Author: xpresso.ai Team

More Data Is Better In Machine Learning Model Creation

Data is at the heart of every machine learning model. Without it, model accuracy drops precipitously. However, there are many machine learning models that need to be developed without having as much data as the engineer would like. This is where data augmentation comes in. It is one of the primary ways of achieving model accuracy in production. However, it is quite a difficult practice to do, as it involves creating synthetic information to feed to your model.

There are not many frameworks currently available for this, which means that most engineers have to develop a solution from scratch. So, what is data augmentation?

What Is Data Augmentation

You can think of data augmentation as a process of creating synthetic data to feed into your machine learning models. For example, let’s say you are training an image recognition algorithm on a data set featuring cars. However, you might not have the necessary pictures of cars you need to feed that data set. Your model accuracy would drop quite low if you tried to put a model into production after training it on that data set.

A way you can alleviate that is to generate more pictures from the ones you already have. For example, you could have pictures of the same cars but different colors. You can also change their orientation by performing basic image manipulation algorithms on them. Doing simple things like this is what is called data augmentation.

How to Solve Data Augmentation Problems

One of the many use cases of data augmentation is when you are working on image classification algorithms. These algorithms need massive data sets to work well, but you might not have that on hand. Data augmentation solves this problem by easily helping you generate multiple versions of the same picture.

You might also be working with other classification algorithms that don’t have the necessary data set to perform adequately. It is always useful to have these tools on hand to help you do a better job with data augmentation. Sometimes it can be as simple as placing objects that shouldn’t be there in your machine learning model. The biggest benefit here is to model accuracy.

When Should You Use Data Augmentation In Your Machine Learning Process

There are a few major use cases for data augmentation in developing your machine learning models. When model accuracy is the most important thing, it should be one of the first methods you move towards to solve those problems. The next use case is when you have a small data set that needs a bit more to make your model as accurate as possible. By expanding your data set, you ensure that your AI algorithm has more to go on when training.

Finally, one of the other important areas is when you don’t have control over the input data. Real-world data might not be as clean and pristine as what you are working with in the lab to train your model. You can use data augmentation to create the rough data that your machine learning algorithm will see in production.

Comparing Structured and Unstructured Data

• When we talk about machine learning, we talk about models that crunch data to meet their objectives. The data comes in two forms – structured and unstructured data.
• Structured data come from predictable sources like sensors, individuals recording data, or transaction stages. This data is controlled with a proper structure, boundary conditions, predictable ranges, and volume.
• The challenge comes with unstructured data, as it its share has grown manifold with the rise of internal/external communication, social and print media, and spanning across various form factors like blogs, newspapers, reviews, advertisements, and social media sites.
• Compared to the structured data that is ingested by any organization to meet business objectives, unstructured data occupies more than 80% of the total share.

Observing unstructured data is very significant while formulating a strategy based on various competitors’ moves and the latest trends in demands or the technology upgrades. You can easily and quickly look at many aspects of the product/service with specific action points and work towards devising your approach.

The approach towardshandling unstructured data and structured data differs. This is because structured data has pre-defined feature sets and focuses on the target attribute whereas unstructured data may throw diverse aspects to the observer. For example,if we analyse user feedback, we may encounter an unpleasant service, product defect, or transactional issue. By studying the competition, one can get to know the new product/service launched, upcoming trends, and the opportunities which may be tapped.

Source of data

• AI/ML practices can retrieve unstructured data from various sources like business documents, emails, social media, customer feedback, webpages, open-ended survey responses, images, audio, and videos.
• These are retrieved from a central repository that containsemails, business documents or customer feedback.
• At the same time, scrapping can fetch data from differentwebsites that are based on indexing keywords, which in turn are aligned to the business’s end- goals.
• Social media provides APIs for obtainingdata based on users, pages, or hashtags that are used to highlight the focus area.

