Automated Machine Learning

Traditionally, the development of predictive solutions is a challenging and time consuming process that requires expert resources in software development, data engineering, and data science. Engineers are required to complete the following tasks in an iterative and cyclical manner.

1. Preprocess, feature engineer, and clean data
2. Select appropriate model
3. Tune Hyperparameters
4. Analyze Results
5. Repeat

As the industry identified the blockers that make the development of machine learning solutions costly, we (as a community) aim to figure out a way to automate the process in an attempt to make it easier and faster to deploy intelligent solutions. Therefore, selecting and tuning models can be automated to make the analysis of results easier for non-expert and expert developers.

Automated machine learning is the ability to have a defined dataset with a specific target feature, and automatically iterate over the dataset with different algorithms and combination of input variables to select the best model. The purpose is to make developing this solutions require less resources, less domain knowledge, and less time.

How it Works

Most Auto ML libraries available are used to solve supervised learning in order to solve specific problems. If you are unfamiliar, there are two main categories of machine learning.

• Supervised Learning: is where you have input variables and output variables, and you apply algorithms to learn the mapping function of input to output.
• Unsupervised Learning: is where you have input variables but no output variables to map them to. The goal is typically to identify trends and patterns in the data to make assumptions.

Note there is a category called semi-supervised learning but we will not get into that here. But it is simply a combination of the two categories above.

In order to use auto machine learning your dataset must be feature engineered. Meaning, you manually develop transformations to create a machine learning dataset to solve your problem. Most Auto ML libraries have built in transformation functions to solve the most popular transformation steps, but in my experience these functions are rarely enough to get data machine learning ready.

Once you have featured engineer your dataset the developer simply needs to determine the type of algorithm they need. Most supervised learning algorithms can be classified as:

• Classification: The output variable is a set number of outcomes. For example, predicting if a customer will return to a store is either a “yes” or a “no”. Classification is additionally broken into multiclassification (3 or more outcomes) and binary classification (2 outcomes).
• Regression: The output is a numeric value. For example, predicting the prices of a car or house.

When given an algorithm type, Auto ML libraries will run iterations over your dataset to determine the best combination features, and best hyperparameters for each algorithm, therefore, in turn it actually trains many models and gives the engineer the best algorithm.
I would like to highlight the differences between having to engineer columns for machine learning, and selecting the appropriate columns for machine learning. For example, lets assume I want to predict how many point of sale transactions will occur every hour of the day. The raw dataset is likely transactional, therefore, will require a developer to summarize the data at the hour level i.e. grouping, summing, and averaging. But often times developers will create custom functions in order to describe the trends in the dataset. This process is feature engineering.

Feature selection comes after feature engineering. I may summarize my dataset with 10 different columns that I believe will be useful, but Auto ML libraries may select the 8 best columns out of the 10.

The difference between feature engineering and feature selection is huge. Most libraries will handle common or simple data engineering processes, however, the majority of the time a data engineer will need to manually create those transformations in order to use Auto ML libraries.

When Auto Machine Learning libraries are used in the development process the output is usually a dataset containing metadata on the training runs and their results. This dataset enables developers to easily choose the best model based off the metrics provided. Being able to choose the best model out of many training iterations with different algorithms and feature columns automatically is that it enables us to easily automate the model selection process for *each* model deployment. With typical machine learning deployments, engineers typically deploy the same algorithm with the same feature columns each time. But with Auto Machine Learning solutions we are able to note only choose the best algorithm, feature combination, and hyper-parameters each time. That means, we can deploy a decision tree model trained on 4 columns one release, the deploy a logistic regression model trained on 5 columns another release without any code edits. This is so simple, yet so awesome about how easy it can be!

Available Libraries

MLBox, a python library for automated machine learning. Key features include distributed processing of data, robust feature selection, accurate hyperparameter tuning, deep learning support, and model interpretation.

TPOT, an automated machine learning python that uses genetic programming to optimizes machine learning pipelines. Similar to other automated machine learning libraries it is built on top of scikit learn.
The AutoML with TPOT is now available.

Auto-sklearn, a python library is great for all the sci-kit learn developers out there. It sits on top of sci-kit learn to automate the hyperparameter and algorithm selection process.

