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Architect and build the full machine learning lifecycle with AWS: An end-to-end Amazon SageMaker demo

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In this tutorial, we will walk through the entire machine learning (ML) lifecycle and show you how to architect and build an ML use case end to end using Amazon SageMaker. Amazon SageMaker provides a rich set of capabilities that enable data scientists, machine learning engineers, and developers to prepare, build, train, and deploy ML models rapidly and with ease. For our use case, we have chosen an automobile claims fraud detection example.

We will initially provide an architectural walkthrough of the various portions of the ML lifecycle and then point to the code that builds each section of the lifecycle on SageMaker.

To get started, data scientists use an experimental process to explore various data preparation tasks, in some cases engineering features, and eventually settle on a standard way of doing so. Then they embark on a more repeatable and scalable process of automating stages of this process, until the model provides the necessary levels of performance (such as accuracy, F1 score, and precision). Then they package this process in a repeatable, automated, and scalable ML pipeline.

The following diagram illustrates the manual investigative and the automated operational workflows.

New capabilities required for new tasks in the ML lifecycle

At a high level, the ML lifecycle looks like the following diagram.

The general phases of the ML lifecycle are data preparation, train and tune, and deploy and monitor, with inference being when we actually serve the model up with new data for inference.

As ML evolves and matures in the industry, we see an increased need for activities that support various facets of scaling of ML tasks and artifacts; making the artifacts that are the outputs of each task consistently standardized, more accessible, more transparent, and therefore more governable. In addition, each of these activities needs to scale from an exploratory activity to a consistent, automated and scalable activity via automated pipelines.

In the detailed preceding ML Lifecycle diagram, the red boxes represent comparatively newer concepts and tasks that are now deemed important to include in, and run in a scalable, operational, and production-oriented (vs. research-oriented) environment.

These newer lifecycle tasks and their corresponding Amazon SageMaker capabilities include the following:

  • Data wrangling – We use SageMaker Data Wrangler for cleaning, normalizing, transforming and encoding data, as well as joining datasets. The output of SageMaker Data Wrangler is data transformation code that works with SageMaker Processing, SageMaker Pipelines, SageMaker Feature Store, or with Pandas in a plain Python script. Feature engineering can now be done faster and easier, with SageMaker Data Wrangler where we have a GUI-based environment and can generate code that can be used for the subsequent phases of the ML lifecycle.
  • Detecting bias – With SageMaker Clarify, in the data prep or training phases, we can detect pre-training (data bias) and post-training bias (model bias). At the inference phase, SageMaker Clarify gives us the ability to provide interpretability and explainability of the predictions by providing insight into which factors were most influential in coming up with the prediction.
  • Feature Store (offline) – After we complete our feature engineering, encoding, and transformations, we can standardize features offline in SageMaker Feature Store, to be used as input features for training models.
    SageMaker Feature Store allows you to create offline feature groups that keep all the historical data and can be used as inputs to training.
    Note that Features can be ingested from a feature processing pipeline into the online feature store and will then get replicated to the offline store. The offline store could be used to run batch inference as well. Thus, the online feature store can also be used as input for training.
  • Artifact lineage: We can use SageMaker ML Lineage Tracking to associate all the artifacts (such as data, models, and parameters) with a trained model to produce metadata that is stored in a model registry. In addition, tracking human in the loop actions such as model approvals and deployments further facilitates the process of ML governance.
  • Model Registry: The SageMaker Model Registry stores the metadata around all the artifacts that you include in the process of creating your models, along with the trained models themselves in a model registry. Later, we can use human approval to note that the model is ready for production. This feeds into the next phase of deploy and monitor.
  • Inference and Feature Store (online): SageMaker Feature Store provides for low latency (up to single digit milliseconds) and high throughput reads for serving our model with new incoming data.
  • Pipelines: After we experiment and decide on the various options in the lifecycle (such as which transforms to apply to our features, determine imbalance or bias in the data, which algorithms to choose to train with, or which hyperparameters are giving us the best performance metrics), we can automate the various tasks across the lifecycle using SageMaker Pipelines.
    This lets us streamline the otherwise cumbersome manual processes into an automated ML pipeline. To build this pipeline, we will prepare some data (customers and claims) by ingesting the data into SageMaker Data Wrangler and apply various transformations in SageMaker Data Wrangler within SageMaker Studio.  SageMaker Data Wrangler creates .flow files. We will use these transformation definitions as a starting point for our automated pipeline and go through the ML Lifecycle all the way to deploying the model to a SageMaker Hosted Endpoint. Note that some use cases may require one, larger, end-to-end pipeline, that does everything. Other use cases may require multiple pipelines, such as the following:
    • A pipeline for all data prep steps.
    • A pipeline for training, tuning, lineage, and depositing into the model registry (which we show in the code associated with this post).
    • Possibly another pipeline for specific inference scenarios (such as real time vs. batch).
    • A pipeline for triggering retraining by using SageMaker Model Monitor to detect model drift or data drift and trigger retraining using, for example, an AWS Lambda

Use case: Fraud detection for auto insurance claims

In this post, we use an auto insurance claim fraud detection use case to demonstrate how you can easily use Amazon SageMaker to predict the probability that an incoming auto claim may be fraudulent.

We dive into the implementation details in these six notebooks, where we demonstrate how you can enhance your effectiveness as a data scientist and ML engineer by using the new Amazon SageMaker services and features (pictured in red in the preceding figure) to solve problems at each stage of the ML lifecycle.