Operation on the unstructured data

• To train models with unstructured data, the data requires cleansing and sorting. However, it must be done differently from structured data.
• It will depend on the nature of data, text, audio, image, or video. The cleansing will also depend on the end goal of the analysis of the data.
• The text, when analyzed, will give associations, sentiments, translations, or trends.
• NLTK, Gensim, Polyglot, TextBlob, CoreNLP, spaCy, Pattern, Vocabulary, PyNLPI, and Quepy are few libraries that provideusers out of the box features for immediate use by tuning hyper-parameters.
• Most libraries provide features like tokenization, language detection, named entity recognition, part of speech tagging, sentiment analysis, word embeddings, classification, translation, WordNet integration, parsing, word inflection, adding new models, or languages through extensions.
• Specialized libraries like Pattern, Vocabulary, PyNLPl, Quepy give additional features like crawling text from websites, network analysis, graph centrality, visualization, translations, and question-like interface.
• With audio, one has toanalyze the sentiment based on the tone as well. The analysis is done after audio data is converted to text and analyzing the same. Tone and accent are also an essential aspect of the audio.
• Python libraries like Librosa and PyAudio are used widely for the analysis of audio data. This data analysis is used for music genre detection, voice commands, generating language/voice for voice-based assistants.
• Pyo, pyAudioAnalysis, Dejavu, Mingus, hYPerSonic, Pydub, Loris are few Python libraries that provide users out of the box features for immediate use by tuning hyper-parameters.
• These libraries provide features like sound granulation, audio manipulations, classify unknown sounds, apply dimensionality reduction to visualize audio data and content similarities, perform supervised and unsupervised segmentation, detect audio events and exclude silence periods from long recordings.
• Libraries like Mingus work on music data, while hYPerSonic, Pydub, Loris work on the low-level analysis of the sound data for time- and frequency-scale modification and sound morphing.
• Image processing is one of the other extensions of data analysis. The image analysis can help identify/count people, objects, detect faults, and various other features.
• Scikit-image, OpenCV, Mahotas, SimplelTK, SciPy, Pillow, Matplotlib are few Python libraries for image data giving users out of box features for immediate use with the tuning of hyper-parameters.
• The libraries provide image processing, face detection, object detection,  watershed transformation, morphological processing, image convolution.
• These libraries also support multiple image formats, can manipulate images for extracted information, or conduct analysis for measurements.
• Video data processing is an extension of image and audio data processing. The audio and image data are analysed and collated to give the results.
• Since the models to analyse and predict unstructured data may require data in different forms, the data captured is pushed to the data lake and retrieved for training the models based on the transformations. To predict using the streaming data, the trained models are further deployed on the MLOps workflow as web services. The streaming data subsequently train the model if the forecast is accepted or rejected. Finally, the trained model may be deployed again as the web service. The frequency of deployment may vary from few minutes to few days.
• General techniques used in handling structured data can be applied to unstructured data for ease of operations later. The units of unstructured data are tagged with the findings for use  withfurther models. NoSQL databases like MongoDB, Hadoop, and other popular databases can help keep the data in JSON format.

Role of MLOps in managing and using unstructured data

• Similar to the regular structured data, the MLOps platform gives a foundation for the practice to access the data lake, set up intermediate components for transformation/tagging, use the model code to generate a trained model, and deploy the trained model to the web service.
• Furthermore, the MLOps platform can automate various tasks, like data ingestion through the streaming API, scheduling the training, deploying the latest trained models, or sending the alerts to related stakeholders for an item that needsimmediate attention. In addition, the platform can generate regular reports for stakeholder’s consumption and give a baseline to the upcoming models.
• With the advent of edge computing, trained models can also follow the hierarchical model. The parent trained model may be trained on the servers and be broadcasted to the IoT devices. This reduces the latency of the prediction and updated data requirements. This is quite relevant on video cameras for face/object detection.

Feature in xpresso.ai to handle Unstructured Data

• xpresso.ai connects with multiple platforms on a regular basisto fetch data with specific parameters. The data can come in various extensions, and the API can give back various statistical figures for the data scientist to explore and decide their further course of action.
• xpresso components can be customized for handling text, audio, and image data in streaming or batch format.
• xpresso has universal connectors to fetch data from various data sources, which is typically a file system in case of unstructured data. Some examples. are Local FS, HDFS, or AWS S3. A dict parameter in the import_dataset method of class gives the repository specifications.
• For UnstructuredDataset, the Data connector returns a pandas DataFrame with metadata information of file(s) constituting the file size, name, and path. xpresso then provides a basic explore method that helps to perform numeric analysis on the file sizes of all the files collected.
• It calculates different numeric metrics (i.e. min, max, pdf, quartiles, mean, median, var, etc.) on file sizes. Since the data is in the pandas data frame, one is free to use other libraries to do more advanced analysis. It is a good practice to version the data at every step of transformation.
• This data can be further used in the models for the predictions and analysis from the data.

