Month: August 2021

Taking Advantage of the Innovations Machine Learning Ops (MLOps)…

One of the biggest problems with machine learning projects is that most of them never make it into production. In fact, 87% of these projects, to be precise. The main reason is that there are many engineering and data problems that need to be overcome for ML teams to build a successful project. 

In fact, most ML teams spend most of their time working to build the infrastructure needed to start deploying ML models. This is obviously a bad thing because they waste a lot of time doing something that doesn’t necessarily benefit them. It is for this reason why MLOps exists

What Is MLOps

What is MLOps? It is a set of best practices and tooling that enable companies to streamline machine learning operations. It can be thought of as DevOps for machine learning projects. Obviously, machine learning operations are a lot more complicated than software projects. They involve more than just software code.

You also have multiple different professions working on the same projects, which itself adds a lot of complexity. On top of that, your business executives might not be on the same page as your data team. MLOps solves all these problems by providing the best practices that enable companies to streamline operations and create successful machine learning projects. These best practices also help companies create a streamlined ML pipeline to continuously deliver excellent models to production.

The Innovations Made by MLOps

Machine learning projects are fundamentally different from software ones. In machine learning projects, you have both software code and data. From this data, you need to build complicated ML models. You end up with a CI CD CT DevOps process that isn’t comparable to anything else. The CT stands for continuous training.

The majority of models are not perfect the first time they are deployed. They need continuous training and evaluation to ensure that they meet your business needs. It is one of the many reasons why MLOps is still so complicated. You must develop code and data in parallel, and they can never fall out of sync. You also must have different teams with different skill sets working together on these projects. It makes things a lot more complicated, but it is a lot more efficient than the alternative ways of deploying machine learning models in production.

Understanding the Full ML Pipeline

The full ML pipeline is much more difficult than the DevOps pipeline, but that is due to how complicated ML products can be. Before you can even start anything, you have to look at the business benefits of deploying machine learning to production. You have to then build the data source and training pipeline that is needed to be successful. It means collecting and preparing the data to be used inside your model.

You are also required to train the data, but this is a continuous process. Most companies never get to the end result they want after the first model. You have to continuously evaluate and improve the model as time goes on. You can think of MLOps as a cyclical process that is more complicated than DevOps.

On top of all of this, you also have to build the right team with the various professions needed to come together to make this process work. However, MLOps is a radical shift in the way people build and deploy ML models. It has proven itself to be quite useful, and it will continue to do so for the foreseeable future.

Pick The Right Architect for Your Project

The increasing complexity of enterprise data architecture deployments makes it more important than ever to have an experienced professional who can act as an architect on every project. Modern data projects now require someone who can be the main person everyone else can come to for solutions and insights. The main reason why things are getting so complicated is that modern cloud deployments are making data deployments more difficult. With the cloud, data has to be more distributed, and you require more complex software to work with it. It makes having that central figure more important to our project’s success.

That central figure is crucial, but which is the right one for your specific project? Let’s break down for different types of architects that you can potentially have on your project.

The Traditional Data Architect

These traditional data architects are usually the ones who define how data can be collected, stored, and used. They essentially create the main architecture that will drive your data project. They determine the direction the business goes in when dealing with data projects. They will also be the ones who control access to that data. Data permissions is an even more significant part of the puzzle now that we have massive corporations and distributed cloud environments. They deal with the governance, as laws are now affecting the way we use and consume data today.

As you can see above, the traditional data architect is the central figure in almost everything an organization does concerning data projects.

MLOps Architect

The overwhelming complexity of machine learning architecture design has now reached a place where MLOps makes a lot of sense for most organizations. When you have such a project, a machine learning architect might be the right person to tackle the complexities your deployment has to deal with. Since machine learning projects are cyclical, the architect has to be flexible enough to ensure that the right strategy is being chosen at every phase. They also have to be able to communicate with the various teams within the machine learning project. That is because most machine learning projects are done by data scientists and machine learning engineers along with the various stakeholders and executives in the company. They also have to Institute a data engineering architecture that will scale to wherever the company needs it to go.

Enterprise Architect

The enterprise architect is responsible for laying down a great foundation to manage information inside what is possible for a corporation’s data needs. These architects are usually working hard to ensure that the corporation is compliant with various privacy laws and regulations.

They are also the ones that set the tone that the workers will have to follow. For example, they will have clear policies on how various people in the organization can use and access data. The enterprise architect is responsible for choosing the best architecture possible for the project. They are also there to ensure that the overall project steers clear of anything that could hinder progress.

