Table of Content
- Machine Learning Strategy
- How can you grow your business?
- How will your cost structure evolve?
- How to Develop and Train ML Models
- Machine Learning and Scalability
- Developing a Scalable Machine Learning Pipeline: —An ML Training Pipeline
- Operationalization of Machine Learning Models
- Challenges with Machine Learning Systems
- Machine Learning Datasets
- Machine Learning with Elasticsearch
- Advantages of Elasticsearch
- How to Set Up Elasticsearch on a Windows Platform
- ML Pipeline Automation for a Prediction Service
- Steps in Automating ML Model Training
- Informational Resources
- Krasamo’s Managed Services for Machine Learning
- Machine Learning in Python
- Python Frameworks
Machine Learning Strategy
For many years, traditional businesses have been transforming digitally through new software and processes—but implementing software is no longer enough. Successful enterprises are now differentiating themselves from competitors through Machine Learning (ML) and Artificial Intelligence (AI) applications.
As we move our business plans to the digital world, monetization is shifting to different business models. Business revenues and scaling are being projected in different forms. Growing the company is always on the minds of C-executives.
How can you grow your business?
Traditional businesses with physical products are now merging to digital services and evolving toward new business scenarios that bring explosive growth opportunities—without boundaries—including the ability to scale worldwide.
Digital goods and services with AI predictive capabilities can benefit from low production and distribution costs. Scaling digitally through software is powerful when incorporating prediction into the equation through machine learning models, business variables may behave differently than in the current scenario.
To scale a business by incorporating AI-driven services (run by software) into its core requires an analysis of responsiveness to uncontrolled variables brought in from external sources.
ML services train data and algorithms specific to the activity and variables that frequently change and impact business effectiveness—which requires retraining algorithms, usually involving people and increasing costs.
Companies that incorporate Machine Learning models usually improve their operating leverage but have to carefully watch the behavior of profit margins as revenues go up or down.
How will your cost structure evolve?
Machine Learning can enable operations with zero human operators (using digital agents instead) as well as software-running operations, automatic learning, and prediction to make decisions in controlled environments (a perfect scenario).
To succeed, the business analyst must frame the problem correctly and become aware of the economic implications and clear outcomes of how Machine Learning will impact the business.
A Machine Learning strategy will ease the performance, scalability, and reliability of ML models and turn them into fully functioning systems.
Krasamo’s Machine Learning engineering experts blend methods, techniques, best practices, and capabilities to develop and train Machine Learning models that uniquely fit each customer’s organization.
How to Develop and Train ML Models
Training a Machine Learning model means finding the parameters that will fit the training data when running the algorithm to make predictions.
The first step to developing an ML model is to identify the business case and success criteria. Once these are determined, then a plan for achieving the objectives of the project can be created.
Identify and Extract Data
In this step, you need to explore and manage the quality and quantity of data. Therefore, understand how the model will work on real-world data, and select and integrate data from several sources. Having good data is vital, as the model will learn from this data.
Select a name for your model. Add a description of the model. Attach appropriate tags to your model. (Tags are designed to make your model searchable.)
Data analysis is a process that provides a clear understanding of the data and prepares the data to fit the characteristics of the model. Analyzing data helps identify the feature engineering for the ML model. Data preparation is automated for trying out combinations.
Collect and Prepare the Data
In this step, data from several sources is searched and divided (data splits) for training, test sets, and validation. Data has to be modified, assembled, cleaned, and labeled. Also, all duplicates must be removed and all errors corrected. (It’s worth writing functions specifically for this purpose.)
Select Your Machine Learning Model
There are many models from which to choose, depending on the problem. This step includes algorithms of prediction, classification, clustering, deep learning, linear regression, and so on. Experiment with several models from various algorithm categories to find the best performing model. Perform transformations and feature engineering.
Train Your Machine Model
The goal of training is to answer a question or make a prediction correctly as often as possible. This step involves training the datasets to operate smoothly. Algorithms and techniques are involved in training the machine model, such as training with hyperparameters to find the optimal, for example. Also, the model training code is developed during this step.
Model Quality Evaluation
The evaluation step includes selecting the metrics and conducting the actual evaluation. In this step, you evaluate the machine models on the test set, running the pipeline to transform the data, cross-validation, anomaly detection, novelty detection, quality evaluation, etc.
