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Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines




We recently announced Amazon SageMaker Pipelines, the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). SageMaker Pipelines is a native workflow orchestration tool for building ML pipelines that take advantage of direct Amazon SageMaker integration. Three components improve the operational resilience and reproducibility of your ML workflows: pipelines, model registry, and projects. These workflow automation components enable you to easily scale your ability to build, train, test, and deploy hundreds of models in production, iterate faster, reduce errors due to manual orchestration, and build repeatable mechanisms.

SageMaker projects introduce MLOps templates that automatically provision the underlying resources needed to enable CI/CD capabilities for your ML development lifecycle. You can use a number of built-in templates or create your own custom template. You can use SageMaker Pipelines independently to create automated workflows; however, when used in combination with SageMaker projects, the additional CI/CD capabilities are provided automatically. The following screenshot shows how the three components of SageMaker Pipelines can work together in an example SageMaker project.

The following screenshot shows how the three components of SageMaker Pipelines can work together in an example SageMaker project.

This post focuses on using an MLOps template to bootstrap your ML project and establish a CI/CD pattern from sample code. We show how to use the built-in build, train, and deploy project template as a base for a customer churn classification example. This base template enables CI/CD for training ML models, registering model artifacts to the model registry, and automating model deployment with manual approval and automated testing.

MLOps template for building, training, and deploying models

We start by taking a detailed look at what AWS services are launched when this build, train, and deploy MLOps template is launched. Later, we discuss how to modify the skeleton for a custom use case.

To get started with SageMaker projects, you must first enable it on the Amazon SageMaker Studio console. This can be done for existing users or while creating new ones. For more information, see SageMaker Studio Permissions Required to Use Projects.

For more information, see SageMaker Studio Permissions Required to Use Projects.

In SageMaker Studio, you can now choose the Projects menu on the Components and registries menu.

In SageMaker Studio, you can now choose the Projects menu on the Components and registries menu.

On the projects page, you can launch a preconfigured SageMaker MLOps template. For this post, we choose MLOps template for model building, training, and deployment.

On the projects page, you can launch a preconfigured SageMaker MLOps template.

Launching this template starts a model building pipeline by default, and while there is no cost for using SageMaker Pipelines itself, you will be charged for the services launched. Cost varies by Region. A single run of the model build pipeline in us-east-1 is estimated to cost less than $0.50. Models approved for deployment incur the cost of the SageMaker endpoints (test and production) for the Region using an ml.m5.large instance.

After the project is created from the MLOps template, the following architecture is deployed.

After the project is created from the MLOps template, the following architecture is deployed.

Included in the architecture are the following AWS services and resources:

  • The MLOps templates that are made available through SageMaker projects are provided via an AWS Service Catalog portfolio that automatically gets imported when a user enables projects on the Studio domain.
  • Two repositories are added to AWS CodeCommit:
    • The first repository provides scaffolding code to create a multi-step model building pipeline including the following steps: data processing, model training, model evaluation, and conditional model registration based on accuracy. As you can see in the file, this pipeline trains a linear regression model using the XGBoost algorithm on the well-known UCI Abalone dataset. This repository also includes a build specification file, used by AWS CodePipeline and AWS CodeBuild to run the pipeline automatically.
    • The second repository contains code and configuration files for model deployment, as well as test scripts required to pass the quality gate. This repo also uses CodePipeline and CodeBuild, which run an AWS CloudFormation template to create model endpoints for staging and production.
  • Two CodePipeline pipelines:
    • The ModelBuild pipeline automatically triggers and runs the pipeline from end to end whenever a new commit is made to the ModelBuild CodeCommit repository.
    • The ModelDeploy pipeline automatically triggers whenever a new model version is added to the model registry and the status is marked as Approved. Models that are registered with Pending or Rejected statuses aren’t deployed.
  • An Amazon Simple Storage Service (Amazon S3) bucket is created for output model artifacts generated from the pipeline.
  • SageMaker Pipelines uses the following resources:
    • This workflow contains the directed acyclic graph (DAG) that trains and evaluates our model. Each step in the pipeline keeps track of the lineage and intermediate steps can be cached for quickly re-running the pipeline. Outside of templates, you can also create pipelines using the SDK.
    • Within SageMaker Pipelines, the SageMaker model registry tracks the model versions and respective artifacts, including the lineage and metadata for how they were created. Different model versions are grouped together under a model group, and new models registered to the registry are automatically versioned. The model registry also provides an approval workflow for model versions and supports deployment of models in different accounts. You can also use the model registry through the boto3 package.
  • Two SageMaker endpoints:
    • After a model is approved in the registry, the artifact is automatically deployed to a staging endpoint followed by a manual approval step.
    • If approved, it’s deployed to a production endpoint in the same AWS account.