AzureML, an end to end platform for machine learning development and deployment. The library enables faster iterations by manage and tracking experiments, and fully supports most python-based frameworks like PyTorch, TensorFlow, and sci-kit learn. The Auto ML feature is baked into the platform to make it easy to select your model.
The AutoML with AzureML is now available.

Ludwig, a TensorFlow based platform for deep learning solutions was released by Uber to enable users with little coding experience. The developer simply needs to provide a training dataset and a configuration file identifying the features and labels desired.

Check out the libraries above! Automated machine learning is fun to play around with and apply to problems. I will be creating demos and walk throughs of each of these libraries. Once public you will be able to find them on my GitHub.

Anaconda Environments in Visual Studio Code

Over the last several years I have made a transition from developing in R to developing in Python for my data science projects. At first I would use Jupyter Notebooks to develop my solutions, however, I missed R Studio. While I am still a huge fan of the notebooks, especially Databricks Notebooks, I primarily use Visual Studio Code (VS Code). The Python distribution of choice for me is Anaconda, and I really love being able to have different conda environments for my different solutions so that I can avoid dependency conflicts and start fresh with new predictive solutions. The conda environments also make it extremely easy to track your dependencies since you are starting fresh with a new Python interpreter.

One thing that I initially found difficult with VS Code is the ability to create a conda environment and use it as my python interpreter. There is plenty of documentation available but it took me a little while to figure it out since I had to piece together a few different sources. As a reference I am using a Windows 10 Microsoft Surface Laptop for my local development, and I have Anaconda 4.5.12 installed.

Development Environment

To set up your development environment please make sure that you have both Anaconda and Visual Studio Code installed on your machine (links above).Once installed, please open an “Anaconda Command Prompt”.

The following command creates a new conda environment with python 3.7.

conda create -n myenv python=3.7

To activate the new environment run:

conda activate myenv

You have now created and activated an anaconda environment. This means that you have two Python interpreters available on your machine: “base” and “myenv”. To run Python in the “myenv” environment run the code snippet below. Note, to pip or conda install python libraries I will typically just use the Anaconda Command Prompt with my desired environment activated.

python
# the "python" command should enable you to run python code from the command line. 
# Now run the following in order to execute python code

a = 1 + 1
print(a)

Please make note where your python environment is located on your computer. For example, mine are located at “C:\Users\<username>\Anaconda3\envs\myenv\python.exe”.

So far we have only worked with the Anaconda command prompt, lets open up VS Code. There are two VS Code extensions that you will want installed: “Python” and “Anaconda Extension Pack”. Please note that they are both developed by Microsoft.

To install an extension in VS Code navigate to the extension, search for “Python” and “Anaconda Extension Pack”, and install. See below.

We would like to use this new conda environment as our python interpreter in VS Code. Next use the command “CTRL + Shift + P” to open the command palette. Then type “>Python: Select Interpreter”. You will want to paste the file path of the python interpreter we created previously i.e. step 4.

Create a new python script called python_script.py, and paste the following code:

a = 1 + 1
print(a)

To execute this code highlight it or put your cursor on the line, and press “Shift + Enter”.

You are now using a new Anaconda Environment in VS Code!

From Dev to Prod

One advantage of using Anaconda Environments to create solutions is it allows developers to use various versions of python and install packages without conflicting dependencies. It also allows you to easily track what was installed from the base python installation making it easier to go from development to production.

One of my favorite ways of deploying python code is using Docker. In order to deploy a Docker container you will need two files. A requirements.txt file that contains dependencies you wish to pip install, and a Dockerfile that is executed to actually create a container image. I will touch on the requirements.txt file here, but to learn more about deploying python code using docker check out my blog post showing how to deploy data pipelines using containers.

A requirements.txt file is of the format:

configparser==3.5.0
requests==2.20.0
pytest==3.5.1

The best way to managed this file is to simply track manually what you installed since creating your virtual environment. However, if you lost track and simply do not know what libraries you need, open an Anaconda Command Prompt, activate your environment, and run pip freeze. This will list all the libraries and versions that you have installed in your environment. Note that this includes dependency installation, meaning if you installed a library that install dependent libraries it will list those as well. That is not ideal but it will be fine.

Overall, Anaconda environments make python development much easier and allows me to quickly prepare my solution to be deployed to test and production environments.