Model Performance and Adjustment
Machine learning models are tested in real-life situations. For testing purposes, data is divided into the training set and the test set. The model is tested using the “test set” on new instances and checked for generalization errors to determine how the model is overfitting or underfitting the data. At this point, the model should be predicting and ready for deployment.
Launch, Monitor, and Maintain
Now the model must be deployed to the production environment. ML models need to be monitored with humans or machines to check their performance in order to ensure an optimum data pipeline and data quality. This step involves confirming that the system works with current data and is set to trigger alerts if the model needs retraining.
Machine Learning and Scalability
In ML applications, scalability is often a primary concern. Businesses need applications that can maintain the same efficiency when the workload grows, updating to new data and producing predictions. For example, predictions in the stock market happen every millisecond. So, scalability requires building an effective data pipeline. These pipelines should be flexible enough to accommodate many data as well as the high processing velocity required by new ML applications.
Therefore, it is essential to make the Machine Learning infrastructure interoperable to incorporate it into the existing and future resources. For this, we need to set up scalable ML applications to increase the systems’ overall performance. Scalable ML algorithms are a class of algorithms that can deal with any amount of data without consuming a tremendous number of resources, such as memory. The primary purpose of scalable algorithms is to allow fast computations for massive data sets.
Developing a Scalable Machine Learning Pipeline: An ML Training Pipeline
The task: Develop an ML training pipeline that describes ML workflows for each ML model and keeps a repository for each model candidate.
Completing this task will involve a number of steps:
- Selecting a suitable processor: Selecting the proper hardware plays a critical role in scalability. In many cases, for ML, the best CPU is a GPU (Graphical Processing Unit), as they are comparatively faster—and quicker in distributing computations across GPU servers. A traditional CPU (Central Processing Unit) is not ideal for large-scale machine learning. Beyond CPU and GPU, there are TPUs (Tensor Processing Units), Google’s custom-developed application-specific integrated circuits, which are used to accelerate machine learning workloads.
- Data collection: Data collection is the process of gathering and measuring data that needs to be formatted, cleansed, reduced, and rescaled to make it better. Data storage is also essential in order to develop solutions for the business problem at hand.
- The Input Pipeline: Data is entered into the learning algorithm as a set of inputs/pipelines. At this stage, the data can be divided (data segregation) into subsets and components, transformed, and then fed it into the system. The data set is then added to the pipeline. Data processing components are self-contained and usually run asynchronously.
- Model Training: A significant step for scalability, model training includes exploring and cleaning the data as well as engineering new features. Training the model means learning good values for all the weights and the bias from labeled examples.Steps include:
- Inputting training data source
- Naming the data attribute that contains the target to be predicted
- Preparing data transformation instructions
- Training parameters to control the learning algorithm
- Writing a script that runs automatically to train the model
- Testing and validation
Parameters in Machine Learning:
- Optimization: The final step is to optimize. To achieve that, optimal parameters must be identified. Optimization algorithms check the input parameter to a function that results in the minimum or maximum output. Evaluation of data performance provides estimates on how the model is overfitting or underfitting the training data.
- Machine Learning overfitting happens when the model is too complex and doesn’t perform correctly with the training data, giving generalization errors during the validation set. The ML team should find a balance between bias and variances.
- Machine Learning underfitting is when the model is too simple for the intended dataset and predictions are inaccurate. The model needs additional parameters or, perhaps, more parameters should be added to the features of the algorithm.
- Testing Model: Before the final deployment happens, it is necessary to test whether the input data is in line with the output data and is yielding maximum predictions. These tests—cross-validation, error analysis, and data validation—should be done and monitored multiple times.
- Model monitoring: The model monitoring phase ensures active performance monitoring to catch errors in production, detect degradation, and ensure consistency of inference data and metrics with business objectives. Monitoring code checks live performance of the models.
- Deploying: Deployment is when the prediction service goes live. Effectively deploying Machine Learning models is an art rather than a science. Think of deploying frequent model versions of the entire ML system. Cloud hosting platforms are ideal for this purpose. Models can be deployed as a web service and used by web applications that use REST API, deployed as an API for prediction, or containerized. Deploying a model on the cloud using Google AI Cloud Platform provides scalability and load balancing with a perfect environment for running TensorFlow models.
