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Training, debugging and running time series forecasting models with the GluonTS toolkit on Amazon SageMaker

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Time series forecasting is an approach to predict future data values by analyzing the patterns and trends in past observations over time. Organizations across industries require time series forecasting for a variety of use cases, including seasonal sales prediction, demand forecasting, stock price forecasting, weather forecasting, financial planning, and inventory planning.

Various cutting edge algorithms are available for time series forecasting, such as DeepAR, the seq2seq family, and LSTNet (Long- and Short-term Time-series network). The machine learning (ML) process for time series forecasting is often time-consuming, resource intensive, and requires comparative analysis across multiple parameter combinations and datasets to reach the required precision and accuracy with your models. To determine the best model, developers and data scientists need to:

  1. Select algorithms and hyperparameters.
  2. Build, configure, train, and tune models.
  3. Evaluate these models and compare metrics captured at training and evaluation time.
  4. Visualize results.
  5. Repeat the preceding steps multiple times before choosing the optimal model.

The infrastructure management associated with the scaling required at training time for such an iterative process may lead to undifferentiated heavy lifting for the developers and data scientists involved.

In this post and the associated notebook, we show you how to address these challenges by providing an approach with detailed steps to set up and run time series forecasting models at scale using Gluon Time Series (GluonTS) on Amazon SageMaker. GluonTS is a Python toolkit for probabilistic time series modeling, built around Apache MXNet. GluonTS provides utilities for loading and iterating over time series datasets, state-of-the-art models ready to be trained, and building blocks to define your own models and quickly experiment with different solutions.

Solution overview

We first show you how to set up GluonTS on SageMaker using the MXNet estimator, then train multiple models using SageMaker Experiments, use SageMaker Debugger to mitigate suboptimal training, evaluate model performance, and finally generate time series forecasts. We walk you through the following steps:

  1. Prepare the time series dataset.
  2. Create the algorithm and hyperparameters combinatorial matrix.
  3. Set up the GluonTS training script.
  4. Set up a SageMaker experiment and trials.
  5. Create the MXNet estimator.
  6. Set up an experiment with Debugger enabled to automatically stop suboptimal jobs.
  7. Train and validate models.
  8. Evaluate metrics and select a winning candidate.
  9. Run time series forecasts.

Prerequisites

Before getting started, you must set up your SageMaker notebook instance and install the required packages. Complete the following steps:

  1. Onboard to Amazon SageMaker Studio with the Quick start procedure.
  2. When you create an AWS Identity and Access Management (IAM) role to the notebook instance, be sure to specify access to Amazon Simple Storage Service (Amazon S3). You can choose any S3 bucket or specify the S3 bucket you want to enable access to. You can use the AWS-managed policies AmazonSageMakerFullAccess to grant general access to SageMaker services.

You can use the AWS-managed policies AmazonSageMakerFullAccess to grant general access to SageMaker services.

  1. When the user is created and active, choose Open Studio.

When the user is created and active, choose Open Studio.

  1. On the Studio landing page, on the File drop-down menu, choose New.
  2. Choose Terminal.

Choose Terminal.

  1. In the terminal, enter the following code:
    git clone https://github.com/aws-samples/amazon-sagemaker-gluonts-timeseriesforecasting-with-debuggerandexperiments

  2. Open the notebook by choosing Amazon SageMaker GluonTS time series forecasting.ipynb
  3. Install the required packages by entering the following code:
    ! pip install gluonts
    ! pip install --upgrade sagemaker
    ! pip install sagemaker-experiments
    ! pip install --upgrade smdebug-rulesconfig

Preparing the time series dataset

For this post, we use the individual household electric power consumption dataset. (Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.) The usage data is aggregated hourly.

Let’s download and store the usage data as a DataFrame:

import pandas as pd
url = "https://raw.githubusercontent.com/aws-samples/amazon-forecast-samples/master/notebooks/common/data/item-demand-time.csv"
df = pd.read_csv(url, header=None, names=["date", "usage", "client"])

Define the S3 bucket and folder locations to store the test and training data. This should be within the same Region as the notebook instance, training, and hosting.