Technical solution overview

Let’s take a look at the services used in the ML lifecycle for implementing our fraud detection use case. Each section has an accompanying notebook on GitHub that you can follow as you read through the explanations in this post.

Wrangling and preprocessing the dataset

We use two synthetic datasets, consisting of customers and claims that we have synthetically generated. We use SageMaker Data Wrangler to ingest, analyze, prepare, and transform each dataset. You can do this in the GUI-based feature available in SageMaker Studio.

Second, we use SageMaker Data Wrangler to export the transformed data as two CSV files that can be picked up in an Amazon Simple Storage Service (Amazon S3) bucket by SageMaker Processing, in order to conduct scalable data preparation and preprocessing.

Storing the features

After SageMaker Processing applies the transformations defined in SageMaker Data Wrangler, we store the normalized features in an offline feature store so the features can be shared and reused consistently across an organization among collaborating data scientists. This standardization is often key to creating a normalized, reusable set of features that can be created, shared, and managed as input into training ML models. You can use this feature consistency across the ML maturity spectrum, whether you are a startup or an advanced organization with a ML Center of Excellence.

Assessing and Mitigating bias, training and tuning

The issues relating to bias detection and fairness in AI have taken a prominent role in ML. Data bias is often inadvertently injected during the data labeling and collection process, and may often be overlooked in the significance of its impact on training a model. SageMaker Clarify is a fully-managed toolkit to identify potential bias within a training dataset or model, explain individual inference results, aggregate these explanations for an entire dataset, integrate with built-in monitoring capabilities to assess production performance, and provide these capabilities across modeling frameworks.

You can use SageMaker Clarify to assess various types of bias. For example, assessing pre-training bias (data) can focus on determining if class imbalance or a variety of other factors are beyond a threshold and therefore may bias the model we seek to train. SageMaker Clarify helps improve your ML models by detecting potential biases prior to training (data bias) and after training, assess post-training bias (model bias) and can also help explain the predictions that models make during inference.

After we implement our bias mitigation strategy, the next step is often to choose a training algorithm and experiment with various ways of tuning it so as to obtain acceptable ML performance metrics such as F1, AUC, or accuracy. For this post, we use the XGBoost algorithm for training our model using the data in the feature store, and evaluate F1 metrics.

We can also check the resulting model’s post-training bias and, when satisfied with both the performance and transparency (bias) metrics, tune the model to get the most out of its performance through hyperparameter optimization.

We can track the lineage of these experiments using Lineage Tracking to track various aspects of the evolution of our experiments including answering questions related to the following:

  • Data – Which dataset did we use?
  • Prep – How did we clean, transform and featurize the data?
  • Training – Which model and training job configuration did we use?
  • Tuning – Which hyperparameters did we use?

During our experimentation, we may have trained many models, from different datasets, prepared with different transformations, each with their own performance metrics and bias metrics. If we like a result, we can look at the artifact lineage associated with it so we can reproduce those results or improve them.

Capturing artifact lineage in experiments

Not only do we want to store our trained models themselves, but also the specific datasets, feature  transformations, preprocessing mechanisms, algorithms, and hyperparameter configurations that were used to produce and optimize the models for governance and reproducibility purposes. We can store that metadata, which tracks the experiment and lineage of the model, with a reference to the data and the model in the SageMaker Model Registry.

Deploying the model to a SageMaker hosted endpoint

After we decide which models should be approved for deployment, we can deploy them to a SageMaker hosted endpoint, where they are ready for serving predictions.

Running predictions on the model using the online feature store

We create models so we can run predictions on them. We can invoke an endpoint directly, since Amazon SageMaker endpoints have load balancers behind them to balance incoming load.

Another common invocation pattern for running inference is the ML Gateway Pattern, where we expose the inference as a service endpoint and invoke it using an Amazon API Gateway. This pattern also allows the benefits of a service oriented architecture exposing a set of ML services as RESTful endpoints. Incoming service requests benefit from being load balanced, cached, and monitored using Amazon API Gateway. Amazon API Gateway then calls an AWS Lambda function which can call the SageMaker endpoint.

In this post, we will serve the endpoint by invoking it in real time using incoming data that is materialized as features in an online feature store. The resulting insurance claim is then designated as fraud or not fraud using the XGBoost trained and tuned model.

Explaining the model’s predictions

We can then inspect why this decision was made and present an explainable narrative to inquisitive parties. For this, we use the explainability features of SageMaker Clarify.

Solution architecture and ML lifecycle workflows

Let’s dive deeper and explore the solution architecture for each of the four workflows for data prep, train and tune, deploy, and finally a pipeline that ties everything together in an automated fashion up to storing the models in a registry.

Manual workflow

Before we automate parts of the lifecycle, we often conduct investigative data science work. This is often carried out in the exploratory data analysis and visualization phases, where we use SageMaker Data Wrangler to figure out what we want to do with our data (visualize, understand, clean, transform, or featurize) to prepare it for training. The following diagram illustrates the flow for the two datasets on SageMaker Data Wrangler.

One of the outputs you can choose in SageMaker Data Wrangler is a Python notebook that distills these activities into a set of functions. The .flow file output contains a set of transformations that provide SageMaker Processing with guidance on what transformations to apply to features. The following screenshot shows the export options from SageMaker Data Wrangler.

We can send this code to SageMaker Processing to create a preprocessing job that prepares our datasets for training in a scalable and reproducible way.