All SageMaker resources, such as training jobs, pipelines, models, and endpoints, as well as AWS resources listed in this post, are automatically tagged with the project name and a unique project ID tag.

Modifying the sample code for a custom use case

After your project has been created, the architecture described earlier is deployed and the visualization of the pipeline is available on the Pipelines drop-down menu within SageMaker Studio.

To modify the sample code from this launched template, we first need to clone the CodeCommit repositories to our local SageMaker Studio instance. From the list of projects, choose the one that was just created. On the Repositories tab, you can select the hyperlinks to locally clone the CodeCommit repos.

On the Repositories tab, you can select the hyperlinks to locally clone the CodeCommit repos.

ModelBuild repo

The ModelBuild repository contains the code for preprocessing, training, and evaluating the model. The sample code trains and evaluates a model on the UCI Abalone dataset. We can modify these files to solve our own customer churn use case. See the following code:

|-- codebuild-buildspec.yml
|-- pipelines
| |-- abalone
| | |--
| | |--
| | |--
| | |--
| |--
| |--
| |--
| |--
| |--
|-- sagemaker-pipelines-project.ipynb
|-- setup.cfg
|-- tests
| --
|-- tox.ini

We now need a dataset accessible to the project.

  1. Open a new SageMaker notebook inside Studio and run the following cells:
    !unzip -o
    !mv "Data sets" Datasets import os
    import boto3
    import sagemaker
    prefix = 'sagemaker/DEMO-xgboost-churn'
    region = boto3.Session().region_name
    default_bucket = sagemaker.session.Session().default_bucket()
    role = sagemaker.get_execution_role() RawData = boto3.Session().resource('s3')
    .Bucket(default_bucket).Object(os.path.join(prefix, 'data/RawData.csv'))
    .upload_file('./Datasets/churn.txt') print(os.path.join("s3://",default_bucket, prefix, 'data/RawData.csv'))

  1. Rename the abalone directory to customer_churn. This requires us to modify the path inside codebuild-buildspec.yml as shown in the sample repository. See the following code:
    run-pipeline --module-name pipelines.customer-churn.pipeline 

  1. Replace the code with the customer churn preprocessing script found in the sample repository.
  2. Replace the code with the customer churn pipeline script found in the sample repository.
    1. Be sure to replace the “InputDataUrl” default parameter with the Amazon S3 URL obtained in step 1:
      input_data = ParameterString( name="InputDataUrl", default_value=f"s3://YOUR_BUCKET/RawData.csv",

    2. Update the conditional step to evaluate the classification model:
      # Conditional step for evaluating model quality and branching execution
      cond_lte = ConditionGreaterThanOrEqualTo( left=JsonGet(step=step_eval, property_file=evaluation_report, json_path="binary_classification_metrics.accuracy.value"), right=0.8

    One last thing to note is the default ModelApprovalStatus is set to PendingManualApproval. If our model has greater than 80% accuracy, it’s added to the model registry, but not deployed until manual approval is complete.

  1. Replace the code with the customer churn evaluation script found in the sample repository. One piece of the code we’d like to point out is that, because we’re evaluating a classification model, we need to update the metrics we’re evaluating and associating with trained models:
    report_dict = { "binary_classification_metrics": { "accuracy": { "value": acc, "standard_deviation" : "NaN" }, "auc" : { "value" : roc_auc, "standard_deviation": "NaN" }, },
    } evaluation_output_path = '//'
    with open(evaluation_output_path, 'w') as f: f.write(json.dumps(report_dict))

The JSON structure of these metrics are required to match the format of sagemaker.model_metrics for complete integration with the model registry. 

ModelDeploy repo

The ModelDeploy repository contains the AWS CloudFormation buildspec for the deployment pipeline. We don’t make any modifications to this code because it’s sufficient for our customer churn use case. It’s worth noting that model tests can be added to this repo to gate model deployment. See the following code:

├── buildspec.yml
├── endpoint-config-template.yml
├── prod-config.json
├── staging-config.json
└── test ├── buildspec.yml └──

Triggering a pipeline run

Committing these changes to the CodeCommit repository (easily done on the Studio source control tab) triggers a new pipeline run, because an Amazon EventBridge event monitors for commits. After a few moments, we can monitor the run by choosing the pipeline inside the SageMaker project.