Now let’s divide the raw data into train and test samples and save them in their respective S3 folder locations using the Pandas DataFrame query function. We can check first few entries of the train and test dataset. Both datasets should have the same fields, as in the following code:

df_train = df.query('date <= "2014-31-10 11:00:00"').copy()
df_train.to_csv("train.csv")
s3_client.upload_file("train.csv", "glutonts-electricity", pref+"/train.csv")
df_train.head() df_test = df.query('date >= "2014-1-11 12:00:00"').copy()
df_test.to_csv("test.csv")
s3_client.upload_file("test.csv", "glutonts-electricity", pref+"/test.csv")
df_test.head()

Creating the algorithm and hyperparameters combinatorial matrix

GluonTS comes with pre-built probabilistic forecasting models. Instead of simply predicting a single point estimate, probabilistic forecasting assigns a probability to every outcome. GluonTS provides a number of ready to use algorithm packages for training probabilistic forecasting models. When you select an algorithm, you can configure the hyperparameters to control the learning process during model training.

SageMaker supports bring your own model using Script mode. You can use SageMaker to train and deploy a model using custom MXNet code. The Amazon SageMaker Python SDK MXNet estimators and models and the SageMaker open-source MXNet container make writing a MXNet script and running it in SageMaker easier.

In this post, we train using four different models from the GluonTS toolkit: 

DeepAR – A supervised learning algorithm for forecasting scalar time series using Recurrent Neural Networks (RNN)

SFeedFwd (Simple Feedforward) – A supervised learning algorithm where information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any), and to the output nodes in the forward direction

LSTNet – A multivariate time series forecasting model that uses the combination of Convolution Neural Network (CNN) and the RNN to find short-term local dependency patterns among variables and then find long-term patterns for time series trends

seq2seq (sequence-to-sequence learning) – A family of architectures; for this post we use the MQCNNEstimator of the seq2seq family to set up our training

All these algorithms are already part of GluonTS; we use it to quickly iterate and experiment over different models.

A trainer defines how a network is going to be trained. Let’s define a trainer object using a Pandas DataFrame that has the base list of algorithms, different epochs, learning rate, and hyperparameter combinations that we want to define for our training runs. We use the product function to derive combinations of these parameters from the base set into separate rows in the DataFrame. Each row corresponds to a training job configuration that we subsequently pass to the MXNet estimator to run the training job. See the following code:

import pandas as pd
d = {'epochs': [5, 10, 15, 20], 'algo': ["DeepAR", "SFeedFwd", "lstnet", "seq2seq"], 'num_batches_per_epoch': [10, 15, 20, 25], 'learning_rate':[1e-2, 1e-3, 1e-3, 1e-3], 'hybridize':[False, True, True, True]}
df_hps = pd.DataFrame(data=d)
df_hps['prediction_length'] = [30, 60, 75, 100] from itertools import product prod = product(df_hps['epochs'].unique(), df_hps['algo'].unique(), df_hps['num_batches_per_epoch'].unique(), df_hps['learning_rate'].unique(), df_hps['hybridize'].unique(), df_hps['prediction_length'].unique())
df_hps_combo = pd.DataFrame([list(p) for p in prod], columns=list(['epochs', 'algo', 'num_batches_per_epoch', 'learning_rate', 'hybridize', 'prediction_length']))
df_hps_combo['jobnumber'] = df_hps_combo.index

Setting up the GluonTS training script

We use a Python entry script to import the necessary GluonTS libraries, set up the GluonTS estimators using the model packages for our algorithms of interest, and pass in our algorithm and hyperparameter preferences from the MXNet estimator we set up in the notebook. The script uses the train and test data files we uploaded to Amazon S3 to create the corresponding GluonTS datasets for training and evaluation. When training is complete, the script runs an evaluation to generate metrics and store them using the SageMaker Debugger hook function, which we use to choose a winning model. For further analysis, the metrics are also available via the SageMaker trial component analytics (which we discuss later in this post). The model is then serialized for storage and future retrieval.

For more details, refer to the entry script available in the GitHub repo. From the accompanying notebook, you can also run the cell in Step 3 to review the script.