Summary

• Using unstructured data to meet business goals is an essential contribution to any organization’s AI/ML practice. xpresso platform helps automate the operations and keep the stakeholders informed about the common sentiment in the business arena.
• The analysis of unstructured data would be of utmost importance when it would derive intelligence with structured data, identify the business’s action points, and help them devise the strategy to keep up with the market demand and competition.

Importance of Data Versioning

• While AI/ML practices always talk about data and models to develop business applications, the ecosystem cannot scale up if we do not adequately manage data, parameters, and model versions. As the practice scales up, the data versioning system acts as the spine of the platform.
• Data versioning refers to saving new copies of your files when you make changes so that you can go back later and retrieve specific versions of your datasets. The system helps in data management and orchestration while giving the input to the AI/ML models in various forms.
• Versioning is linked to a change due to reprocessing, correcting, or appending additional data in the structure, contents, or condition of the entity in question. This entity can be documents, software, pieces of code, data science models, or any other collection of information.
• Specifically, data versioning refers to the process of uniquely identifying data, similar to categorizing code, to enable pulling and using any version of the data as required. In simpler words, it is a method to track changes associated with fluctuating data.
• When data is identified uniquely, data scientists can determine whether and how data has changed, which version of a dataset they are working with, and understand if a newer version is available.
• Also, with proper versioning, data scientists can understand data provenance, what processing step caused the data to change, and how that propagates across the data processing pipeline. Explicit versioning allows for repeatability in experiments, enables comparisons, and prevents confusion.

Let us understand in greater detail why data versioning is essential:

• Ensure better training data: ML comprises rapid experimentation, iteration, and training models on data. Thus, training on incorrect data can have disastrous results for the outcomes of a ML project.
• Track data schema: Enterprise data is usually obtained in batches, and often minor changes in the ML schema are applied over the course of a project. With proper versioning, you can easily track and evolve the data schema over time. You can also understand whether these changes are backward and forward compatible.
• Continual model training: In production environments, data is refreshed periodically and may trigger a fresh run of the model training pipeline. When such automated retraining occurs, it is important to have data versioned for tracking a model’s efficacy.
• Enhance traceability and reproducibility: Data scientists must be able to track, identify the provenance of data, and point out which version of a dataset reinforces the outcomes of their experiments. They should re-run the entire ML pipeline and reproduce the exact results each time as it is a critical input for the modeling process. Thus, the original training data must always be available. Hence, from a reproducibility/traceability perspective, proper versioning is critical.
• Auditing: Proper versioning ensures that the integrity of data-based activities is upheld by identifying when modifications are made. By monitoring and analyzing the actions of both users and models, auditors can identify intentional and accidental lapses in user behavior. Data science auditors can thus examine the effect of data changes on model accuracy and determine best ML practices for the enterprise.

Pachyderm is one of the most popular data versioning frameworks that has multiple features and SDK APIs. These can be used to integrate with the MLOps platform to use the features programmatically. It acts as a repository for data management and orchestration atall the stages of MLOps. The repository may possibly contain input/output datasets, hyperparameter configurations, trained models, and experiment metadata.

We further discuss the key features of the data versioning systems used in MLOps, and the advantages forusers while using those features.

Job Execution Schemas

• The models consume data in different manners while training, and the datasets give different results for a separate set of hyperparameters.
• Different workflow models for data ingestion and hyperparameter tuning are suggested depending on the nature of the data. The data versioning systems have built-in functionality to set up any hybrid models.
• Jobs can run on various criteria.
  • For incremental data, the job runs as per the set schedule for the set hyperparameters.
  • In the case of static data, the jobs run based on variation in hyperparameters.
• The results explain the combination of hyperparameters that will provide the best outcome for the segregation of the learning rate and the results on the test data. Refer Annexure A for details.

Data Flow Designs

• The data is present in repositories and made available to the model for training; while the output is rendered to separate repositories. This is the simplest condition that can be configured for the pipeline.
• In complex situations, the data is presented in a combination of multiple repositories with different ways of how the data is fed to the training models.
• The data is fed to the models in various forms. The unit data fed to the model can be all data at once, each directory independently, individual files one by one, or combined data from multiple inputs together.
• The input files give input in unions, crosses, joins, and groups taking data from multiple repositories. Refer Annexure B for details.