The Architect Specializing with the Cloud

Cloud computing has made infrastructure a specialized domain within the data science world. You now need an architect that specializes in managing various aspects of whatever cloud you are working on. For example, Amazon has its own specific services and virtual machine instances that you can work with. You need an architect who understands the back-end architecture of whatever cloud platform the organization has decided to use.

The right data management architecture can be the difference between a smooth sailing project or one that stalls completely. These architects are also responsible for monitoring changes in cloud services and ensuring that these services stay up over 99% of the time. While this position isn’t purely about data engineering, it has a massive effect on whether your project will be completed successfully or not.

Data Driven Decision Making: Going On the Offense Is…

With the data being touted as the new oil, it is more important than ever for the chief data officers inside companies to create a data management strategy that is offensive instead of defense. What that means is that the data should be used to drive marketing and sales, which are the lifeblood of every business. Instead of focusing on defensive things like regulations, compliance, and management, companies can leverage data to improve profits and cut costs.

The role of the Chief Data Officer is now to facilitate the transition from defense to offense inside their organization. However, many people don’t understand this new reality, and they continue to languish behind organizations that just get it. Data driven decisions are the answer to the majority of their business problems, but brave CDOs would have to take the risk to make that transition.

How the CDO’s Role Has Changed

The CDO can be thought of as the overarching authority that executes the data strategy for an organization. They are usually responsible for everything that goes into a data driven organization. That has traditionally meant going through the process of managing, warehousing, and ensuring that the data is clean and reliable. They also focus on infrastructure, but this was a lot more difficult back then because the cloud did not exist. Each individual organization had to have its own data warehousing infrastructure and toolkits.

The ability to perform complex calculations in place did not exist, and you could not have data driven decision making. The way that organizations used data was purely as a commodity that could be a secondary facilitator of growth inside the organization. It wasn’t the main thing, and it is why the changes being made today are so sorely needed.

As the chart shows, things will need to change dramatically, as data becomes a more central part of how organizations can make decisions.

The Traditional Defensive Strategy

Data has gotten more important because of the cloud and the ability for almost anyone to get access to almost unlimited computational power. When that is coupled with the massive amounts of data available today, it makes for an amazing strategy in determining how companies can shape their future by harnessing the power that data provides. It makes it a lot easier for companies to create a data management strategy that is both robust and forward-facing.

However, the traditional model focused exclusively on the bad side of data. Instead of seeing data as a crucial resource, they saw it as a resource that needed to be guarded and cared for. You can think of it as data being gold in a vault that requires a lot of security to protect. This defensive strategy was not conducive to success, which is why that strategy is changing so dramatically. The CDO no longer has to focus only on infrastructure, regulation, and compliance. They no longer have to focus on data management and how it is warehoused.

What It Means to Go On the Offense

The modern Chief Data Officer sees data as a valuable resource that can be used to improve businesses relatively quickly. The focus is now on using data to make better decisions that translate into a better and bigger bottom line. It means using machine learning and AI solutions to drive growth and innovation inside an organization.

While managing data is less important in this new paradigm, it is still a critical component. Offensive strategies focus more on taking advantage of real-time opportunities that pop up. It means being flexible and using data as a critical weapon in your arsenal to generate massive amounts of income.

How AI can Improve Customer Experience Using Data Personalization…

One of the challenges of deep learning models is translating them to the AI customer experience. It is crucial to understand how to use these new tools available to us to create a data driven business strategy. Hyper personalization is becoming a crucial component of modern business success.

Your organization needs to learn how AI can improve customer experience in all facets of your business. You can also use AI to create better decisions in determining which road your business will take. The future of this industry will come from organizations understanding how they can leverage deep learning models to deliver on their business goals. You also need to know how to strike a balance between hyper personalization and coming off as the creepy big brother who is secretly spying on the visitor. While you want to give them great personalized options, you don’t want to seem like you are reading their mind.

Know Why They Come to Your Website or Service

It is crucial for your organization to capture as much data as you can from each visitor. That data can be fed in to improve your customer experience. As mentioned above, hyper personalization is becoming a key component of the way online companies work. Deep learning models are a great way of doing this, and you can create yours by harvesting as much data as possible from each visitor.

