Generally if it doesn’t, you will see a different Skype error, however, it’s still worth checking if nothing so far has worked. To do this, open Settings, go to Privacy, select Camera, then flip the Allow Apps to Access Your Camera toggle to On.

Update Your Webcam Drivers.

Uninstall and Reinstall Skype.

If you’ve made it this far down the list without any luck, it’s time to uninstall and reinstall Skype on your computer. Once you have uninstalled Skype run Ccleaner to make sure you removed everything then reinstall it again.

A Side Note…

If you usually like to access Skype using the web version but can’t access it because it’s no longer working with Firefox. The article below will show you how to bypass the block.

How to Fix Skype Web Not Working on Firefox. (Browser Not Supported)

Why Are Ai Use Cases Not Going Live? Mlops Bring An Answer

Over the last years, many organizations have been investing substantially in data and analytics. The objective is to become more data-driven, become a tech style organization. Companies willing to go further than just symbolically profiling the organization, invest in AI to go from descriptive analytics to predictive and prescriptive analytics. This requires a solid data and AI governance program, IT infrastructure that makes all data readily available in a so-called data lake, and piloting of the organization through a carefully selected key performance indicator portfolio. It has been widely documented however that the most important hurdle is the change to a culture that embraces agility and experimentation. In fact, it is the humans that need reskilling. As a consequence, training programs have been launched and large organizations can now boast about their hundreds of use cases created by interdisciplinary teams which are shared on an internal repository for further development and innovation. The hard question comes next: what is the return on these huge investments? Why are so little AI use cases in production and where is the generation of tangible value? There seems to be a gap that needs to be filled and MLOps are bringing part of the answer. Before going into MLOps, let us take one step back. It has always been a brain teaser for the software development community to find the best methodology for project management. It started with the waterfall approach, introduced in the 70’s by Winston Royce. This linear approach defines several steps in the software development lifecycle: requirements, analysis, design, coding, testing, and delivery. Each stage must be finished before starting the next and the clients only see the results at the end of the project.  This methodology creates a “tunnel of development” between gathering the client requirements and the delivery of the project. For many years, this linear approach has been the cause of tremendous loss in resources. An error in the design stage or the clients changing their mind required rebooting the development process. Furthermore, engineering teams were clustered in different stages (developers for coding, QA teams for testing and Sysadmin for delivering) which created frictions a fertile ground for communication errors. This is one of the reasons which led to a new methodology which started around 2001: the agile approach. Agile principles have infused the software engineering culture for more than 20 years. It has endowed companies with the ability to adapt to new information rather than following an immutable plan. In a fast-changing business environment, it is more a question of survival than a simple change of methodology. Now, companies put customer involvement and iteration at the heart of the software development process. They bring together engineers with complementary skills within teams coordinated by product managers to regularly release pieces of software, gather feedback and adapt the roadmap accordingly. This was a true revolution, but it was not perfect: there was still a gap between software development and what happens after the software is released, also known as operations. In 2008, Patrick Debois and Andrew Clay fill this gap with the DevOps (contraction of development and operations) methodology. By bringing all teams (software developers, QA and Sysadmin) together in the development and the operations processes, waiting times are reduced and everyone can work more closely, in order to faster develop better solutions. Back to today, what can bring DevOps today in the era of artificial intelligence? The needs are the same: companies are looking for methodology to develop and scale AI algorithms to generate value and reap the benefits of their investments. Data leaders recently began to investigate the benefits of the Devops methodology. However, machine learning and AI algorithms have a peculiarity that drastically differentiate from traditional software: the data. MLOps is a set of practices, bringing Devops, machine learning and data engineering together to deploy and maintain ML systems in production. This is the missing piece which allows organizations to release the value contained in data using artificial intelligence. With formalization and standardization of processes, MLOps fosters experimentation but also guarantee rapid delivery, to scale machine learning solutions beyond their use case status. Once the solutions are in production and consume new data, monitoring predictive performance is key. Universal outperforming ML solutions for specific solutions simply don’t exist, hence organizations need monitoring predictive performance in real time. MLOps helps monitoring this performance and acts in case deterioration due to concept drift occurs. The automation of the collection of lifecycle information of algorithms, that is tracking what has been recalibrated by whom and why, allows improving the learning process and reporting to auditors if required. Hence, accountability and compliance issues can be addressed. While most data training programs focus on the elements of machine learning, statistics and coding, and work on use cases in a sandbox environment, MLOps principles are not yet covered extensively. Furthermore, business leaders invest in AI without fully understanding how to create an efficient development and operations environment for their data teams. Filling the gap between data and operations is not straightforward. The complexity of ML algorithms, often considered as a black box ran by data scientists who are supposedly the only one in the company to understand what they are doing, separates others from the development process and creates another gap between AI and business. MLOps does not only concern engineers, every stakeholder of data-based solutions should be involved. The revolution of artificial intelligence is undoubtedly happening now, and all those who intend to be part of it will have a role in creating and running MLOps processes in their organization. Future data leaders should acquire basic MLOps skills in their training programs to remove the harmful and unnecessary boundary between business leaders and engineering teams around data-related topics.  