Data Provenance

• Another vital aspect of data versioning is Data Provenance. This is the ability to track any result back to its raw input, including all analysis, code, and intermediate products.
• xpresso implements provenance for both commits and repositories. You can track revisions of the data and understand the connection between the data stored in one repository and the results in another repository.
• Data scientists use the provenance feature to build on each other’s work, share, transform, update datasets, and automatically maintaina complete audit trail so that all results are reproducible.
  • For example, an individual’s mortgage application will go through multiple approval steps after checking out various data points and analyses. Let’s assume the decision is unfavorable, and the applicant challenges the decision. The applicant wants to know the reason for the decision. The historical trail of data is essential to confirm that there is no bias in making the decision.
  • Reproducibility of the results is a critical factor for establishing this aspect. The results can only be reproduced when there is an exact set of the trained model on a particular dataset and the production dataset for deriving the results.
• Reproducibility is a part of regulatory requirements as well to maintain the reproducibility of results.

Data Versioning Features in xpresso.ai

xpresso integrates data versioning with code and hyperparameter version, along with security features. The artifacts and models are stored in a customized data repository. One can access these in 3 modes, as follows:

1) Data versioning libraries, available with xpresso.ai, enables you to version control your datasets.
2) The xpresso.ai Control Center GUI enables you to view the stored datasets and models via the ‘Data Repository ’ explorer, thus providing a rich yet simplified user experience.
3) You can also usepre-built components in your pipeline with no-code. At run time you can provide the parameters and perform various activities such as pull dataset, push dataset, create branch, view commits, etc.

The process of version repository creation is integrated with project creation. The security of the repository is based on the accesses given in the project to certain sections. The data versioning system is controlled.

• The set of defined steps make sure that the versioning is taken care of in an integrated manner using APIs. The Version Controller Factory class kicks off the use of the API, further using Version Controller and Data Versioning connector to check in the artifacts after authentication using Versioning Authenticator module.
• xpresso Control Center GUI is used to access and manage the data versioning system of the project. One can view the repo and datasets, create branches, push datasets and even download them on a local system. The screenshot of the same is shown below:

xpresso’s pre-built components in Docker images uses the version control repository details from the property files as parameters.

Other advantages of Data Versioning System in MLOps

Other tangible benefits of the data versioning system on the MLOps platforms are as follows.

• Various strategies stated above are essential for designing the data flow while training models. The data versioning system should give data scientists the flexibility to contain the data flow strategy within the configuration files and not be hard-coded in the model code. This allows data scientists  to re-use the same model code for multiple scenarios.
• Comparing multiple repositories on the basis of their input data and their output will help the data scientist learn which data transformations and cleansing policy helped generate better results. This helps avoid underfitting/overfitting training of the models.
• The data versioning system acts as a single source of data or truth for various initiatives without having to maintain multiple copies of the large datasets. This saves disk space and improves the manageability of the datasets for bigger teams.

Summary

• Proper integration of a data versioning system with the xpresso MLOps platform helps users roll out models to production with a mature data context.
• The data versioning system helps build flexibility and traceability in the design while assisting the practice managers to keep the regulatory compliances in place, which is critical for any MLOps practice.

Explaining the Machine Learning Pipeline

The machine learning pipeline is an often misunderstood concept. There are a variety of machine learning processes that need to happen to go from an idea to a working application with machine learning embedded. You can think of machine learning as a list of steps that need to happen to get to your finished working application. However, machine learning is special because it brings together a lot more steps than traditional software engineering. It makes the process of creating an automated machine learning pipeline more difficult.

A traditional machine learning pipeline starts with acquiring data, and it ends up with a machine learning engineer putting the model into your working application. That is why it is crucial for you to understand how everything works, as that is the only way for you to understand how to improve your processes and results. Most machine learning initiatives fail, and a reason for that is a misunderstanding of what the various machine learning processes are.

Stages of the Machine Learning Pipeline

The goal of every machine learning project is to put an accurate model into a production application. However, you also need to feed that model with good data. Building machine learning pipelines like this isn’t easy, and there are many machine learning processes you have to go through.

