Month: May 2022

Best Practices for Putting Your Machine Learning Project into…

The most difficult part of any machine learning project is putting that machine learning model into production. Without the ability to deploy a model, you have essentially failed. The entire point of machine learning is putting your model into a production environment where it will be used effectively. However, a big question mark is how to deploy machine learning in a way that is effective and quick.

You don’t want to spend time and effort deploying your machine learning model in a way that isn’t conducive to your success. Because of that, there are a few ways to deploy machine learning model technologies that can be decisive in your success.

Build with Containers Connected to an API

Containerization has been a massive trend in software engineering. It is now a major trend in ML deployment. One of the easiest ways to build your app is to use containers. It is quite robust, but it suffers the downside of being very complicated to manage. You essentially connect each container to an API, making it possible to communicate with the other containers in your app. Your machine learning project is then easy to configure and operate on the back end and front end.

That makes it possible to scale quickly, meaning your machine learning model will be able to work on much more data. However, the main downside of being able to deploy machine learning model technologies like this is how long it takes to develop the API. API development is quite challenging, and you might not be able to deploy machine learning for a long time. You would spend a lot of time focusing on your infrastructure without any results.

Python File That Gets Triggered

Another easy way to deploy your model is to use Python and SQL together. You essentially connect your SQL to a batch file that is run every time new data is added. The Python code can then train or retrain the model as needed. You can then use Python code to transmit that updated data to your web application. However, it is quite difficult to work with because you need to have a good environment at all times.

You also have security concerns because you need to put your SQL login information inside the Python file. If your server were to get hacked, everything inside your database would be leaked. That is an obvious problem that makes the solution less than optimal for environments where security is a major concern when building a machine learning project.

Embed Python In the SQL Itself

The final thing you can do is to embed the Python inside SQL itself. The SQL code can run an embedded Python script that trains or retrain the model as needed. You don’t have to deal with containers or APIs, but it can be quite challenging. You’ll be able to do certain things you could not do before, but it offers plenty of security downsides. The major one is the fact that remote code execution will need to be enabled.

You also don’t have access to certain things because the Python environment is only going to be basic. Because of that, you will have to use machine learning project services to build your machine learning model that is built into SQL Server. This can be a problem going forward. However, the variety gives you many options in choosing the best way to build out your project.

How Feature Stores Help Your Data Science Projects

Despite machine learning algorithms being an excellent way to improve applications, companies are still struggling to put things into production. MLOps has grown in recent years, enabling companies to do a good job, but there is still a long way to go. Putting a model into production is still the most difficult struggle in data science.

Data innovations like feature stores are becoming a vital piece of the puzzle to enable your data science projects to scale to the next level. As the name says, a feature store is a database of features that allow you to rapidly work with data instead of creating it yourself all the time. It makes it possible to create accurate models more quickly without having to rely on features being created manually.

Feature stores have traditionally been the purview of massive corporations like Google, Netflix, and Facebook. These companies have the resources and manpower to create a large catalog of feature stores. However, the use of MLOps is making the process easier for everyone in this field. Eventually, we might see feature stores be a ubiquitous part of the model engineering process.

What Are Feature Stores?

Features are what make your machine learning model more accurate. You can think of them as the variables you manipulate to help your algorithm learn and make predictions. They are specific to data, meaning you have to calculate them quite often. However, you can also create a database of these features to enable you to calculate and manipulate data more quickly.

It enables you to create an accurate model without all the waiting that comes from having to calculate these features manually all the time. If you have to create features online, you need the plaintiff processing power and fast memory-based database systems. Because of that, feature stores are typically out of reach for most of the machine learning practitioners in the industry.

Reasons to Have a Feature Store

A feature store makes it easy for you to develop your model quickly. On top of that, your model is going to be a lot more accurate, meaning you can get real tangible results from your machine learning initiatives. The other great benefit of these feature stores is the fact that you will be able to transition to production much more easily.

Since you have feature stores available, it is all about taking that and putting it into production. Operationalizing your machine learning becomes a snap, and you don’t have the problems that other people in data science would.

Feature Stores in the MLOps World

The good news about feature stores is that MLOps is making it a lot easier to produce them. This benefit enables companies in data science to save time and effort instead of rebuilding features for different models. Automating how you work with a feature is one of the biggest innovations that MLOps brings to the table. A growing number of projects in the machine learning and data science industry are completely transforming the way we look at how feature stores and model development work.

Improve Performance in Your AI and ML Projects

The majority of AI ML projects fail. In fact, over 85% never make it to production. This means the overwhelming majority of companies are pouring money into failing prospects. Companies need a better way to make data science projects work. There has to be a better data science process that transforms the way machine learning models are built and operationalized. If that doesn’t change, we will see data science continue to be a niche prospect that only a few massive corporations can actually be successful in.

These corporations will continue to be ahead of their peers, meaning that most companies will never be able to catch up as time goes on. The best thing that companies can do in the data science world is to start changing how they deploy AI ML projects. This change will enable them to completely improve the results they are getting.