Data prep

The following diagram shows the data prep architecture. The code is available in the notebook 1-data-prep-e2e.ipynb.

In the attached notebook for the data prep stage, we assume all the work was done in SageMaker Data Wrangler and the output is available in the /data folder of the example code, so you can follow the flow of the notebook. You can query, explore, and visualize features using SageMaker Data Wrangler from SageMaker Studio.

You can provide an S3 bucket that contains the results of the SageMaker Data Wrangler job that has output two files: claims.csv and customer.csv. If you want to move on and assume the data prep has been conducted, you can access the preprocessed data in the /data folder containing the files claims_preprocessed.csv (31 features) and customers_preprocessed.csv (19 features). The policy_id and event_time columns in customers_preprocessed.csv are necessary when creating a feature store, which requires a unique identifier for each record and a timestamp.

Dataset features and distribution

You can find the code for exploring the data in the notebook 0-AutoClaimFraudDetection.ipynb.

Here are some sample plots that indicate the nature of the class imbalance and to what features fraud may be correlated.

The dataset is heavily weighted towards male customers.

Fraud is positively correlated with having a greater number of insurers over the past 5 years. Customers who switched insurers more frequently also had more prevalence of fraud.

We loaded the raw data from the S3 bucket and created 10 transforms for claims and 6 for customers.

Transformations and featurizations

For claims, we formatted some strings and encoded several categorical features. See the following code:

Data columns (total 31 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 policy_id 5000 non-null int64 1 incident_severity 5000 non-null float64 2 num_vehicles_involved 5000 non-null int64 3 num_injuries 5000 non-null int64 4 num_witnesses 5000 non-null int64 5 police_report_available 5000 non-null float64 6 injury_claim 5000 non-null int64 7 vehicle_claim 5000 non-null int64 8 total_claim_amount 5000 non-null int64 9 incident_month 5000 non-null int64 10 incident_day 5000 non-null int64 11 incident_dow 5000 non-null int64 12 incident_hour 5000 non-null int64 13 fraud 5000 non-null int64 14 driver_relationship_self 5000 non-null float64 15 driver_relationship_na 5000 non-null float64 16 driver_relationship_spouse 5000 non-null float64 17 driver_relationship_child 5000 non-null float64 18 driver_relationship_other 5000 non-null float64 19 incident_type_collision 5000 non-null float64 20 incident_type_breakin 5000 non-null float64 21 incident_type_theft 5000 non-null float64 22 collision_type_front 5000 non-null float64 23 collision_type_rear 5000 non-null float64 24 collision_type_side 5000 non-null float64 25 collision_type_na 5000 non-null float64 26 authorities_contacted_police 5000 non-null float64 27 authorities_contacted_none 5000 non-null float64 28 authorities_contacted_fire 5000 non-null float64 29 authorities_contacted_ambulance 5000 non-null float64 30 event_time 5000 non-null float64

For customers, we have the following code:

# Column Non-Null Count Dtype --- ------ -------------- ----- 0 policy_id 5000 non-null int64 1 customer_age 5000 non-null int64 2 customer_education 5000 non-null int64 3 months_as_customer 5000 non-null int64 4 policy_deductable 5000 non-null int64 5 policy_annual_premium 5000 non-null int64 6 policy_liability 5000 non-null int64 7 auto_year 5000 non-null int64 8 num_claims_past_year 5000 non-null int64 9 num_insurers_past_5_years 5000 non-null int64 10 customer_gender_male 5000 non-null float64 11 customer_gender_female 5000 non-null float64 12 policy_state_ca 5000 non-null float64 13 policy_state_wa 5000 non-null float64 14 policy_state_az 5000 non-null float64 15 policy_state_or 5000 non-null float64 16 policy_state_nv 5000 non-null float64 17 policy_state_id 5000 non-null float64 18 event_time 5000 non-null float64

Preprocessing

Data is exported from SageMaker Data Wrangler into an S3 bucket. It’s then preprocessed using SageMaker Processing. We assume that the output of the preprocessing job has been deposited in the S3 bucket you provide, or you can find the preprocessed data in the /data folder.

Ingesting the preprocessed data into SageMaker Feature Store

After SageMaker Processing finishes the preprocessing and we have our two CSV data files for claims and customers ready. We have contributed to the standardization of these features by making them discoverable and reusable by ingesting them into SageMaker Feature Store.

SageMaker Feature Store is a centralized store for features and their associated metadata, allowing features to be easily discovered and reused across your organization or team. You have the option of creating an offline feature store (stored in Amazon S3) or an online component stored in a low-latency store, or both. Data is stored in your S3 bucket using a prefixing scheme based on event time. The offline feature store is append-only, which enables you to maintain a historical record of all feature values. Data is stored in the offline store in Parquet format for optimized storage and query access. SageMaker Feature Store supports combining data to produce, train, validate, and test datasets, and allows you to extract data at different points in time.

To store features, we first need to define their feature group. A feature group is the main feature store resource that contains the metadata for all the data stored in Amazon SageMaker Feature Store. A feature group is a logical grouping of features, defined in the feature store, to describe records. A feature group’s definition is composed of a list of feature definitions, a record identifier name, and configurations for its online and offline store.

The online database is optional, but very useful if you need supplemental features to be available at inference. In this section, we create two feature groups for our claims and customers datasets. After inserting the claims and customers data into their respective feature groups, you need to query the offline store with Amazon Athena to build the training dataset.