As a machine learning engineer, it is also crucial for you to automate portions of the pipeline. Automation streamlines the entire ML pipeline, making it possible to deploy successful models a lot faster.

It all starts with an idea and the data needed to test whether that idea is true or not. This is the part of the ML pipeline where your data scientists have come up with a hypothesis and are testing it in one of many statistical modeling tools in the industry. They can use these tools to ensure that they have a good handle on things. Most of the data ingestion and cleaning is done manually at this step, and business goals are evaluated.

The next step is to start training the model using the newly acquired data. This is still the step that is done by data scientists, but this is changing quickly with the introduction of MLOps. At this stage of the machine learning pipeline, machine learning engineers are usually not involved in the process of putting this model into use.

After this step, the model is optimized, evaluated, and it is eventually deployed by machine learning engineers. It is finally at this page where we learn whether the project was a success or not. In reality, 85% of models never make it into the application, meaning that the failure rate is quite significant.

Problems the ML Pipeline Solves

This pipeline is important because it solves a lot of challenges that businesses have. The main one is that most of these processes are repetitive. By using the ML pipeline, businesses can start to automate a lot of the processes that they would have to do manually in the past.

The next problem it solves is that it helps fix errors quickly. Because of the iterative nature of the machine learning pipeline, you can easily see how your changes lead to side effects. You can then make the necessary adjustments and continue iterating on your project.

Overall, this helps you reduce risk and create more accurate machine learning models.

Using an Off-the-shelf ML Pipeline Solution

It might seem like a handful to build an entire ML pipeline yourself. That is because it is, which is why you should focus on using off-the-shelf solutions like xpresso.ai. An off-the-shelf solution like this helps you streamline the machine learning process, making it easier for you to bring successful models to production.

Ensuring Your Machine Learning Platform Is Future-proof

One of the biggest problems in the machine learning industry is that things are changing so rapidly. Because of that, machine learning platforms have a massive problem constantly keeping up with change. These machine learning platforms come in the form of commercial toolsand also in-house solutions.

As a business, it is crucial for you to have principles in place to ensure that you don’t end up with a big problem. There are three principles that can help guide your efforts in building machine learning models. ML models are the output in every project, so it is crucial that you can successfully deploy them every time.

The most important thing you need to know is that the right machine learning platforms will be the ones that you can use in the future. Building platforms will become an industry itself.

Have a Process for Working with ML Data

The first step to ensuring that your ML platform will be useful in the long run is to have a streamlined data operation that can stand up to the test of time. One of the easiest ways to do that is to use open formats for your data. By avoiding all things proprietary, you make it easy to continually use tools supported by the entire industry.

Data is also all about the platform used to process it. We know that the cloud is the future of computing, which is why you should orient your data formats and storage to the cloud. You can do it in a way that lets your data scientists have easy access to all the information they need in R and Python. That makes it easy for them to do the hardest part of the machine learning process. You might think that machine learning models would be difficult to build because of the engineering challenges involved, but the reality is that data processing is the most difficult part. It is usually the part of the process that takes up the most time and effort. Any aspect that can be automated will result in massive increases in productivity.

Build Bridges Between Data Science and Machine Learning Engineering

The second principle involves the fact that machine learning projects need both data scientists and machine learning engineers to bring to market. However, there isn’t an ML platform that both can use simultaneously. As it stands, ML models go through an iterative process that starts with data scientists and ends with machine learning engineers.

One of your biggest priorities should be facilitating the collaboration of these two groups of workers. You need to have the right tools and processes to ensure that there is a lot of back and forth between these two groups. You also need to have a management structure in place that works well with both of them. Sharing your machine learning models should be a major priority for your project as well.

Be Adaptable to Changing Environments

The traditional machine learning model development process looks like what you see above. However, it is crucial to note that things will not always be the same way. You’ll eventually have to plan for a world where machine learning model development is streamlined. Having a scalable solution is also crucial in this environment. Do your machine learning platforms scale? That is something you need to figure out before you even get started. You also need to look at potential software and technological changes in your stack. If you don’t want to go to all of this, you might want to stick to an off-the-shelf end-to-end solution like xpresso.ai.

Machine Learning Models Are Useless If They Aren’t In…

The point of building machine learning models is to put them into a production application. That is why your people must have experience doing just that. There is a step-by-step process that they need to go through, but they also need to have the model deployment experience that can make the difference in tricky situations.