Regis Amichia, Data Science Lead, Foxintelligence

Was The Rtx 3090 Release Another Failure For Nvidia?

As you can see in the image above, UK retailer Overclockers had been slowly increasing prices from September 17th until the September 24th launch. As mentioned in our RTX 3080 verdict, it’s awful for consumers to experience this and there should be some sort of regulation in place to prevent this.

Speaking of price, we speculated in a previous article that, due to the cost of the RTX 3090 graphics cards, there could potentially be more stock available, ultimately meaning more successful purchases could made. How wrong we were… This time round, Nvidia really did fail consumers wanting to get their hands on a new GPU with the Founder’s Edition showing out of stock on their website even before the button to purchase went live. Moreover, there was extremely limited stock on third-party retailers, even more so than the RTX 3080, which was quite an impressive feat actually. Nvidia did put out a statement curbing consumer expectations but, this just isn’t enough:

“Since we built GeForce RTX 3090 for a unique group of users, like the TITAN RTX before it, we want to apologize up front that this will be in limited supply on launch day,”

“We know this is frustrating, and we’re working with our partners to increase the supply in the weeks to come.”

Yes, stock levels are expected to be lower than usual, especially due to the COVID-19 pandemic but, to notify buyers a day before while also gifting a number of graphics cards to YouTubers certainly isn’t a good look.

Now, let’s talk about those pesky bots and scalpers. The whole bots and scalpers fiasco has been a consistent failure across all preorders and releases, not just the RTX 3000 series graphics cards but, with the Xbox Series X, S, and PS5 all failing to counter bots and scalpers from snatching stock from the clutches of genuine customers. So, it was fantastic to see that retailers we’re putting measures in place such as multiple CAPTCHAs for both logging in and checking out as a prevention process. Dont get us wrong, some still slipped through the net, especially on sites that weren’t able to put extra measures in place but it was certainly better from a consumer’s point of view. The major problem was, there was no stock to grab, so it was essentially a moot point for the 3090’s release…

To sum it all up, The RTX 3090 launch followed similar mistakes that the RTX 3080 had but there were some actual improvements. However, it completely failed in one key area – stock levels. The bots and scalpers were at obviously large, trying to scoop up and resell the graphics cards but, it seems retailers actually took note of what happened last time and implemented some changes. It was simply down to what Nvidia call “unprecedented demand” as the reason why 99% of people weren’t able to buy them. With the RTX 3070 being next on the agenda and being a more budget option, we definitely expect the same scenario to occur. It could be different but as always, we won’t be holding our breath.

If you did somehow manage to get your order in on an RTX 3090 then congratulations, you’ve managed to pull of some wizadry! If you want to keep pressing that F5 button refreshing retailer pages, head to our where to buy page where we have all those links for you.

C++ Permutation Of An Array That Has Smaller Values From Another Array

B = [1, 20, 10, 12] Output: 12, 22, 41, 13

Input: A = [2, 5, 9, 7], B = [1, 12, 4, 54] Output: 2 7 5 9

using namespace std; int main(){     int A[] = { 2, 5, 9, 7 };     int B[] = { 1, 12, 4, 54 };     int n = sizeof(A) / sizeof(int);     /***********************We are linking element to its position***********/     for (int i = 0; i < n; i++)         A_pair.push_back({A[i], i});     for (int i = 0; i < n; i++)         B_pair.push_back({B[i], i});     /***********************************************************************/     /*****Sorting our pair vectors********************/     sort(A_pair.begin(), A_pair.end());     sort(B_pair.begin(), B_pair.end());     int i = 0, j = 0, ans[n];     memset(ans, -1, sizeof(ans));     while (i < n && j < n) {                                             ans[B_pair[j].second] = A_pair[i].first;             i++;             j++;         }         else {             remaining.push_back(i);             i++;         }     }     j = 0;     for (int i = 0; i < n; ++i){                         if (ans[i] == -1){             ans[i] = A_pair[remaining[j]].first;             j++;         }     }     for (int i = 0; i < n; i++)         cout << ans[i] << ” “;     return 0; }