To ingest data, we first designate a feature group for each type of feature, in this case, one per CSV file. You can ingest data into feature groups in SageMaker Feature Store in one of two ways: streaming or batch. For this post, we use the batch method.

When the offline feature store is ready, a crawler catalogs it and loads the catalog into an Athena table. To construct the train and test datasets, we use a SQL query to join the claims and customers tables that were created in Athena.

Training and tuning

The code for this section can be found in the following notebooks: 2-lineage-train-assess-bias-tune-registry-e2e.ipynb and 3-mitigate-bias-train-model2-registry-e2e.ipynb. The following diagram illustrates the workflow for the bias check, training, tuning, lineage, and model registry stages.

We write the train and test split datasets to our designated S3 bucket, and create an XGBoost estimator to train our fraud detection model with a fraud or no fraud logistic target. Prior to starting the SageMaker training job using the built-in XGBoost algorithm, we set the XGBoost hyperparameters. You can learn more about XGBoost’s Learning Task Parameters, Tree Booster Parameters.

We take the opportunity to track all the artifacts or entities involved with the training job so we can track the lineage of the model. This is done by importing several sagemaker.lineage components. See the following code:

from sagemaker.lineage import context, artifact, association, action.

Lineage Tracking provides us with visibility into the code, training data,  and model artifacts that we then associate with association_type='Produced' and association_type='ContributesTo', which links what contributed to and what produced a given artifact in the process.

We also assess degrees of pre-training and post-training bias using SageMaker Clarify. Pre-training metrics show a variety of possible preexisting bias in our dataset. Post-training metrics show bias in the predictions resulting from the model. We use analysis_config.json to specify which groups we want to check bias across and which metrics we want to show.

We assess two metrics: the difference in positive proportions in predicted labels (DPPL) and if a class imbalance exists in the data. For our use case, we measure this on the gender feature, which indicates if we have more male customers than female customers. Results indicate a slight bias in our model measured by the DPPL metric.

Deploying and serving the model

The code for this section can be found in the notebook 4-deploy-run-inference-e2e.ipynb. The following diagram shows the deploy and serve stage for real-time inference.

We choose the model that conforms to our metrics best, with an appropriate tolerance of F1 score, and deploy that model by creating a SageMaker training job that results in deploying the model to a SageMaker hosted endpoint.

When the endpoint is in place, we use the online feature store to run inference on the endpoint.

Interpreting the results

The following plot shows the data features and their relative impact on the prediction, using SHAP values.

We can trace back much of our interpretation of inference results to the features that had the most impact on the model output.

Creating an automated workflow using SageMaker Pipelines

The code for this section can be found in the notebook 5-pipeline-e2e.ipynb.

After we complete a few iterations of our manual exploratory data science and are happy with the outcomes of our cleansing, transformations, and featurizations, we may want to create an automated workflow using SageMaker Pipelines, so we can scale and don’t have to go through this manual process every time.

The following diagram shows our end-to-end automated MLOps pipeline, which includes eight steps:

  1. Preprocess the claims data with SageMaker Data Wrangler.
  2. Preprocess the customers data with SageMaker Data Wrangler.
  3. Create a dataset and train/test split.
  4. Train the XGBoost algorithm.
  5. Create the model.
  6. Run bias metrics with SageMaker Clarify.
  7. Register the model.
  8. Deploy the model.

Conclusion

In December 2020, AWS announced many new AI and ML services and features. In this post, we discussed how to build an end to end fraud detection use case for auto insurance claims using most of the these new capabilities including: SageMaker Data Wrangler for feature transformation, SageMaker Processing for preprocessing data, SageMaker Feature Store (offline) for standardization of features, SageMaker Clarify for bias detection pre- and post-training and for post-inference interpretability of results, ML Lineage Tracking to help with governance of ML artifacts, SageMaker Model Registry for model and metadata storage, and SageMaker Pipelines for end to end workflow automation. Check out additional information about each of these services by clicking on the following product page links.


About the Author

Ali ArsanjaniDr. Ali Arsanjani is the Tech Sector AI/ML Leader and Principal Architect for AI/ML Specialist Solution Architects with AWS helping customers make optimal use of ML using the AWS platform. He is also an adjunct faculty member at San Jose State University, teaching and advising students in the Data Science Masters Programs.

Source: https://aws.amazon.com/blogs/machine-learning/architect-and-build-the-full-machine-learning-lifecycle-with-amazon-sagemaker/

Artificial Intelligence

Deep Learning vs Machine Learning: How an Emerging Field Influences Traditional Computer Programming

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When two different concepts are greatly intertwined, it can be difficult to separate them as distinct academic topics. That might explain why it’s so difficult to separate deep learning from machine learning as a whole. Considering the current push for both automation as well as instant gratification, a great deal of renewed focus has been heaped on the topic.

Everything from automated manufacturing worfklows to personalized digital medicine could potentially grow to rely on deep learning technology. Defining the exact aspects of this technical discipline that will revolutionize these industries is, however, admittedly much more difficult. Perhaps it’s best to consider deep learning in the context of a greater movement in computer science.

Defining Deep Learning as a Subset of Machine Learning

Machine learning and deep learning are essentially two sides of the same coin. Deep learning techniques are a specific discipline that belong to a much larger field that includes a large variety of trained artificially intelligent agents that can predict the correct response in an equally wide array of situations. What makes deep learning independent of all of these other techniques, however, is the fact that it focuses almost exclusively on teaching agents to accomplish a specific goal by learning the best possible action in a number of virtual environments.