What is the point of harvesting all that data? The main point is to get an idea of why people are using your website with service. It allows you to make an AI based recommendation system that will help you understand how to influence their buying decisions. When you do things this way, it improves the AI customer experience that your programs can deliver. It also ensures that your business can be based on factual insights that were created by data. The bottom line is that you need to constantly be gathering data on what people are doing on your website.

The Importance of Accurate Assumptions About How People Make Decisions

Your AI based recommendation system should have the right assumptions to be accurate. One of those assumptions has to do with how people make decisions. When you gather data about people visiting your website, you might not factor in how social groups can influence their decisions. It is why being able to group people together into various hierarchies is a crucial component of any hyper personalization model. Deep learning models can be programmed with these assumptions in mind.

Knowledge of basic consumer psychology can improve the way that your business operates. It will enable you to have a data driven business strategy, which is a proven method of improving the results of machine learning and artificial intelligence deployments. Your machine learning models needed also consider which places your visitor is also going online to get advice on what to purchase. This can be broadly thought of as understanding the influencers in their lives.

Make Decisions Based On Data

Ultimately, data-based decision making should be a core part of your business strategy. The best organizations are the ones that understand this reality. They are preparing themselves by hiring the best people and adapting exceptional data-based thinking. They are also building out effective machine learning models that can form the backbone of a greater strategy. Some of the deep learning models available today are also being constantly used and refined to create even better results. As time goes on, these strategies will enable corporations to get the best results possible.

Operationalization(O16N) and Machine Learning Operations (MLOps): What Is The…

The machine learning space is filled with new buzzwords every single day. It might be difficult to get a handle on what these terms actually mean, but fret not; help is on the way. We will show you the differences between MLOps and Operationalization. The difference is subtle, but these terms are sufficiently different to make a difference inside your enterprise AI endeavors. The first thing to note is that these two terms are quite similar in what they define. In fact, you might find that the differences between them are in the level of the organization most likely to use each method. MLOps typically focuses on the technical side and is usually the concern of engineers and scientists. O16N focuses on the managerial and business side.

What is MLOps? Machine learning operations is a broad term that essentially means DevOps for machine learning projects. It is an entire methodology of putting machine learning models into production. This methodology focuses on the tools, and you are likely to see it in the technical people on your team. 

Operationalization mostly has to do with what management and other parts of an organization have to do to put those machine learning models into production. As mentioned above, you can think of operationalization as MLOps from a management perspective.

What Is This New Field of MLOps

You can see from this chart that MLOps is a way of making enterprise AI more like the software engineering field. There are various MLOps tools that are used to make this process in reality. It contains flexible tools that enable organizations to go through the entire pipeline quite seamlessly. The best part of MLOps is that it is mostly focused on the engineering and technical side.

The mathematical and technical sides are the most crucial parts of the process, as they determine how seamlessly an algorithm will be implemented. It is vital that all aspects of a project be managed holistically, but you cannot get things done if your technical people are not knowledgeable enough. There’s also the major factor that data scientists and machine learning engineers have to come together to productionize ML models. What this means is that they have to create the workflow that gets the project from point A to B. There are various MLOps tools that are used to make this a reality. MLOps best practices are also being developed over time, which will come as the industry matures even more.

As you can see below, machine learning operations can be tricky when all things are put into place. It is that technical difference that is apparent in Operationalization and MLOps.

Operationalization: How It Differs From MLOps

Operationalization has many overlaps with MLOps, but there are a few differences that make the difference. The main difference has to do with the focus on the business side of your machine learning operations. For example, what will the business impact be? Answers to these questions are usually answered by the managers and executives in the company. They are usually the ones focusing on a 3,000-foot overview of what is going on. They look at the business impact that your enterprise AI efforts will have. They also look at non-technical aspects like legal and data governance. The managers are also there to ensure that the various people in the organization are working together to achieve the right goal. For example, while MLOps might focus on the performance of the data version control system, O16N will be focused on the hiring resources put into place to facilitate acquiring talented individuals. The CEO is more likely to be involved in the O16N side of things. Things like hiring, the structure of your teams, and how you measure ROI make up O16N.

Conclusion

Now you have gotten a general overview of Operationalization and MLOps. As you can see, there are large overlaps in the various methodologies we are talking about. These overlaps have to do with the layer of a corporation focused on the machine learning operations that are currently happening. To be successful, it is crucial that your organization focus on both aspects. MLOps covers the technical side, and operationalization covers the business and managerial side. When they work together, your organization will find massive success.