Azure Machine Learning Services and Azure Databricks

As a consultant working almost exclusively in Microsoft Azure, developing and deploying artificial intelligent (AI) solutions to suit our client’s needs is at the core of our business. Predictive solutions need to be easy to implement and must scale as it becomes business critical. Most organizations have existing applications and processes that they wish to infuse with AI. When deploying intelligence to integrate with existing applications it needs to be a microservice type feature that is easy to consume by the application. After trial and error I have grown to love implementing new features using both the Azure Machine Learning Service (AML Service) and Azure Databricks.

Azure Machine Learning Service is a platform that allows data scientists and data engineers to train, deploy, automate, and manage machine learning models at scale and in the cloud. Developers can build intelligent algorithms into applications and workflows using Python-based libraries. The AML Service is a framework that allows developers to train wherever they choose, then wrap their model as a web service in a docker container and deploy to any container orchestrator they wish!

Azure Databricks is a an optimized Apache Spark Platform for heavy analytics workloads. It was designed with the founders of Apache Spark, allowing for a natural integration with Azure services. Databricks makes the setup of Spark as easy as a few clicks allowing organizations to streamline development and provides an interactive workspace for collaboration between data scientists, data engineers, and business analysts. Developers can enable their business with familiar tools and a distributed processing platform to unlock their data’s secrets.

While Azure Databricks is a great platform to deploy AI Solutions (batch and streaming), I will often use it as the compute for training machine learning models before deploying with the AML Service (web service).

Ways to Implement AI

The most common ways to deploy a machine learning solution are as a:

• Consumable web service
• Scheduled batch process
• Continuously streaming predictions

Many organizations will start with smaller batch processes to support reporting needs, then as the need for application integration and near real-time predictions grow the solution turns into streaming or a web service.

Web Service Implementation

A web service is simply code that can be invoked remotely to execute a specific task. In machine learning solutions, web services are a great way to deploy a predictive model that needs to be consumed by one or more applications. Web services allow for simply integration into new and existing applications.

A major advantage to deploying web services over both batch and streaming solutions is the ability to add near real-time intelligence without changing infrastructure or architecture. Web services allow developers to simply add a feature to their code without having to do a massive overhaul of the current processes because they simply need to add a new API call to bring those predictions to consumption.

One disadvantage is that predictions can only be made by calling the web service. Therefore, if a developer wishes to have predictions made on a scheduled basis or continuously, there needs to be an outside process to call that web service. However, if an individual is simply trying to make scheduled batch calls, I would recommend using Azure Databricks.

Batch Processing

Batch processing is a technique to transform a dataset at one time, as opposed to individual data points. Typically this is a large amount of data that has been aggregated over a period of time. The main goal of batch processing is to efficiently work on a bigger window of data that consists of files or records. These processes are usually ran in “off” hours so that it does not impact business critical systems.

Batch processing is extremely effective at unlocking *deep insights* in your data. It allows users to process a large window of data to analyze trends over time and really allow engineers to manipulate and transform data to solve business problems.

As common as batch processing is, there are a few disadvantages to implementing a batch process. Maintaining and debugging a batch process can sometimes be difficult. For anyone who has tried to debug a complex stored procedure in a Microsoft SQL Server will understand this difficulty. Another issue that can arise in today’s cloud first world is the cost of implementing a solution. Batch solutions are great at saving money because the infrastructure required can spin up and shut down automatically since it only needs to be on when the process is running. However, the implementation and knowledge transfer of the solution can often be the first hurdle faced.

By thoughtfully designing and documenting these batch processes, organizations should be able to avoid any issues with these types of solutions.

Stream Processing

Stream processing is the ability to analyze data as it flows from the data source (application, devices, etc.) to a storage location (relational databases, data lakes, etc.). Due to the continuous nature of these systems, large amounts of data is not required to be stored at one time and are focused on finding insights in small windows of time. Stream processing is ideal when you wish to track or detect events that are close in time and occur frequently.

The hardest part of implementing a streaming data solution is the ability to keep up with the input data rate. Meaning that the solution must be able to process data as fast or faster than the rate at which the data sources generate data. If the solution is unable to achieve this then it will lead to a never ending backlog of data and may run into storage or memory issues. Having a plan to access data after the stream is operated on and reduce the number of copies to optimize storage can be difficult.

While there are difficulties with a streaming data architecture, it enables engineers to unlock insights as they occur. Meaning, organizations can detect or predict if there is a problem faster than any other method of data processing. Streaming solutions truly enable predictive agility within an organization.