Traditional machine learning algorithms usually teach artificial nodes how to respond to stimuli by rote memorization. This is somewhat similar to human teaching techniques that consist of simple repetition, and therefore might be thought of the computerized equivalent of a student running through times tables until they can recite them. While this is effective in a way, artificially intelligent agents educated in such a manner may not be able to respond to any stimulus outside of the realm of their original design specifications.

That’s why deep learning specialists have developed alternative algorithms that are considered to be somewhat superior to this method, though they are admittedly far more hardware intensive in many ways. Subrountines used by deep learning agents may be based around generative adversarial networks, convolutional neural node structures or a practical form of restricted Boltzmann machine. These stand in sharp contrast to the binary trees and linked lists used by conventional machine learning firmware as well as a majority of modern file systems.

Self-organizing maps have also widely been in deep learning, though their applications in other AI research fields have typically been much less promising. When it comes to defining the deep learning vs machine learning debate, however, it’s highly likely that technicians will be looking more for practical applications than for theoretical academic discussion in the coming months. Suffice it to say that machine learning encompasses everything from the simplest AI to the most sophisticated predictive algorithms while deep learning constitutes a more selective subset of these techniques.

Practical Applications of Deep Learning Technology

Depending on how a particular program is authored, deep learning techniques could be deployed along supervised or semi-supervised neural networks. Theoretically, it’d also be possible to do so via a completely unsupervised node layout, and it’s this technique that has quickly become the most promising. Unsupervised networks may be useful for medical image analysis, since this application often presents unique pieces of graphical information to a computer program that have to be tested against known inputs.

Traditional binary tree or blockchain-based learning systems have struggled to identify the same patterns in dramatically different scenarios, because the information remains hidden in a structure that would have otherwise been designed to present data effectively. It’s essentially a natural form of steganography, and it has confounded computer algorithms in the healthcare industry. However, this new type of unsupervised learning node could virtually educate itself on how to match these patterns even in a data structure that isn’t organized along the normal lines that a computer would expect it to be.

Others have proposed implementing semi-supervised artificially intelligent marketing agents that could eliminate much of the concern over ethics regarding existing deal-closing software. Instead of trying to reach as large a customer base as possible, these tools would calculate the odds of any given individual needing a product at a given time. In order to do so, it would need certain types of information provided by the organization that it works on behalf of, but it would eventually be able to predict all further actions on its own.

While some companies are currently relying on tools that utilize traditional machine learning technology to achieve the same goals, these are often wrought with privacy and ethical concerns. The advent of deep structured learning algorithms have enabled software engineers to come up with new systems that don’t suffer from these drawbacks.

Developing a Private Automated Learning Environment

Conventional machine learning programs often run into serious privacy concerns because of the fact that they need a huge amount of input in order to draw any usable conclusions. Deep learning image recognition software works by processing a smaller subset of inputs, thus ensuring that it doesn’t need as much information to do its job. This is of particular importance for those who are concerned about the possibility of consumer data leaks.

Considering new regulatory stances on many of these issues, it’s also quickly become something that’s become important from a compliance standpoint as well. As toxicology labs begin using bioactivity-focused deep structured learning packages, it’s likely that regulators will express additional concerns in regards to the amount of information needed to perform any given task with this kind of sensitive data. Computer scientists have had to scale back what some have called a veritable fire hose of bytes that tell more of a story than most would be comfortable with.

In a way, these developments hearken back to an earlier time when it was believed that each process in a system should only have the amount of privileges necessary to complete its job. As machine learning engineers embrace this paradigm, it’s highly likely that future developments will be considerably more secure simply because they don’t require the massive amount of data mining necessary to power today’s existing operations.

Image Credit: toptal.io

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Source: https://datafloq.com/read/deep-learning-vs-machine-learning-how-emerging-field-influences-traditional-computer-programming/13652

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Artificial Intelligence

Extra Crunch roundup: Tonal EC-1, Deliveroo’s rocky IPO, is Substack really worth $650M?

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For this morning’s column, Alex Wilhelm looked back on the last few months, “a busy season for technology exits” that followed a hot Q4 2020.

We’re seeing signs of an IPO market that may be cooling, but even so, “there are sufficient SPACs to take the entire recent Y Combinator class public,” he notes.

Once we factor in private equity firms with pockets full of money, it’s evident that late-stage companies have three solid choices for leveling up.

Seeking more insight into these liquidity options, Alex interviewed:

  • DigitalOcean CEO Yancey Spruill, whose company went public via IPO;
  • Latch CFO Garth Mitchell, who discussed his startup’s merger with real estate SPAC $TSIA;
  • Brian Cruver, founder and CEO of AlertMedia, which recently sold to a private equity firm.

After recapping their deals, each executive explains how their company determined which flashing red “EXIT” sign to follow. As Alex observed, “choosing which option is best from a buffet’s worth of possibilities is an interesting task.”

Thanks very much for reading Extra Crunch! Have a great weekend.

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist


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The Tonal EC-1

Image Credits: Nigel Sussman

On Tuesday, we published a four-part series on Tonal, a home fitness startup that has raised $200 million since it launched in 2018. The company’s patented hardware combines digital weights, coaching and AI in a wall-mounted system that sells for $2,995.

By any measure, it is poised for success — sales increased 800% between December 2019 and 2020, and by the end of this year, the company will have 60 retail locations. On Wednesday, Tonal reported a $250 million Series E that valued the company at $1.6 billion.