The Product-first Mindset

The main problem with AI ML projects is the focus on the laboratory environment instead of operationalization. Data science projects are useless when they stay in the lab. However, the focus during the development is on the data science process instead of improving how you put things into production. Data science in production is a completely different beast, and you need to consider other factors when deploying your models.

The companies that will be the most successful are the ones focusing on operationalization and making data science projects work in a production environment instead of the lab.

Use a Better Platform

Another source of problem for bringing AI ML projects to production is that companies try to do everything themselves. Data science is rapidly innovating, meaning there are now many platforms like xpresso.ai that can help you bring your models to production easily.

Companies that try to do everything in the data science process by themselves end up failing. They spend most of their resources on the infrastructure instead of the actual model. By doing this, you guarantee that your data science projects will never see the light of day. A better choice is to focus on platforms that will help you automate all aspects of the data science process. This means you can focus on building effective models that do what you want. It also means you don’t have to spend time and effort on things that don’t give you a return.

Other Ways to Improve

The best thing you can do is to focus your time and effort on finding MLOps platforms like xpresso.ai that can do a good job. These platforms will enable you to find success, meaning your data science projects become easier to put into production. The major stumbling blocks in developing machine learning algorithms can be automated, and you then focus on fine-tuning your model to give it the best performance possible. These platforms will also enable you to maintain and monitor your model in production. This drastically shortens the development cycle and makes life as a machine learning engineer easier.

Things Keeping AI from Being Adopted

Despite the benefits of using AI models inside your application, AI adoption seems to not be as widespread as it could be. That is because it is incredibly difficult to make AI adoption in the enterprise work well. That needs to be overcome to facilitate rapid AI adoption. If you can get your organization to build AI models really well, you will put yourself ahead of the competition. It will enable you to do more inside your organization to get the benefits you are looking for.

It is also going to enable your company to outcompete all others around you. There are a few key issues that have plagued most corporations, but these are getting better with time. The main issue is that your organization needs to be persistent in figuring out easy solutions to the common problems that most companies face in AI.

Finding Skilled People

The first major issue for overcoming the lack of AI adoption is the people problem. It is incredibly difficult to find people skilled in developing great AI models. In fact, there is a shortage of skilled professionals in this arena. There are some ways that smart companies are getting around this problem, and you will have to do the same things to make it work as well.

One of those solutions is to have an integrated platform that makes developing AI models a lot easier. These platforms make enterprise AI adoption easy and cheap. You no longer need to worry about hiring engineers that cost more than $100,000 a year. You also don’t need to worry about the competition you will face from companies trying to pry those engineers from you.

Not Having a Good Process

Just because you have the right people, it doesn’t mean that you will be successful in building AI models. You still need to have a good system in place to enable those people to make enterprise AI adoption a reality. AI adoption in the enterprise requires everybody to be on board to support it. You need people to have a good understanding of what is required, and your leaders need to want to empower engineers to do a good job.

This is a major problem for a lot of companies, and it is why they fail to get the results they are looking for. For example, if your managers don’t believe in the mission, you will not have success with AI adoption. This is something that requires everyone in the organization to believe and trust.

Problems with the Organization

Your organization should focus on building from within. The team you have is already your best resource for creating AI models and facilitating AI adoption in the enterprise. You can train the people you already have to be better in this area, and you can also use off-the-shelf solutions to make the process more simple.

For example, you can use xpresso.ai to act as an end-to-end solution in all aspects of developing AI models. With tools like this, your team doesn’t need to be as experienced, and you can save a lot of time and money improving your organization from within. It will drive AI adoption, making your company one of the best in this arena.

Operationalize Deep Learning with Less Work

A major problem with machine learning is moving from the laboratory environment to production. This is especially true for deep learning. Turning deep learning models into functional units that add value to a program is more difficult than what is needed when operationalizing AI. Doing the traditional MLOps things like a feature store and more can add plenty of value to your project.

There are a few things you need to do that are specific to deep learning models. When you do them well, you can successfully use MLOps to deliver excellent deep learning applications that benefit your company. By following a standard system, you streamline the entire process, making it faster and cheaper. You also don’t require anything extra to get these benefits. This is one of the many reasons why understanding how deep learning works is such a vital thing. Deep learning has the potential to completely transform many industries when fully integrated. However, companies still need to master the intricacies that are specific to deep learning.

Moving Out of the Lab to a Production Environment

In the laboratory environment, deep learning models are not that difficult to get working well. However, it is a completely different beast when you put that deep learning model into production. Something as simple as a feature store is quite difficult to get to work in production, as there are many other operational considerations you need to account for. For example, operationalizing AI is difficult, but it doesn’t require as much computing power as deep learning does.

Deep learning is computationally intensive, meaning that you have an added element to worry about. That is where precise monitoring is such a crucial component when working with these types of models. You need to understand how to take these things and make them work in a way they were never intended to. MLOps becomes crucial when it comes to working in this environment.

Making the DL Pipeline Work Well

The first thing that needs to be done is to have a great pipeline for orchestration. By automating the orchestration process, you will be able to save time and effort when developing deep learning models. For example, MLOps tools allow you to maintain versions of models, automate the packaging process, automate some data cleaning, and monitor your services when they are in production. In fact, this is where a significant portion of the problems come in.