Check out the Walkthrough

Implementing a machine learning solution with Azure Databricks and Azure Machine Learning allows data scientists to easily deploy the same model in several different environments. Azure Databricks is capable of making streaming predictions as data enters the system, as well as large batch processes. While these two ways are great for unlocking insights from your data, often the best way to incorporate intelligence into an application is by calling a web service. Azure Machine Learning service allows a data scientist to wrap up their model and easily deploy it to Azure Container Instance. From my experience this is the best and easiest way to integrate intelligence into existing applications and processes!

Check out the walkthrough I created that shows engineers how to train a model on the Databricks platform and deploys that model to AML Service.

Streaming Machine Learning with Azure Databricks

Organizations are beginning to not only benefit from streaming data solutions, but require them to differentiate themselves from their competitors. Real-time reporting, alerts, and predictions are now common requests for businesses of all sizes.

That said, they rarely understand the requirements or implementation details needed to achieve that level of data processing. Streaming data is information that is generated and consumed continuously. This information typically includes many data sources, including log files, point of sale data (in store and online), financial data, and IoT Devices, to name just a few.

Implementation

Fast and Easy

Generating business-changing insights from streaming data can be a difficult process; however, there are quick wins for organizations of all sizes. Microsoft Azure offers an abundance of PaaS or SaaS products that allow users to connect to sources and automate workflows.

With Azure Logic Apps, it is extremely easy to set up data pipelines that extract data from your social media pages, analyze them for sentiment analysis, and alert users when comments or posts need to be addressed. While this may not be a business-changing solution, it gives companies the ability to have a more intimate level of interaction with customers or users than they had before.

Microsoft has provided a simple solution for companies to take advantage of this capability. Using Azure Logic Apps and Microsoft Cognitive Services, one can be alerted of any positive or negative tweets that occurs about their company. This is an easy and cost-effective way to implement intelligence into workflows. (Check out the example available here.) Azure Logic Apps connect to a variety of data sources, enabling organizations to obtain a quick win for real-time reporting with a deceptively simple drag-and-drop interface.

Ideal Implementation

From my experience, companies benefit most from custom machine learning solutions that solve a specific business problem using their own data. Creating solutions tailored to solve a problem in a specific environment allows a business to truly take a proactive approach as they incorporate intelligence throughout their organization. However, lack of knowledge is often a barrier for companies when implementing custom and scalable solutions.

Azure Databricks is an optimized Apache Spark platform perfect for data engineering and artificial intelligence solutions. It is an ideal platform for implementing batch or streaming processes on business critical data, and enables developers to create and deploy predictive analytics (machine learning and deep learning) solutions in an easy to use notebook environment.

Initially, organizations may implement their solutions as batch processes on Azure Databricks to save on cloud consumption costs, but plan for the future by using a platform that will scale and grow with the needs of the business. Batch processes allow users to save on monthly costs by turning off your virtual machines when they are not used, then when real-time insights is required the developer can almost flip a switch for streaming data. Deploy cost effective infrastructure now with the ability to scale limitlessly as you need in the future.

Below is a common infrastructure diagram I implement with my customers. If streaming is not required then we simply bypass the event hub and write python or scala scripts to connect directly to the data sources.

1. A number of data sources (devices, applications, databases etc.) that publish information to an Azure Event Hub (or Apache Kafka).
   1. Please note that whatever the data source is, there will always need to be some sort of process or application that collects data and sends it to the Event Hub.
2. Azure Databricks will write the stream of data as quickly as possible to an unaltered, “raw”, data storage in an Azure Data Lake Store or Azure Blob Storage.
3. In addition to writing to raw storage, Databricks will be used to cleanse data as needed and stream appropriately to an application database, Power BI, or use Databricks Delta for real-time insights, consumption, and intelligent automated actions. Please note that applications can read directly off an Event Hub as a consumer as well.
4. Then use Azure Databricks to train a machine learning or deep learning model that can be used to make streaming or batch predictions.