Our deep dive examines Tonal’s origins, product development timeline, its go-to-market strategy and other aspects that combined to spark investor interest and customer delight.

We call this format the “EC-1,” since these stories are as comprehensive and illuminating as the S-1 forms startups must file with the SEC before going public.

Here’s how the Tonal EC-1 breaks down:

We have more EC-1s in the works about other late-stage startups that are doing big things well and making news in the process.

What to make of Deliveroo’s rough IPO debut

Why did Deliveroo struggle when it began to trade? Is it suffering from cultural dissonance between its high-growth model and more conservative European investors?

Let’s peek at the numbers and find out.

Kaltura puts debut on hold. Is the tech IPO window closing?

The Exchange doubts many folks expected the IPO climate to get so chilly without warning. But we could be in for a Q2 pause in the formerly scorching climate for tech debuts.

Is Substack really worth $650M?

A $65 million Series B is remarkable, even by 2021 standards. But the fact that a16z is pouring more capital into the alt-media space is not a surprise.

Substack is a place where publications have bled some well-known talent, shifting the center of gravity in media. Let’s take a look at Substack’s historical growth.

RPA market surges as investors, vendors capitalize on pandemic-driven tech shift

Business process organization and analytics. Business process visualization and representation, automated workflow system concept. Vector concept creative illustration

Image Credits: Visual Generation / Getty Images

Robotic process automation came to the fore during the pandemic as companies took steps to digitally transform. When employees couldn’t be in the same office together, it became crucial to cobble together more automated workflows that required fewer people in the loop.

RPA has enabled executives to provide a level of automation that essentially buys them time to update systems to more modern approaches while reducing the large number of mundane manual tasks that are part of every industry’s workflow.

E-commerce roll-ups are the next wave of disruption in consumer packaged goods

Elevated view of many toilet rolls on blue background

Image Credits: Javier Zayas Photography (opens in a new window) / Getty Images

This year is all about the roll-ups, the aggregation of smaller companies into larger firms, creating a potentially compelling path for equity value. The interest in creating value through e-commerce brands is particularly striking.

Just a year ago, digitally native brands had fallen out of favor with venture capitalists after so many failed to create venture-scale returns. So what’s the roll-up hype about?

Hack takes: A CISO and a hacker detail how they’d respond to the Exchange breach

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What did COVID do to all our models?

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What did COVID do to all our models?

An interview with Dean Abbott and John Elder about change management, complexity, interpretability, and the risk of AI taking over humanity.


By Heather Fyson, KNIME

What did COVID do to all our models?

After the KNIME Fall Summit, the dinosaurs went back home… well, switched off their laptops. Dean Abbott and John Elder, longstanding data science experts, were invited to the Fall Summit by Michael to join him in a discussion of The Future of Data Science: A Fireside Chat with Industry Dinosaurs. The result was a sparkling conversation about data science challenges and new trends. Since switching off the studio lights, Rosaria has distilled and expanded some of the highlights about change management, complexity, interpretability, and more in the data science world. Let’s see where it brought us.

What is your experience with change management in AI, when reality changes and models have to be updated? What did COVID do to all our models?

 
[Dean] Machine Learning (ML) algorithms assume consistency between past and future. When things change, the models fail. COVID has changed our habits, and therefore our data. Pre-COVID models struggle to deal with the new situation.

[John] A simple example would be the Traffic layer on Google Maps. After lockdowns hit country after country in 2020, Google Maps traffic estimates were very inaccurate for a while. It had been built on fairly stable training data but now that system was thrown completely out of whack.

How do you figure out when the world has changed and the models don’t work anymore?

 
[Dean] Here’s a little trick I use: I partition my data by time and label records as “before” and “after”. I then build a classification model to discriminate the “after” vs. the “before” from the same inputs the model uses. If the discrimination is possible, then the “after” is different from the “before”, the world has changed, the data has changed, and the models must be retrained.

How complicated is it to retrain models in projects, especially after years of customization?

 
[John] Training models is usually the easiest step of all! The vast majority of otherwise successful projects die in the implementation phase. The greatest time is spent in the data cleansing and preparation phase. And the most problems are missed or made in the business understanding / project definition phase. So if you understand what the flaw is and can obtain new data and have the implementation framework in place, creating a new model is, by comparison, very straightforward.

Based on your decades-long experience, how complex is it to put together a really functioning Data Science application?

 
[John] It can vary of course, by complexity. Most of our projects get functioning prototypes at least in a few months. But for all, I cannot stress enough the importance of feedback: You have to talk to people much more often than you want to. And listen! We learn new things about the business problem, the data, or constraints, each time. Not all us quantitative people are skilled at speaking with humans, so it often takes a team. But the whole team of stakeholders has to learn to speak the same language.

[Dean] It is important to talk to our business counterpart. People fear change and don’t want to change the current status. One key problem really is psychological. The analysts are often seen as an annoyance. So, we have to build the trust between the business counterpart and the analytics geeks. The start of a project should always include the following step: Sync up domain experts / project managers, the analysts, and the IT and infrastructure (DevOps) team so everyone is clear on the objectives of the project and how it will be executed. Analysts are number 11 on the top 10 list of people they have to see every day! Let’s avoid embodying data scientist arrogance: “The business can’t understand us/our techniques, but we know what works best”. What we don’t understand, however, are the domains experts are actually experts in the domain we are working in! Translation of data science assumptions and approaches into language that is understood by the domain experts is key!