Setting up a SageMaker experiment

SageMaker Experiments automatically tracks the inputs, parameters, configurations, and results of your iterations as trials. You can assign, group, and organize these trials into experiments. SageMaker Experiments is integrated with SageMaker Studio, providing a visual interface to browse your active and past experiments, compare trials on key performance metrics, and identify the best-performing models. SageMaker Experiments comes with its own Experiments SDK, which makes the analytics capabilities easily accessible in SageMaker notebooks. Because SageMaker Experiments enables tracking of all the steps and artifacts that go into creating a model, you can quickly revisit the origins of a model when you’re troubleshooting issues in production or auditing your models for compliance verifications. You can create your experiment with the following code:

from smexperiments.experiment import Experiment
sagemaker_boto_client = boto3.client("sagemaker") Experiment.create(
experiment_name=experiment_name,
description="Timeseries models",
sagemaker_boto_client=sagemaker_boto_client)

For each job, we define a new trial component within that experiment. Next we define an experiment config, which is a dictionary that we pass into the fit() method later on. This ensures that the training job is associated with that experiment and trial. For the full code block for this step, refer to the accompanying notebook.

Creating the MXNet estimator

You can run MXNet training scripts on SageMaker by creating an MXNet estimator. Before setting up the actual training runs with the parameter sweep, let’s test the MXNet estimator using a single set of an algorithm and hyperparameters, in this case DeepAR. See the following code:

import sagemaker
from sagemaker.mxnet import MXNet mxnet_estimator = MXNet(entry_point='blog_train_algos.py', role=sagemaker.get_execution_role(), train_instance_type='ml.m5.large', train_instance_count=1, framework_version='1.7.0', py_version='py3', hyperparameters={'bucket': bucket, 'seq': 10, 'algo': "DeepAR", 'freq': "D", 'prediction_length': 30, 'epochs': 10, 'learning_rate': 1e-3, 'hybridize': False, 'num_batches_per_epoch': 10, })

After specifying our estimator with all the necessary hyperparameters, we can train it using our training dataset. We train it by invoking the fit() method of the estimator. We pass the location of train and test data as well as the experiment configuration. The training algorithm returns a fitted model (or a predictor in GluonTS parlance) that we can use to construct forecasts. See the following code:

mxnet_estimator.fit({"train": s3_train_channel, "test": s3_test_channel}, experiment_config=experiment_config, wait=False)

You can review the job parameters and metrics from the trial component view in SageMaker Studio (see the following screenshot).

You can review the job parameters and metrics from the trial component view in SageMaker Studio

Setting up an experiment with SageMaker Debugger enabled to automatically stop suboptimal jobs

We ran a parameter sweep and created lots of different configurations when we ran the product function to generate the hyperparameters combinatorial matrix in the second step above. Doing so may produce parameter combinations that lead to suboptimal models. We can use SageMaker Debugger to tune our experiment. Debugger automatically captures data from the model training and provides built-in rules that check for conditions such as overfitting and vanishing gradients. We can then specify actions to automatically stop training jobs ahead of time that would otherwise produce low-quality models. Some of the models in our experiment use RNNs that can suffer from the vanishing gradient problem. We use the Debugger tensor variance rule, which allows us to specify an upper and lower bound on the gradient values. We also specify the action StopTraining, which stops a training job when the rule triggers. By default, Debugger collects data with an interval of 500 steps. For this post, our training dataset is small and our models only train for a few minutes, so we can decrease the save interval. We create a custom collection, where we collect gradients at an interval of 5:

mxnet_estimator.fit({"train": s3_train_channel, "test": s3_test_channel}, experiment_config=experiment_config, wait=False)
from sagemaker.debugger import DebuggerHookConfig, CollectionConfig debugger_hook_config = DebuggerHookConfig( collection_configs=[ CollectionConfig( name="custom_collection", parameters={ "include_regex": "(.*gradient)(?!.*featureembedder)(.*weight)", "start_step": "10", "save_interval": "5"})])

We then define a new SageMaker experiment to run the trials based on the combinatorial matrix we created earlier. When the experiment is complete, we can determine how many seconds it ran. We then define a helper function to compute the billable seconds and how many training jobs were stopped automatically. This setup is especially useful if you run a parameter sweep with training jobs that train for hours. In our case, each job only trained for less than 10 minutes. Until the Debugger data is uploaded, fetched, and downloaded into the processing job, a few minutes may pass, so the potential cost reduction is less for smaller training jobs.