Operationalization of Machine Learning Models
Operationalization involves building an integrated ML system that is continuously in production.
This process refers to the deployment of ML models to be consumed by business applications to predict the target class or target value of a classification/regression problem. In simple terms, operationalization refers to how the designed and developed models will keep running so that those functions or applications that integrate continue to perform with a high level of accuracy and stability.
Operationalization and managing ML models are complex tasks that require maintaining and continuously testing and validating the code, data, and models, managing performance and experiments, and maintaining the accuracy of the algorithms and data to avoid degradation of the models.
A model’s success depends on data collection, data engineering, and data science, which means collecting the correct data, understanding the effort needed to extract data from the system, and applying the engineering principles necessary to format, transform, and get the data ready from a data science standpoint. All three lead to the deployment of data.
Challenges with Machine Learning Systems
- Requires continuous testing and validation of data and models
- Needs to integrate external solutions to the ML pipeline
- Model analysis and retraining of candidate ML model
- MLOps culture implementation throughout the machine learning development lifecycle
- Process and metadata management
- Automate the steps of building the ML model
- Data collection and verification
- Retraining models due to degradation or decay (tracking statistics and looking for emerging patterns)
- Tracking experiments to reproduce and reuse during the lifecycle
Machine Learning Datasets
A dataset is a synchronized collection of data. It can be in the form of a table, a schema, or an object.
This process is, once again, an integral part of the Machine Learning process. In simple terms, the word “datasets” means a collection of data. You can usually find a dataset in a tabular form. Each column denotes a specific variable, and each row signifies a specific member of the dataset.
Types of Datasets
There are typically five types of datasets:
- Numerical dataset: A numerical dataset consists of only the numbers—for example, the height and weight of a person, the total number of pages in a notebook, the number of apples in a grocery store, etc.
- Correlation dataset: This type denotes the relationship of variables or attributes between datasets. For example, people that exercise regularly have lower cholesterol levels.
- Multivariate dataset: This dataset consists of multiple variables, such as the length, width, and height of a rectangular box, for example.
- Categorical dataset: This type of dataset consists of the characteristics of a defined object or person, such as an individual’s gender and relationship status, for example.
- Bivariate dataset: This type consists of two variables, such as students’ academic scores and their ages, for example.
Datasets are updated regularly. The most significant benefit of using a dataset is that it helps the user obtain desired data in an organized manner, retrieving the required information quickly from a massive collection of data, thereby saving time and executing tasks more quickly.
Machine Learning with Elasticsearch
Using Elasticsearch in Machine Learning is a helpful method to find data insights, patterns, anomalies, and outliers. This tool enables users to stock, hunt, and inspect significant volumes of data very quickly.
Algorithms are applied to data in order to find metadata, uncovering information and identifying causes that make sense in context when updating ML models. Data is ingested and applied to the ML models.
The benefit of having Elasticsearch is being able to perform and combine many different kinds of complex queries for all types of data and retrieve complex summaries. Data is stored in JSON documents searchable in near real-time with powerful REST APIs.
Advantages of Elasticsearch:
- High-speed performance
- All data is stored in a JSON format document, a widely accepted web format, and supports auto-completion and instant search.
How to Set Up Elasticsearch on a Windows Platform
- Java must be installed (Version 7 or higher).
- Install the Elasticsearch zip file from the website and unzip the file. Go to the Bin folder and run the Elasticsearch for a batch file.
- An output will occur on the screen, and Elasticsearch is installed.
ML Pipeline Automation for a Prediction Service
Machine Learning systems usually create a multistep pipeline that trains and validates models manually. Still, once your team learns and develops a workable model, the ML pipeline matures and must be automated. Automation speeds up new model training and implementations.
An ML development and operations culture (MLOps) is needed in order to solve the challenges involved in keeping the ML systems in production continuously. DevOps teams evolve to integrate all the elements with the CI/CD environment in order to automate the building, testing, and deployment of ML pipelines.
Continuous training (CT) automation and experimentation in model architecture, feature engineering, and tuning hyperparameters are then added to the operations.