When you try to deploy machine learning models, it is often a difficult prospect because you need to automate significant portions of running it in production. You need to scale your machine learning models, which is why operationalizing AI is so difficult in production environments.

Steps to Take

There are certain things you can do to make it better, and this is where having effective tools come into play. Your machine learning models will not develop themselves, but they can be significantly improved with the right set of tools to automate everything. These tools will enable you to do things more efficiently and quickly. They will also ensure that your deep learning models are built well, and you will be able to scale and iterate on everything you do. A tool like xpresso.ai has everything you need in one to build deep learning models that are accurate and useful.

Transform Your Machine Learning Projects with MLOps

MLOps has been a revolution in how we approach machine learning projects. However, you need an integrated platform to ensure that you have all the tools to put machine learning into production. MLOps is a great addition, but the entire machine learning lifecycle needs a lot more automation if projects are to go the way you want.

This is why having a platform with a robust offering can be such a godsend for your projects. That platform will then allow you to automate significant portions of the process, and it is going to guarantee your success. It will radically transform the way you look at building machine learning projects, and it can even help you deliver things more quickly.

An Integrated ML Experience

MLOps performs the same functions in the machine learning world that DevOps does in software engineering. It facilitates the automation of so many different functions that make machine learning projects work. A good MLOps solution will completely transform the way you look at how machine learning works. The machine learning lifecycle becomes a snap, and you can streamline the way you work with machine learning models.

Many companies fail to put machine learning into production because it is such a complicated thing to do. They don’t have the necessary skills and technical know-how to do it, but they also suffer from structural issues that cause them to fail. Putting a machine learning model into production isn’t where your project ends. You also have to maintain your model to ensure that it is accurate and up to date.

MLOps for Compliance and Governance

Ethical AI will be the next big thing because it is critical to our modern society. We see ML algorithms being used to decide whether you get loans and do other critical functions in our society. MLOps makes it easy for you to put machine learning into production, meaning that your compliance and governance directives will be more successful. As machine learning models increase in our society, we will need access to the best MLOps workflows possible to ensure that your company stays an of the curve.

It will eventually become too expensive not to comply with the latest rules and regulations. In fact, you might completely lose your business if you don’t do things correctly as time goes on. That is one of the many reasons why MLOps is becoming so viable. It allows you to monitor how your machine learning model works, so you can then ensure that it makes things fair for everyone.

How MLOps Helps with the Future

MLOps is crucial to the future of machine learning. Companies will be able to use MLOps to improve the machine learning lifecycle. Instead of focusing on your infrastructure, you will start to think about the business problems you are trying to solve using machine learning models. It will lead to you deploying models faster, making it easy to improve accuracy and iterate on what you’re doing to get the maximum ROI possible. It is also going to improve your ability to gain the trust of the people who will be impacted by your machine learning models.

AI Improves Customer Analytics

Companies understand how crucial it is to use customer analytics to provide real value for their initiatives. However, there are so many issues that have yet to be solved. That is why these companies are turning toward using AI customer analytics to deliver more value. AI has revolutionized many other parts of our world, and the retail space is no different. These companies collect mountains of data, but they don’t have the necessary analytical capacity to turn that data into real insights.

AI is primed to help with that transformation to enable companies to make better decisions that are backed up by cutting-edge algorithms that are known to work. Only time will tell how customer journey analytics and other features will come together to make this AI-based revolution possible.

Optimize Inventory

The big thing that many retail companies struggle with is inventory selection. It is quite difficult to optimize your inventory in real-time, as you never know what customers will want. This is where having access to customer analytics AI solutions can come into play. These AI customer analytics solutions are really useful, as they allow you to turn your data into useful insights that will completely transform how you feel about business inventory optimization.

It is going to enable you to spend less money while profiting more. The reason that these solutions are so great is that you are now able to use AI algorithms to accurately predict what you need to stock to get the most sales. It is quite a remarkable achievement to be able to do this, but the current algorithms in the marketplace are primed to do a good job at improving in this department.

Identify Future Products to Sell

Another benefit of AI customer analytics is figuring out what products to sell to people in the future. The current capabilities of various customer analytics solutions are lacking. They don’t give companies the insights needed to make smart decisions for the future.

AI can give you more accurate information, meaning you get more out of your data. As it currently stands, data is wasted, and companies don’t seem to understand what they need to do to get awesome results. That is completely changing with the advent of the many uses that AI algorithms bring to the table. By using AI, you can analyze the current situation and know what products your customers will want before they do.

Help Improve Profitability

Artificial intelligence will help your bottom line. By improving customer analytics, you will be able to find those problem areas that have kept you from maximum profitability. You’ll be able to put your data to use, and it is going to help with your customer journey analytics. You will easily be able to see where you can influence customers to purchase more from you. You will also be able to understand the value you get from them, making it possible to get even more things done as well. It isn’t always easy in the retail space, but AI technologies make it easy for outlets to reach their full potential.