#name of experiment
timestep = datetime.now()
timestep = timestep.strftime("%d-%m-%Y-%H-%M-%S")
experiment_name = timestep + "-timeseries-models" #create experiment
Experiment.create( experiment_name=experiment_name, description="Timeseries models", sagemaker_boto_client=sagemaker_boto_client)

See the accompanying notebook for the full code in this section.

Training and validating models

In a previous step, we trained one model. Now we iterate over all possible combinations of hyperparameters and algorithms that we generated using the product function with the SageMaker Debugger rules enabled to detect suboptimal training jobs and stop them automatically if the rule fails. A SageMaker experiment consists of multiple trials with a related objective. A trial consists of one or more trial components, such as a data preprocessing job and a training job. Each trial component within our experiment corresponds to one training job run. SageMaker Studio provides an experiments browser that you can use to view lists of experiments, trials, and trial components (see the following screenshot).

SageMaker Studio provides an experiments browser that you can use to view lists of experiments, trials, and trial components

You can choose one of these entities to view detailed information about the entity or choose multiple entities for comparison (see the following screenshot).

You can choose one of these entities to view detailed information about the entity or choose multiple entities for comparison

For more information, see View Experiments, Trials, and Trial Components. For the code block for this step, refer to the accompanying notebook. If you would like to tune further, you can also run a hyperparameter tuning job. Amazon SageMaker automatic model tuning, also known as hyperparameter tuning, finds the best version of a model by running many training jobs on your dataset using the algorithm and ranges of hyperparameters that you specify. Please refer to the SageMaker documentation for an example.

When our experiment completes its training run, we can check to see if any training jobs were stopped automatically. As we can see in the following screenshot, Debugger identified that the tensor variance of three jobs exceeded the gradient limits we set up in the rule and stopped them.

Debugger identified that the tensor variance of three jobs exceeded the gradient limits we set up in the rule and stopped them.

Evaluating metrics and selecting a winning candidate

When the training jobs are running, we can use the experiments view in Studio or the ExperimentAnalytics module to track the status of our training jobs and their metrics. In the training script, we used the SageMaker Debugger function save_scalar to store metrics such as mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE) in the experiment. We can access the recorded metrics via the ExperimentAnalytics function and convert it to a Pandas DataFrame:

from sagemaker.analytics import ExperimentAnalytics trial_component_analytics = ExperimentAnalytics(experiment_name=experiment_name)
df = trial_component_analytics.dataframe()
new_df = df[['epochs', 'learning_rate', 'hybridize', 'num_batches_per_epoch','prediction_length','scalar/MASE_GLOBAL - Min', 'scalar/MSE_GLOBAL - Min', 'scalar/RMSE_GLOBAL - Min', 'scalar/MAPE_GLOBAL - Min']]
mape_min = new_df['scalar/MAPE_GLOBAL - Min'].min()
df_winner = new_df[new_df['scalar/MAPE_GLOBAL - Min'] == mape_min]

Now let’s review the job summary and find the job with better forecasting accuracy. Different metrics define their own expectation function. The MAPE is a common statistical measure used for forecast accuracy. From our results matrix, let’s find the prediction job that has the lowest MAPE value to get the winning model. The following screenshot shows that the lowest MAPE in our run is 0.07373.

The following screenshot shows that the lowest MAPE in our run is 0.07373.

Download the winning model with the following code:

s3 = boto3.client("s3")
windir = "gluonts/blog-models/"+str(df_winner['jobnumber'].item())+"/" def downloadDirectoryFroms3(bucket, windir): s3_resource = boto3.resource('s3') bucket = s3_resource.Bucket(bucket) for obj in bucket.objects.filter(Prefix = windir): print(obj.key) if not os.path.exists(os.path.dirname(windir)): os.makedirs(os.path.dirname(windir)) bucket.download_file(obj.key, obj.key) # save to same path downloadDirectoryFroms3(bucket, windir)

Restore the predictor with the following code:

from gluonts.model.predictor import Predictor
path = pathlib.Path(windir) winning_predictor = Predictor.deserialize(path)