The latest trend now is deep learning, apparently it can solve everything. I got a question from a student lately, asking “why do we need to learn other ML algorithms if deep learning is the state of the art to solve data science problems”?

 
[Dean] Deep learning sucked a lot of the oxygen out of the room. It feels so much like the early 1990s when neural networks ascended with similar optimism! Deep Learning is a set of powerful techniques for sure, but they are hard to implement and optimize. XGBoost, Ensembles of trees, are also powerful but currently more mainstream. The vast majority of problems we need to solve using advanced analytics really don’t require complex solutions, so start simple; deep learning is overkill in these situations. It is best to use the Occam’s razor principle: if two models perform the same, adopt the simplest.

About complexity. The other trend, opposite to deep learning, is ML interpretability. Here, you greatly (excessively?) simplify the model in order to be able to explain it. Is interpretability that important?

 
[John] I often find myself fighting interpretability. It is nice, sure, but often comes at too high a cost of the most important model property: reliable accuracy. But many stakeholders believe interpretability is essential, so it becomes a barrier for acceptance. Thus, it is essential to discover what kind of interpretability is needed. Perhaps it is just knowing what the most important variables are? That’s doable with many nonlinear models. Maybe, as with explaining to credit applicants why they were turned down, one just needs to interpret outputs for one case at a time? We can build a linear approximation for a given point. Or, we can generate data from our black box model and build an “interpretable” model of any complexity to fit that data.

Lastly, research has shown that if users have the chance to play with a model – that is, to poke it with trial values of inputs and see its outputs, and perhaps visualize it – they get the same warm feelings of interpretability. Overall, trust – in the people and technology behind the model – is necessary for acceptance, and this is enhanced by regular communication and by including the eventual users of the model in the build phases and decisions of the modeling process.

[Dean] By the way KNIME Analytics Platform has a great feature to quantify the importance of the input variables in a Random Forest! The Random Forest Learner node outputs the statistics of candidate and splitting variables. Remember that, when you use the Random Forest Learner node.

There is an increase in requests for explanations of what a model does. For example, for some security classes, the European Union is demanding verification that the model doesn’t do what it’s not supposed to do. If we have to explain it all, then maybe Machine Learning is not the way to go. No more Machine Learning?

 
[Dean]  Maybe full explainability is too hard to obtain, but we can achieve progress by performing a grid search on model inputs to create something like a score card describing what the model does. This is something like regression testing in hardware and software QA. If a formal proof what models are doing is not possible, then let’s test and test and test! Input Shuffling and Target Shuffling can help to achieve a rough representation of the model behavior.

[John] Talking about understanding what a model does, I would like to raise the problem of reproducibility in science. A huge proportion of journal articles in all fields — 65 to 90% — is believed to be unreplicable. This is a true crisis in science. Medical papers try to tell you how to reproduce their results. ML papers don’t yet seem to care about reproducibility. A recent study showed that only 15% of AI papers share their code.

Let’s talk about Machine Learning Bias. Is it possible to build models that don’t discriminate?

 
[John] (To be a nerd for a second, that word is unfortunately overloaded. To “discriminate” in the ML world word is your very goal: to make a distinction between two classes.) But to your real question, it depends on the data (and on whether the analyst is clever enough to adjust for weaknesses in the data): The models will pull out of the data the information reflected therein. The computer knows nothing about the world except for what’s in the data in front of it. So the analyst has to curate the data — take responsibility for those cases reflecting reality. If certain types of people, for example, are under-represented then the model will pay less attention to them and won’t be as accurate on them going forward. I ask, “What did the data have to go through to get here?” (to get in this dataset) to think of how other cases might have dropped out along the way through the process (that is survivor bias). A skilled data scientist can look for such problems and think of ways to adjust/correct for them.

[Dean] The bias is not in the algorithms. The bias is in the data. If the data is biased, we’re working with a biased view of the world. Math is just math, it is not biased.

Will AI take over humanity?!

 
[John] I believe AI is just good engineering. Will AI exceed human intelligence? In my experience anyone under 40 believes yes, this is inevitable, and most over 40 (like me, obviously): no! AI models are fast, loyal, and obedient. Like a good German Shepherd dog, an AI model will go and get that ball, but it knows nothing about the world other than the data it has been shown. It has no common sense. It is a great assistant for specific tasks, but actually quite dimwitted.

[Dean] On that note, I would like to report two quotes made by Marvin Minsky in 1961 and 1970, from the dawn of AI, that I think describe well the future of AI.

“Within our lifetime some machines may surpass us in general intelligence” (1961)

“In three to eight years we’ll have a machine with the intelligence of a human being” (1970)

These ideas have been around for a long time. Here is one reason why AI will not solve all the problems: We’re judging its behavior based on one number, one number only! (Model error.) For example, predictions of stock prices over the next five years, predicted by building models using root mean square error as the error metric, cannot possibly paint the full picture of what the data are actually doing and severely hampers the model and its ability to flexibly uncover the patterns. We all know that RMSE is too coarse of a measure. Deep Learning algorithms will continue to get better, but we also need to get better at judging how good a model really is. So, no! I do not think that AI will take over humanity.

We have reached the end of this interview. We would like to thank Dean and John for their time and their pills of knowledge. Let’s hope we meet again soon!