Because of the reality of machine learning, we have to realize that ML model deployment is ultimately the most important part of the process. Everything else is just theorizing until your machine learning models are in a working production application. Having hands-on experience and putting a model into a production application is now a must for data scientists. For most companies, it is unreasonable to expect them to have a team for model creation and another for deployment. Only a few mega-corporations can afford to do that. However, putting anML model in production is a combination of art and science. It is something that almost any data scientist can learn and master.

Getting Infrastructure Right for Data

Every machine learning project needs good data to be successful. However, having the proper infrastructure for that data is often a problem for most companies. They don’t understand where to put the data to make it as effective as possible. Moreover, they might run into problems with the architecture of their application. You must have the necessary infrastructure to feed your model the data it needs to make accurate predictions.

You need to answer questions about where and how you store your training data. You also need to get a handle on the size of your data set as well. How will you retrieve that data set for training and production? These things are important to know because they will determine how you structure your infrastructure in a way that will serve your specific needs. It is for this reason why ML model deployment is such a difficult task.

Choosing the Right Tooling

The next step in the process is choosing the right tooling for your projects. The first choice is usually the cloud solution you will choose. For example, you can choose between AWS, Azure, and Google Cloud Platform. Each solution will have its own way of doing things, but they are not all you have available. You can also choose an end-to-end system like xpresso.ai.

Building a machine learning model is usually the scientific part of the equation. The art begins with choosing the right software tools and hardware infrastructure. The reason why software solutions are so important is that you might run into trouble when trying to deploythemto a cloud platform. How popular is that software solution? It is important to answer this question because it will determine how easy it is to get help when you have these problems.

The Right MLOps Pipeline

Ultimately, it will take an entire team of people to make the right choices and deploy your machine learning models to production. As long as you understand the entire process, you will come out way ahead of your competition.

If you are struggling to deploy your machine learning models, it might be worth your time and effort to have an end-to-end solution that can do everything for you. It means your engineers don’t need to be as skilled, and they can focus on building a model and training data. Solutions like xpresso.ai provide this and much more.

The Good and the Bad Of Machine Learning

Machine learning has become a major buzzword in recent years. There are many advantages of machine learning, but it also has disadvantages as well. It is crucial to weigh the pros and cons to get to a machine learning language that you understand and can decide on. The advantages of machine learning also only come into effect when you understand how to fully utilize its power in your own applications. It can cause a lot more harm than good if you don’t know what you are doing.

We have to remember the reason why we deploy machine learning models to production applications. The main reason is that it benefits us by identifying patterns that humans couldn’t without this new technology. Once we remember that, we must figure out whether our application needs this technology or not. The good thing is that most applications will benefit from the predictive capability that machine learning brings to the table.

The Benefits of Machine Learning

The main advantage of machine learning is that it easily allows you to identify patterns and trends. It is also automated, and once you build that initial model, you can just let it run, and it will predict things for you for a long time. The machine learning model is always improving, meaning that the results get better over time. In machine learning language, we can say that we have a continuously improving program that can handle various data. On top of that, you can use it in multiple different types of applications.

The Downsides of Machine Learning

While machine learning does offer a lot of advantages, it is not without its disadvantages. The main disadvantages of machine learning have to do with how difficult it is to implement.

The first major disadvantage is how long it takes to acquire the data necessary to train your model. In fact, some data needs to be scraped from the Internet, which could cause you many problems. This will take a lot of resources that could be put elsewhere. Machine learning cannot work well with bad data, so you will need to clean that data as well. On top of that, you could have problems interpreting the result, which could cause major problems for your work in implementing your model. There’s also the possibility that your machine learning training didn’t go well. That could introduce a lot of errors, which could render your program worthless before you even begin. All of these problems can happen without that expertise, so you will need to spend a lot of money hiring people who know what they are doing.

Putting Machine Learning to Work for You

Ultimately, once you have decided you want to put machine learning functions inside your application, you need to do that systematically. If you don’t have the expertise, one of the best things you can do is use an end-to-end solution from xpresso.ai. Instead of hiring many people and building the infrastructure yourself, you can leverage the infrastructure that is already there. You have all the tools and everything else needed to make machine learning work in a way that will improve your application.