Steps in Automating ML Model Training
- Evaluation—Analyze how to automate the steps of the ML training pipeline to achieve continuous training models from new data for faster iteration and readiness.
- Exploratory Data Analysis (EDA)—Data analysis is a manual step to understand the data for building the model before making assumptions. (The model analysis is also a manual task.)
- Build and Test—Try algorithms and models and develop the source code for the ML pipeline steps to automate. Build, test, and package components. Source code is sent to the repository. Many types of testing and verifications are performed on the training models.
- Modular Components—Components, code, packages, artifacts, and executables are shared for reusability.
- Automated Data Validation—Ensures that the expected data behaviors, patterns, and expected features comply with data schema. (Watch trigger alerts.)
- Automated Model Validation—With the trained model, test a dataset to verify the quality of the prediction results, checking the values of the variances. Validation is performed offline, and then online validation is handled with canary deployment.
- ML Metadata—Metadata is stored when the ML pipeline is executed; the metadata is then used to compare versions during model validation. This process also helps in evaluating metrics, debugging errors, and finding anomalies.
- Triggers—Monitoring code is written to check the ML system’s performance and trigger response alerts when detecting changes in data that feed the training model.
- Feature Store—A feature store is a repository of features for training and serving that allows the reuse of feature sets with metadata that can be fetched automatically in a batch to the prediction service.
- Verify the Integration and Deploy—Verify that configurations are correct for integrating with the target environment (APIs, REST API, etc.) and deploy artifacts. Before deploying, check resources and IT infrastructure.
- Schedule Automatic Execution—The trained model is pushed to the registry.
- Prediction Service Model—The model is deployed and working with live data.
- Continuous Monitoring – Apply continuous testing and verifications to validate and ensure performance.
With Krasamo, the process of automating Machine Learning pipelines involves the gradual transition from manual to semi-automated to fully automated.
Navigating the timeline for testing and deploying implementations includes a number of different skills and processes. Our expert teams are ready to provide a consultation for your business today. Contact Krasamo Sales.
- TensorFlow Extended (TFX): An end-to-end platform for creating and managing an ML pipeline
- TFX: Production ML with TensorFlow in 2020 (TF Dev Summit ’20)
- Deploy ML Pipelines with Kubernetes Using Kubeflow
- Check a Pipeline Code Sample
Krasamo’s Managed Services for Machine Learning
You can build and deploy ML models and manage your Machine Learning operations (MLOps) with a collaborative partnership with Krasamo. Our expert teams will help implement your ML operations through third-party services such as AWS or Google.
This modality will simplify and help manage your workflows, thereby avoiding infrastructure management tasks. Building and managing ML pipelines requires significant effort, so partnering with Krasamo for managed services will help your business boost productivity.
Machine Learning Managed Services Solutions with Krasamo
- Build ML pipeline using TensorFlow
- Evaluate and monitor model performance
- Metadata management (metadata tracking)
- Track artifacts and lineage
- Collaboration capabilities
- Feature engineering
- Track data and performance (notifications)
Machine Learning in Python
Python is particularly suitable for building Machine Learning models and applications. Intuitive and easy to read, it is supported by an extensive collection of ML libraries and frameworks.
Production-ready Python frameworks include:
Get Started on Heroku with Python
Google Machine Learning with TensorFlow APIs Crash Course
Machine learning has become an integral part of business operations in the digital age, together with large amounts of data and computing power.
Have you been wondering how to power your products and features through Machine Learning? Or how to transform your business in disruptive environments? Machine Learning is particularly suitable for products that require solving complex problems and typically demand high human involvement for fine-tuning and analyzing large amounts of data. Machine Learning can help product managers improve products and product offerings by mining data from ML algorithms to find new patterns from predictions that were previously unknown or difficult to see.
A successful Machine Learning strategy emphasizes business issues to solve, builds a business case, and puts users at the center while applying the relevant technical aspects to the project.
A Krasamo business analyst can run an ROI analysis and cost analysis (fixed vs. variable) for you with projections of Machine Learning models and simulated algorithm running. Want to add Machine Learning to your data? Or discover which Machine Learning algorithm to use? Or perform an ML model training simulation? Krasamo has a team with expertise in Machine Learning models ready to meet your requirements.