About Dean Abbott and John Elder

What did COVID do to all our models Dean Abbott is Co-Founder and Chief Data Scientist at SmarterHQ. He is an internationally recognized expert and innovator in data science and predictive analytics, with three decades of experience solving problems in omnichannel customer analytics, fraud detection, risk modeling, text mining & survey analysis. Included frequently in lists of pioneering data scientists and data scientists, he is a popular keynote speaker and workshop instructor at conferences worldwide, also serving on Advisory Boards for the UC/Irvine Predictive Analytics and UCSD Data Science Certificate programs. He is the author of Applied Predictive Analytics (Wiley, 2014) and co-author of The IBM SPSS Modeler Cookbook (Packt Publishing, 2013).


What did COVID do to all our models John Elder founded Elder Research, America’s largest and most experienced data science consultancy in 1995. With offices in Charlottesville VA, Baltimore MD, Raleigh, NC, Washington DC, and London, they’ve solved hundreds of challenges for commercial and government clients by extracting actionable knowledge from all types of data. Dr. Elder co-authored three books — on practical data mining, ensembles, and text mining — two of which won “book of the year” awards. John has created data mining tools, was a discoverer of ensemble methods, chairs international conferences, and is a popular workshop and keynote speaker.


 
Bio: Heather Fyson is the blog editor at KNIME. Initially on the Event Team, her background is actually in translation & proofreading, so by moving to the blog in 2019 she has returned to her real passion of working with texts. P.S. She is always interested to hear your ideas for new articles.

Original. Reposted with permission.

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Source: https://www.kdnuggets.com/2021/04/covid-do-all-our-models.html

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The AI Trends Reshaping Health Care

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Click to learn more about author Ben Lorica.

Applications of AI in health care present a number of challenges and considerations that differ substantially from other industries. Despite this, it has also been one of the leaders in putting AI to work, taking advantage of the cutting-edge technology to improve care. The numbers speak for themselves: The global AI in health care market size is expected to grow from $4.9 billion in 2020 to $45.2 billion by 2026. Some major factors driving this growth are the sheer volume of health care data and growing complexities of datasets, the need to reduce mounting health care costs, and evolving patient needs.

Deep learning, for example, has made considerable inroads into the clinical environment over the last few years. Computer vision, in particular, has proven its value in medical imaging to assist in screening and diagnosis. Natural language processing (NLP) has provided significant value in addressing both contractual and regulatory concerns with text mining and data sharing. Increasing adoption of AI technology by pharmaceutical and biotechnology companies to expedite initiatives like vaccine and drug development, as seen in the wake of COVID-19, only exemplifies AI’s massive potential.

We’re already seeing amazing strides in health care AI, but it’s still the early days, and to truly unlock its value, there’s a lot of work to be done in understanding the challenges, tools, and intended users shaping the industry. New research from John Snow Labs and Gradient Flow, 2021 AI in Healthcare Survey Report, sheds light on just this: where we are, where we’re going, and how to get there. The global survey explores the important considerations for health care organizations in varying stages of AI adoption, geographies, and technical prowess to provide an extensive look into the state of AI in health care today.               

One of the most significant findings is around which technologies are top of mind when it comes to AI implementation. When asked what technologies they plan to have in place by the end of 2021, almost half of respondents cited data integration. About one-third cited natural language processing (NLP) and business intelligence (BI) among the technologies they are currently using or plan to use by the end of the year. Half of those considered technical leaders are using – or soon will be using – technologies for data integration, NLP, business intelligence, and data warehousing. This makes sense, considering these tools have the power to help make sense of huge amounts of data, while also keeping regulatory and responsible AI practices in mind.

When asked about intended users for AI tools and technologies, over half of respondents identified clinicians among their target users. This indicates that AI is being used by people tasked with delivering health care services – not just technologists and data scientists, as in years past. That number climbs even higher when evaluating mature organizations, or those that have had AI models in production for more than two years. Interestingly, nearly 60% of respondents from mature organizations also indicated that patients are also users of their AI technologies. With the advent of chatbots and telehealth, it will be interesting to see how AI proliferates for both patients and providers over the next few years.

In considering software for building AI solutions, open-source software (53%) had a slight edge over public cloud providers (42%). Looking ahead one to two years, respondents indicated openness to also using both commercial software and commercial SaaS. Open-source software gives users a level of autonomy over their data that cloud providers can’t, so it’s not a big surprise that a highly regulated industry like health care would be wary of data sharing. Similarly, the majority of companies with experience deploying AI models to production choose to validate models using their own data and monitoring tools, rather than evaluation from third parties or software vendors. While earlier-stage companies are more receptive to exploring third-party partners, more mature organizations are tending to take a more conservative approach.                      

Generally, attitudes remained the same when asked about key criteria used to evaluate AI solutions, software libraries or SaaS solutions, and consulting companies to work with.Although the answers varied slightly for each category,technical leaders considered no data sharing with software vendors or consulting companies, the ability to train their own models, and state-of-the art accuracy as top priorities. Health care-specific models and expertise in health care data engineering, integration, and compliance topped the list when asked about solutions and potential partners. Privacy, accuracy, and health care experience are the forces driving AI adoption. It’s clear that AI is poised for even more growth, as data continues to grow and technology and security measures improve. Health care, which can sometimes be seen as a laggard for quick adoption, is taking to AI and already seeing its significant impact. While its approach, the top tools and technologies, and applications of AI may differ from other industries, it will be exciting to see what’s in store for next year’s survey results.

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