Tips to Actually Implement a Solution

When implementing new intelligent solutions with cloud infrastructure, it is likely that it will require internal business stakeholder buy in. Therefore, in order to successfully implement a new predictive analytics solution you must:

1.  Identify a business problem to solve and the stakeholders
2.  Visualize or surface results to “wow” stakeholders
3.  Start developing iteratively

If a team attempts to solve too many problems initially by trying to answer all possible questions, they will likely fail to “wow” a business user. Developers will likely focus all their time on coding and analyzing the best path forward that they will only have code to show (code is a rather boring deliverable for most business users), and may simply never get past the proof of concept or analysis phase.

Business Problem

It is common for companies to simply start creating a solution to work with newer technology without a true business problem they are trying to solve. It happens most often for organizations who want to start a data lake strategy. Their main goal is to develop a data lake so that other business units can take advantage of the sandbox environment for predictive analytics.

I believe a centralized data lake for organizations is a great idea for any IT group. However, without a specific business problem, it is difficult to see the true value that a data lake or machine learning solution provides, which in turn can slow adoption. By focusing on solving a single use case other, there will be a reference to other business units on why they should use the enterprise data lake. The reason for adoption is much more tangible.

Wow Stakeholders

There is not a more boring outcome to a business stakeholder than a project resulting in code. Machine learning or deep learning projects must have some type of end product that accurately describes the effectiveness of the solution created. In most machine learning solutions that I implement, I will almost always provide a Power BI Report. This ensures that the model and predictions are tangible because they are shown through visualizations. The business user now has the ability to actually use the predictions and show other internal users the solution.

Iterative Development

The most frustrating part of projects can be the initial planning or analysis phase. Large enterprises will often start a project and get stuck in analysis paralysis. I encourage teams I work with to simply start coding! This does not mean to do zero planning or proof of concepts, but at some point a team has to pick a direction and run with it. Avoid over analyzing various products by picking a small subset of well-known products, analyze them, and go!

Benefits

Streaming data architecture is beneficial in most scenarios where dynamic data is generated on a continual basis. Any industry can benefit from data that’s available almost instantly from the time it was created. Most organizations will begin with simple solutions to collect log data, detect outliers based on set (unintelligent) rules, or provide real-time reporting.

However, these solutions evolve, becoming more sophisticated data processing pipelines that can learn and detect outlier data points as they occur. The true advantage of streaming data is in performing advanced tasks, like machine learning, to take preventive or proactive action.

Processing a data stream effectively generates quick insights, but it does not replace batch processes. Typically, organizations implement both solutions to gain quick, more computationally intensive insights. Streaming data reacts to or anticipates events, while batch processing derives additional insights after the fact.

Batch processing can often require more compute. It’s ideal when time or speed is not a priority. One of the biggest advantages of Azure Databricks is that companies are able to use the same infrastructure for both their workflows!

Batch processing data requires a system to allow data to build up so that it can be processed all at once. This often requires larger compute resources than streaming due to the size of data, which can be a hurdle for most organizations; however, it allows users to aggregate and analyze large amounts of data over a longer period of time. Streaming solutions do less computing, but require machines to be running 100% of the time and typically look at data over a shorter period of time.

Example

I recently created a simple walkthrough of how to implement a streaming data solution on Azure. Check out the walkthrough on GitHub. Please note that an Azure subscription is required.

Conclusion

Organizations of any size can benefit from a streaming solution using Databricks and Azure Data Lake Store. It enables near real-time reporting, as well as, provides a sandbox environment for iterative development of intelligent solutions. Azure Databricks and Data Lake Store allow a developer to implement both batch and streaming solutions in a familiar and easy to use environment.

3 Keys for Your Organization to Get the Most from AI

Organizations are constantly weighing the cost and benefit of investing in Artificial Intelligence (AI) solutions. Introducing advanced predictive analytics to a company can push them to the bleeding edge of innovation and past their competitors, however, the hurdle is often difficult to get over. But why?

First, understand how we define AI

The term AI can mean several different things; however, the most commonly used definition refers to the idea of intelligent machines, which is in slight contrast to the aspirational machine with human-level intelligence. There are endless ways to implement Artificial Intelligence, but the primary ways are via machine learning and deep learning.

Machine learning uses labeled historical data to train a model to understand patterns and make accurate predictions on new and unlabeled data points.

Deep learning is a subcategory of machine learning revolving around neural networks. While neural networks have been around for decades, they have truly exploded in research and use in the last five to ten years. Deep learning is used to solve problems that require a human such as image recognition, text/speech analytics, and decision making (i.e. game playing).