The Key Issues That Make MLOps Different from DevOps

MLOps has risen in popularity in recent times. It is often compared to DevOps; however, there are a few key differences you need to be aware of. These key differences make MLOps wholly incompatible with the workflow in DevOps. The main issue has to do with the nature of machine learning projects. The MLOps workflow is a lot more complex, and it requires more pieces to come together to get projects off the ground. It is one of the major reasons why machine learning projects only make it into production 87% of the time.

The DevOps workflow focuses on tools to automate the building and testing of software projects. That is because software projects are usually only code. Because of that, you only need to focus on tools to test and deploy your code. It makes the DevOps pipeline more streamlined. It also means you don’t have to focus on many of the complexities that make machine learning so difficult.

Machine Learning Projects are More Complex

Complexity is a major problem with machine learning projects. The main problem has to do with finding and cleaning data as well as turning that cleaned data into a machine learning model that is ready to be deployed.

You also have the fact that MLOps projects require two different professions to work. It starts with the data scientists building the models that machine learning engineers then implement. Throughout this process, there is a lot of testing and repetition that must take place. Many steps are repeated until it gets to a satisfactory result. This complexity is more difficult to manage, which is why MLOps is so much more complicated than traditional DevOps.

MLOps Isn’t as Mature as DevOps

The other factor is maturity. Mainstream machine learning is relatively new. The tools to successfully create a machine learning project from start to finish are relatively new. There is also the fact that the version control system in MLOps is not just concerned with code. You also need to keep track of different versions of your data and models. This added complexity is enough to completely change the nature of MLOps.

On top of all the reasons listed above, you also have to think about where the data you use will come from in production. It means taking things like model drift into consideration. These different factors make a big difference when working with most machine learning projects. It means that the concepts in DevOps are not enough to keep up with the requirements of MLOps.

Machine Learning Requires More to Bring to Production

As mentioned above, you will need much more than code to bring a machine learning project to production. The increased requirements will forever make MLOps a different beast to DevOps.

However, as MLOps tools improve, they will become a lot more streamlined. We might even come to a time when deploying machine learning projects is as easy as software engineering projects. Until then, there are tools like xpresso.ai to automate a significant portion of the process. It cuts down your startup time dramatically.

How Proper Governance Will Lower Bias in AI

Regulation is coming to AI. That is one of the many reasons model monitoring and trusted AI are two concepts that developers will need to understand. These two concepts can be the difference in ensuring that your AI models can withstand future oversight and regulations that might come to the industry. AI algorithms are now a crucial component of many modern activities. Because of this, companies need to have a good handle on ensuring that fairness is maintained at every step of the process.

Modern AI algorithms operate like black boxes. That means that they don’t show how they get to the results they put out. This can be a huge problem in societies where fairness is a major cultural phenomenon. After all, you can’t have AI algorithms preferring one racial group over another when making decisions. These things make AI governance key.

International groups have developed a system for classifying what constitutes a fair AI system.

What Is AI Bias

Artificial intelligence algorithms are often built from data sources that are trained to turn into good models. AI bias is anything that makes that model benefit one group arbitrarily over another. For example, you wouldn’t want to have a system that was trained on only one racial group. It would be better at identifying that racial group over others. Simple things like this can have massive impacts on our society.

Because of the black-box nature of AI algorithms, there has to be AI explainability to help normal people understand what goes on inside an algorithm. MLOps governance is also going to be a crucial component of the process. Companies will need new ways of mitigating bias to be successful in the future.

How It Manifests Itself

Bias can manifest itself in many ways. For example, banks might find that certain racial groups are prioritized for loans based on the AI algorithm being implemented. This obviously has a massive effect on the community and the trust they have in the bank. Maintaining transparency can be the difference between trust being lost or not. Model monitoring is also another thing that companies can do to ensure that bias never creeps in.

How Proper Governance Can Fix Things

Proper governance can form the foundation to have trusted AI that works to the benefit of everyone.

AI audits will become a foundation of proper governance. Another solution will be AI explainability. That explainability is key to ensuring that the average person will trust what’s inside your algorithm. It will also help you avoid increased scrutiny that can come from governments all around the world. MLOps has processes built in to help you detect and eliminate bias.

When everything is combined, you have an increased chance of avoiding all of the problems that will come your way. Proper governance will be an issue. Machine learning models will become an increasingly important piece of our society. Having a system to detect and prevent bias is going to be more crucial going forward.