Output 2 7 5 9 Explanation of the Above Code

In this approach, we first link all the elements to their indices to still have their old index in it when we sort it. We sort both of the vectors of pairs now we greedily search for our answers as we move through both the arrays if we get an index of A_pair which has more excellent value than of B_pair, so we store that in our an array(and in the position of B_pair) else as we have sorted both the vectors, so we know that we won’t be able to use this value of A_pair, so we push that elements index in our remaining vector now we fill the array by the help of remaining vector, and then we print the answer.


In this tutorial, we solve a problem to find the Permutation of an array with smaller values from another array. We also learned the C++ program for this problem and the complete approach we solved. We can write the same program in other languages such as C, java, python, and other languages. We hope you find this tutorial helpful.

Bloomberg: Apple Silicon Macs To Launch As Part Of Another Apple Event In November

We’ve already had an Apple September event and are currently looking forward to the iPhone 12 / HomePod mini event next week, but Bloomberg reports Apple won’t be stopping yet. In a story today on what to expect from next week’s event, Mark Gurman writes that the first Apple Silicon Macs will be announced at another Apple product launch event in November.

In line with Ming-Chi Kuo’s reporting from a while ago that the first ARM Mac would be a 13-inch MacBook Pro, Bloomberg says that the first Mac laptop with Apple Silicon “among other products” will be launched next month.

Whilst it is historically unprecedented for Apple to hold three media events in the space of three months, this year is different as everything is being done virtually due to the COVID-19 pandemic. Whilst crafting a meticulously produced livestream that is up to Apple’s standards of excellence is no easy task, the typical logistical hassles of inviting press on campus simply do not apply this year.

Earlier this year, Bloomberg said the first Apple ARM chips would feature a 12-core processor based on the A14X design. Apple’s upcoming Macs are expected to best the Intel equivalents in both performance and power efficiency, providing a faster CPU/GPU for Apple’s portables as well as longer battery life.

The rest of today’s Bloomberg report reiterates the general consensus of rumors; the October event will play host to the new iPhone 12 lineup and a smaller version of the HomePod speaker. Bloomberg says Apple could announce the new HomePod alongside AirPods Studio “as early as this month”. The publication does not give a release window for the AirTags trackers.

Jon Prosser today said that the AirTags launch has been delayed into the March 2023 timeframe, and will not feature at the October event.

In a sweeping report from Chinese social media leaker Kang, and corroborated by reliable Twitter leaker l0vetodream, the smaller HomePod will be called ‘HomePod mini’ and priced at $99 each, much lower than the current $299 price for the original HomePod (which originally launched at $349, but Apple dropped the price about a year later).

The Kang report also claimed that Apple will revive the MagSafe brand name with a set of wireless charger accessories designed for the iPhone 12. As far as the iPhone 12 is concerned, the report released a whole host of specs about the forthcoming phones, including pricing and release dates. The iPhone 12 mini will apparently cost $699, the 6.1-inch iPhone 12 comes in at $799, the iPhone 12 Pro will cost $999 and the iPhone 12 Pro Max will be sold starting at $1099.

Supposedly, all four new models will be unveiled at Tuesday’s event but the releases are going to be staggered. The iPhone 12 and iPhone 12 Pro will be available to preorder on October 16 and start shipping on October 23. The iPhone 12 mini will go up for preorder on November 6 and officially launch on November 13. The 6.7-inch iPhone 12 Pro Max will round out the launch with preorders on November 13 and a November 20th release.

Stay tuned to 9to5Mac as we bring full coverage of all of Apple’s fall product announcements.

FTC: We use income earning auto affiliate links. More.

Update the detailed information about Is Mlops Another Redundant Terminology? on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!