For the sake of this article, we will use AI as a synonym for machine learning and deep learning, even though AI in general may refer to software and hardware having human-level intelligence which is not achieved using current methodologies.

Let’s focus on 3 key areas to get the most from AI

At 10th Magnitude, our data intelligence community focuses on bringing analytics to solve our customers’ problems through data science, reporting, and big data pipeline projects.

Outside of developing solutions we encourage organizations to focus on the following cultural- and process-oriented areas to truly get the most out of their AI solutions.

Know Your Business Use Case(s) and Collaborate 

The majority of AI applications are powered by machine learning, which is used to solve a very specific problem using data.The first thing that I do with a customer who is new to machine learning is to understand and identify all of their business problems. These problems often turn into new use cases for machine learning or deep learning.

Since the use cases are derived from the business itself, the key to creating a successful solution is collaboration between the data science team and business stakeholders. Additionally, stakeholders are likely the individuals who will need to approve the completion or evaluate the success of the developed application

Therefore, understanding what is needed to solve the problem and then relating the problem back to the data is crucial. Keeping the stakeholder aware of the development cycle allows them to understand the challenges data scientists encounter when creating new predictive analytics workflows.

Additionally, this enables the organization to develop a data-driven culture. Involving business users in the development room gives non-technical folks insight into what is possible, allowing them to spot other areas where AI can be of use.

10^th Magnitude believes in the idea of data-driven design, where we use data to solve problems, power applications, and change the way an organization thinks about their business.

Don’t Stop After Development

Developing a machine learning solution is difficult. It is an iterative cycle where individuals go back and forth from the business to understand the problem, gather data, and train models. Developing these solutions takes time, however, once development is done you are only partially completed with the project.More often than not we see customers give up on a solution after the development portion because the model did not perform as well as hoped or the cost to put it all in production is simply too high. It takes a lot of work to move a solution from a development environment to a production one.For example, we recently worked with an organization to build and develop a model to detect anomalies for their different pieces of equipment. It took 2-3 weeks to develop the solution and an additional two weeks to set it up in production; we had to move the code to a production workload, build and release pipelines for two environments, model consumption, model monitoring, and more.

As data scientists, we often forget the difficulty and the amount of time it takes to move a solution to a production so that the organization is able to see the true benefit.

It is important to keep in mind that empowering an application or workflow with machine learning is about more than just the application. It also gives people the ability to see what is possible with the data.

Automation is Your Friend

Usually, data scientists are not familiar with automated build and release pipelines, but it is a skill that is quickly becoming a requisite in order to properly participate in the predictive analytics space. DevOps is the process and culture of delivering value to customers in a sustainable manner. As predictive insights grow organically within an organization, individuals need to be available to develop new solutions. and not maintaining existing ones. Automation is extremely useful in data science projects, specifically for: deploying changes to production with automated tests, retraining of existing model with new data, and monitoring the performance of the model.No data scientists should bring “right-click and deploy” predictive solutions to production; unfortunately, that happens more often than one would hope. Using Visual Studio Team Services (VSTS), we enable our customers to version control their code for team collaboration and set them up with automated build and release pipelines to train, test, and deploy their code.

As more data is collected, the solution will need to be retrained on a cadence to keep the model up to date so that it continues to make good predictions. While this task may seem like a trivial manual task, the time it takes a data scientist to update a model could be used to create new solutions or enhance old ones.

Often clients will only focus on surfacing results to their end users via reports, applications, or workflows; they forget that they need to build an interface to their solution for themselves.

Data scientists are responsible for maintaining the quality of a solution over time, therefore, the metadata gathered from testing the solution (success criteria, training time etc.) should be stored and visualized to understand the current and historical performance of a solution.

Conclusion

Developing machine learning and deep learning applications is far from easy. However, clients often struggle with the amount of effort it takes to create custom solutions, or they get so bogged down in technical details that they forget the why their business started on the path to AI in the first place.

So, what are the keys to successfully incorporate AI into your organization? To start, collaboration between the data science team and business stakeholders, understanding the data science process, and deploying solutions using DevOps. This process makes predictive analytics possible for data science teams of all sizes even as it changes the mindset of the organization as a whole.

If you’re ready to bring AI into your day-to-day, 10^thMagnitude has the solutions to incorporate it seamlessly and painlessly, ensuring that you get the benefits without missing a beat.