Running time series forecasts

When we use the GluonTS predictor to run our forecasts, we request predictions for the quantiles we’re interested in. A forecast at a specified quantile is used to provide a prediction interval, which is a range of possible values to account for forecast uncertainty. For example, a forecast at the 0.5 quantile estimates a value that is lower than the observed value 50% of the time. Our predictions return a QuantileForecast object that contains time series ordered in array for quantiles and mean. See the following code:

import matplotlib.pyplot as plt
from gluonts.dataset.common import ListDataset
plt.rcParams['figure.figsize'] = (20.0, 6.0) # run forecast
startdate = '2014-11-01 01:00:00'
test_pred = ListDataset( [{"start": startdate, "target": raw_df.query('date >= "2014-11-01 01:00:00" and client == "client_12"').copy()['usage'], "item_id": 'client_12'}], freq = "1H"
) pred = winning_predictor.predict(test_pred)
for test_entry, forecast in zip(test_pred, pred): print(forecast.start_date) plt.plot(pd.date_range(start=startdate, periods=30), pd.DataFrame.from_dict(test_entry['target'])[0][:30],color='b') plt.plot(pd.date_range(start=forecast.start_date, periods=df_winner['prediction_length'].item()), forecast.quantile(.3), color='r') #samples contain all 100 quantiles plt.plot(pd.date_range(start=forecast.start_date, periods=df_winner['prediction_length'].item()), forecast.quantile(.5), color='g') #samples contain all 100 quantiles plt.plot(pd.date_range(start=forecast.start_date, periods=df_winner['prediction_length'].item()), forecast.quantile(.7), color='k') #samples contain all 100 quantiles x=pd.date_range(start=forecast.start_date, periods=df_winner['prediction_length'].item()) #samples contain all 100 quantiles y=forecast.quantile(.1) z=forecast.quantile(.9) plt.fill_between(x,y,z,color='g', alpha=0.3)
plt.xticks(rotation=30)
plt.legend(['Usage'], loc = 'lower left')
plt.show()

The blue line in the following forecasted plot represents the historical energy usage for a specific client, and the red, green, and black lines indicate the predicted energy usage at 30%, 50%, and 70% quantiles respectively for that client.

The blue line in the following forecasted plot represents the historical energy usage for a specific client.

For more details, see the GluonTS Model Forecast module.

Conclusion

With SageMaker, it’s easy for every developer and data scientist to set up time series forecasting at scale using the MXNet estimator with the GluonTS toolkit. SageMaker removes the undifferentiated heavy lifting from every step of our ML process, automates infrastructure management, enables us to improve the training efficiency with SageMaker Debugger, and accelerates adoption of ML workflows from months to days. Try out the notebook from our post and let us know your comments and feedback.

References

For more information about GluonTS and algorithms like DeepAR, see the following:


About the Authors

Prem Ranga is an Enterprise Solutions Architect based out of Atlanta, GA. He is part of the Machine Learning Technical Field Community and loves working with customers on their ML and AI journey. Prem is passionate about robotics, is an Autonomous Vehicles researcher, and also built the Alexa-controlled Beer Pours in Houston and other locations.

Nathalie Rauschmayr is an Applied Scientist at AWS, where she helps customers develop deep learning applications.

Mona Mona is an AI/ML Specialist Solutions Architect based out of Arlington, VA. She works with the World Wide Public Sector team and helps customers adopt machine learning on a large scale. She is passionate about NLP and ML explainability areas in AI/ML.

Jana Gnanachandran is an Enterprise Solutions Architect at AWS, focusing on Data Analytics, AI/ML, and Serverless platforms. He helps AWS customers across numerous industries to design and build highly scalable, data-driven, analytical solutions to accelerate their cloud adoption. In his spare time, he enjoys playing tennis, 3D printing, and photography.

Source: https://aws.amazon.com/blogs/machine-learning/training-debugging-and-running-time-series-forecasting-models-with-the-gluonts-toolkit-on-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

Coinsmart. Beste Bitcoin-Börse in Europa
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.

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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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Source: https://www.dataversity.net/the-ai-trends-reshaping-health-care/

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