After a few moments, we can monitor the run by choosing the pipeline inside the SageMaker project. The following screenshot shows our pipeline details.The following screenshot shows our pipeline details. Choosing the pipeline run displays the steps of the pipeline, which you can monitor.

Choosing the pipeline run displays the steps of the pipeline, which you can monitor.

When the pipeline is complete, you can go to the Model groups tab inside the SageMaker project and inspect the metadata attached to the model artifacts.

When the pipeline is complete, you can go to the Model groups tab inside the SageMaker project and inspect the metadata attached to the model artifacts.

If everything looks good, we can manually approve the model.

This approval triggers the ModelDeploy pipeline and exposes an endpoint for real-time inference.

This approval triggers the ModelDeploy pipeline and exposes an endpoint for real-time inference.

This approval triggers the ModelDeploy pipeline and exposes an endpoint for real-time inference. 


SageMaker Pipelines enables teams to leverage best practice CI/CD methods within their ML workflows. In this post, we showed how a data scientist can modify a preconfigured MLOps template for their own modeling use case. Among the many benefits is that the changes to the source code can be tracked, associated metadata can be tied to trained models for deployment approval, and repeated pipeline steps can be cached for reuse. To learn more about SageMaker Pipelines, check out the website and the documentation. Try SageMaker Pipelines in your own workflows today.

About the Authors

Sean MorganSean Morgan is an AI/ML Solutions Architect at AWS. He previously worked in the semiconductor industry, using computer vision to improve product yield. He later transitioned to a DoD research lab where he specialized in adversarial ML defense and network security. In his free time, Sean is an active open-source contributor and maintainer, and is the special interest group lead for TensorFlow Addons.

Hallie CrosbyHallie Weishahn is an AI/ML Specialist Solutions Architect at AWS, focused on leading global standards for MLOps. She previously worked as an ML Specialist at Google Cloud Platform. She works with product, engineering, and key customers to build repeatable architectures and drive product roadmaps. She provides guidance and hands-on work to advance and scale machine learning use cases and technologies. Troubleshooting top issues and evaluating existing architectures to enable integrations from PoC to a full deployment is her strong suit.

Shelbee EigenbrodeShelbee Eigenbrode is an AI/ML Specialist Solutions Architect at AWS. Her current areas of depth include DevOps combined with ML/AI. She has been in technology for 23 years, spanning multiple roles and technologies. With over 35 patents granted across various technology domains, her passion for continuous innovation combined with a love of all things data turned her focus to the field of d ata science. Combining her backgrounds in data, DevOps, and machine learning, her current passion is helping customers to not only embrace data science but also ensure all models have a path to production by adopting MLOps practices. In her spare time, she enjoys reading and spending time with family, including her fur family (aka dogs), as well as friends.



Ethereum Rises on NFT Boom




Ethereum has been rising for the past two days, increasing by $150 yesterday (10%) to $1697 with it currently trading at $1640.

The ratio reversal coincides with the approval of EIP1559, a cryptoeconomics change that burn fees and will increase capacity both by doubling it as well as by removing miner’s incentives to play with capacity through spamming the network.

Momentum then was added by that tokenized Jack Dorsey tweet, which can be taken as the debut of NFTs because even the BBC covered it.

This space has now seemingly grown considerably from pretty much non existent to about $10 million in trading volumes.

The most expensive NFT, excluding Jack Dorsey’s tweet, is the Formula 1 Grand Prix de Monaco 2020 1A that went for 1,079 eth, worth about $2 million.

That’s the actual race course used in F1® Delta Time, a blockchain game running on Ethereum.

That’s one of quite a few games now running on eth. You can see some footballers’ cards if you browse by recently sold, Decentraland plots of lands, Gods Unchained cards, then there’s an actual planet.

Bitcoin will not be measured against the dollar anymore. “You’d measure it against whatever you’d buying with it, such as planets or solar systems,” said Jesse Powell, Kraken’s founder.

Well, for now these planets are being bought with eth on this 0xUniverse that describes itself as “a revolutionary blockchain-based game. Become an explorer in a galaxy of unique, collectable planets. Colonize them to extract resources and build spacecrafts. Keep expanding your fleet until you’ve uncovered the final mysteries of the universe!”

One mystery they might discover is why is a bull gif worth 112 eth? Apparently this was made prior to the election and changed based on who won it, but $200,000 for that?

Then there’s actual art, like the featured image we’ve chosen above called American censorship.

We’re no art critics, but it’s an image that made us stop for a bit and look at it. Now you can look at it too, and since this is a news article and the image is part of the news, they can’t file copyright complaints as far as we are aware.

