- Introduced public and protected modes for deployed service’s URL.
The ability to export a solution from one xpresso instance and
import it to another instance.
- Known Limitation: DataSource import/export is not supported at the moment.
Introduced support for running only some specific components in a
- Known Limitation: Only for Kubeflow runs.
- Introduced support for restarting a failed pipeline run from the component where it initially failed.
- Introduced Visual Studio Code as an online IDE.
- Known Limitation: The current approach isn’t fully secure.
Improved support for multiple deployment clusters in one instance.
Known Limitation: Cluster allotment for old users is a manual step
that will be done by the xpresso.ai team while upgrading
- Known Limitation: Cluster allotment for old users is a manual step that will be done by the xpresso.ai team while upgrading versions.
- For new users, clusters will be allotted at the time of user creation.
- Improvements in ML Components (improved metrics and graph
generation, improved status reporting, etc.)
- Known Limitation: Only SKLearn is done as of now. Keras, LightGBM and XGBoost to follow in the next release.
- Improved the data flow between components.
Accurate claim records can positively impact reserve requirements/loss ratio. This leads to premiums that are more reflective of a payer’s actual experience, rather than a conservative estimate. Knowledge of claims history is powerful information when it comes to negotiating/pricing fair and competitive insurance premiums.
The theory of deploying machine learning models is now well understood. However, not many people understand the nuances needed to do machine learning deployment practically. If you want to deploy machine learning model, you need to understand how to do it in a practical way. That means understanding the Python programming language and the various environments you can use to ensure it works correctly. For example, Flask is one of the many tools you can use to develop APIs when doing machine learning model deployment.
The big thing is to focus on how to deploy your machine learning models. Deploying as an API is quite difficult, which is why you have to be careful when thinking about what to do. However, deploying an API allows you to scale much more quickly than any other way. How to deploy model in production? It all starts with ensuring that your model works correctly. The next step is to decide what you are going to do. Most machine learning model deployment happens with software engineers creating new systems to make it work. However, you can also do everything as an API.
Deciding How to Deploy Your ML Model
The big thing with using an API is it allows you to focus on the architecture instead of on rewriting the model code. Deploying models into production is difficult because there are scientists and machine learning engineers have completely different toolkits. The machine learning engineer is more likely to use Python or some other practical tool to put a model into production.
Machine learning deployment is difficult because you need to either rewrite the code or use an API. Flask is a Python toolkit that allows you to deploy machine learning model code without worrying about changing anything else. You can have your application access your machine learning models as an API call.
Setting Up Your Python Environment
The first step in this process is a create a Python development environment. Anaconda is the virtual environment tool that many people use for Python. This allows you to do machine learning deployment relatively quickly by integrating all of that with Flask.
You can then integrate your typical Python code and add the necessary serialization. As you might recall, when working with APIs, you need to serialize and deserialize when moving things from one place to the next. This allows you to send your API calls as efficiently as possible. This is one of the best ways to ensure you are doing machine learning model deployment as well as possible.
Building Your API and Deploying
The final step in this process is to develop your API using Flask. This is one of the best things for deploying models into production. Machine learning model deployment is easy with the plethora of available tools. However, not everyone might be interested in building and deploying models on their own.
That is why tools like xpresso.ai exist. It essentially gives you everything you need to build and deploy a model successfully without worrying about any other downsides. It is easy to use, and you won’t have to worry about the intricacies that come from making the process work by yourself.
There are few things more difficult than developing machine learning models. Putting an ML model into production is a multistep process, and you need to understand every piece of it to be successful. Many companies don’t know what it takes to develop machine learning models, so they end up having problems. Understanding the ML model lifecycle is crucial for you in many other areas as well. If you are good at this lifecycle, you will be able to develop machine learning models and improve your business results.
The first thing you need to do when building machine learning models is to start with the data acquisition phase. After that, you can start with machine learning model training and machine learning model development. The ML model lifecycle involves taking that data and building a model that can predict things in the future. If you do it well, it becomes a really huge part of your application.
The Model Development Process
The entire ML model lifecycle is quite remarkable. The first step is sourcing the data. If you don’t have proper data, you might need to generate that data by yourself. The way we develop machine learning models isn’t as complicated as you think when you put it into this context. Model development becomes easy because you can then focus on tuning your model based on the data you have. However, it is important to remember that you are putting this model into production.
You have to think about how you will put it into production during this stage of model development. The exploratory data analysis stage is quite important because it will show you whether it is worthwhile to develop this model or not. Sometimes, people find out that it does not make any sense to have an ML model built during this phase.
Data Analysis and Feature Engineering
After you have decided to start building your ML model, the next stage of the ML model lifecycle is exploratory data analysis and feature engineering. Unfortunately, exploratory data analysis usually takes up to 50% of the time needed to develop a model. This is where you find the data and process it down into the features needed to build your ML model.
The model development process isn’t as complicated as it seems, but this is a very difficult step for most people. Feature engineering is essentially the process of building the variables that your ML model will understand. This is how it works when you do machine learning model training. You are then able to adjust the parameters that the features give you.
The last stage is often the most difficult and least understood. Every ML model is useless until it is put into production. This is why you need to understand the machine learning model development process at this phase. If you don’t, you will be one of the many companies that fail to enact machine learning into the application.
You need to understand MLOps because this is a crucial step in managing your ML model. It takes special skills to develop machine learning models, but the deployment step is typically where those skills are tested to the maximum. If you know what you’re doing, you can often then get to the next stage of model development without any issues.
Model deployment is the final step in the process where you put everything you have done to a real test. To deploy ML models requires a deep understanding of the production environment. That is why machine learning deployment is one of the major stumbling blocks when building machine learning models.
Machine learning model deployment might sound easy, but it is usually a major stumbling block that causes many companies to abandon their ML initiatives. These companies never seem to grasp how difficult it is to deploy data science models to production. Many companies don’t even know what does it mean to deploy a machine learning model. However, they quickly learn when they are faced with the prospect of wasting thousands of dollars and many hours on something that failed spectacularly.
The Meaning of Model Deployment
The thing about deploying machine learning models is that many people don’t even know what that means. To deploy a machine learning model means integrating that model into your existing application. You will then feed it inputs, and you will be provided outputs as defined by your use case. For example, you can deploy ML models built for predicting whether a picture features a dog or cat. You would then feed it pictures of dogs or cats, and it will tell you which one it is. You can also feed other pictures, and if it has been well-trained, it will tell you that he has found nothing there.
Deploying ML models is more than this, as you need to worry about other production issues that come into play. Machine learning model deployment also involves thinking about how scalable and portable your application will be. How well can you move it to other systems? These things need to be taken into consideration. You also need to look at the architecture of the system as well.
What an ML System Looks Like
When you deploy model to production, you also have to think about how the architecture fits into what you are trying to achieve. For example, there are various layers that you need to look at when deploying data science models. For example, there is the data layer that is needed to ensure your model works correctly. You also need a layer focusing on your features.
There are also the scoring and evaluation layers, and they help turn predictions and features into answers that will make sense for your use case. Understanding how the system creates a cohesive whole is one of the hallmarks of deploying machine learning models.
Considerations for Model Deployment
The final step is to figure out how to actually deploy your machine learning models. Deploying machine learning models isn’t as easy as you think, as you have to figure out how you will improve and have your machine learning model work in production. For example, do you train your model once? Do you constantly update it periodically? Or maybe you are training and improving your machine learning model in real-time. This is a major thing to think about, as it affects how well you will be able to maintain things. It also affects the performance of your application when you go to deploy ML models in the future.
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.
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.
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.
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.