So why is someone willing to pay 0.2 eth for this, or $330? Well, because he or she likes it of course, and because it is an original piece of work and he is the owner of it and if he wanted to, he could enforce copyrights where it concerns for business use like selling a print version of this or a card version.

He could sell licensing rights and if the artist of this work becomes famous, he has a proof that it is by this artist and more importantly a proof that this is the original by the artist him/her self.

We could have modified that featured image and obviously we saying we haven’t has plenty of credibility, but having proof of the original itself perhaps has as much value as having the original Mona Lisa rather than a copy.

In any event, the mysteries of art speculation we don’t profess to know, but what seems to be happening is an art experimentation, and many artists must be looking to see just what is going on.

Some of those ‘normie’ artists may well Tik Tok about it and they may even go viral in this day and age where any of us can sometime be the show.

So that frothiness should translate to eth not least because all of this is being priced in eth. The son’s and daughters of traditional art collectors, therefore, may well turn some of their dady’s fiat into eth to speculate on what may well become a new world for art.

Whether it will, remains to be seen, but there’s something new and clearly people seem to be excited about it.

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Artificial Intelligence

Generative Adversarial Transformers: Using GANsformers to Generate Scenes




Author profile picture

@whatsaiLouis Bouchard

I explain Artificial Intelligence terms and news to non-experts.

They basically leverage transformers’ attention mechanism in the powerful StyleGAN2 architecture to make it even more powerful!

Watch the Video:

0:00​ Hey! Tap the Thumbs Up button and Subscribe. You’ll learn a lot of cool stuff, I promise.
0:24​ Text-To-Image translation
0:51​ Examples
5:50​ Conclusion


Complete reference:
Drew A. Hudson and C. Lawrence Zitnick, Generative Adversarial Transformers, (2021), Published on Arxiv., abstract:

“We introduce the GANsformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables longrange interactions across the image, while maintaining computation of linearly efficiency, that can readily scale to high-resolution synthesis. It iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation, and can thus be seen as a generalization of the successful StyleGAN network. We demonstrate the model’s strength and robustness through a careful evaluation over a range of datasets, from simulated multi-object environments to rich real-world indoor and outdoor scenes, showing it achieves state-of-theart results in terms of image quality and diversity, while enjoying fast learning and better data efficiency. Further qualitative and quantitative experiments offer us an insight into the model’s inner workings, revealing improved interpretability and stronger disentanglement, and illustrating the benefits and efficacy of our approach. An implementation of the model is available at​.”

Video Transcript

Note: This transcript is auto-generated by Youtube and may not be entirely accurate.


the basically leveraged transformers


attention mechanism in the powerful stat


gun 2 architecture to make it even more






this is what’s ai and i share artificial


intelligence news every week


if you are new to the channel and would


like to stay up to date please consider


subscribing to not miss any further news


last week we looked at dali openai’s


most recent paper


it uses a similar architecture as gpt3


involving transformers to generate an


image from text


this is a super interesting and complex


task called


text to image translation as you can see


again here the results were surprisingly


good compared to previous


state-of-the-art techniques this is


mainly due to the use of transformers


and a large amount of data this week we


will look at a very similar task


called visual generative modelling where


the goal is to generate a


complete scene in high resolution such


as a road or a room


rather than a single face or a specific


object this is different from delhi


since we are not generating the scene


from a text but from a trained model


on a specific style of scenes which is a


bedroom in this case


rather it is just like style gun that is


able to generate unique and non-existing


human faces


being trained on a data set of real




the difference is that it uses this gan


architecture in a traditional generative


and discriminative way


with convolutional neural networks a


classic gun architecture will have a




trained to generate the image and a




used to measure the quality of the


generated images


by guessing if it’s a real image coming


from the data set


or a fake image generated by the




both networks are typically composed of


convolutional neural networks where the




looks like this mainly composed of down


sampling the image using convolutions to


encode it


and then it up samples the image again


using convolutions to generate a new




of the image with the same style based


on the encoding


which is why it is called style gun then


the discriminator takes the generated


image or


an image from your data set and tries to


figure out whether it is real or




called fake instead they leverage


transformers attention mechanism


inside the powerful stargane 2


architecture to make it


even more powerful attention is an


essential feature of this network


allowing the network to draw global


dependencies between


input and output in this case it’s


between the input at the current step of


the architecture


and the latent code previously encoded


as we will see in a minute


before diving into it if you are not


familiar with transformers or attention


i suggest you watch the video i made


about transformers


for more details and a better


understanding of attention


you should definitely have a look at the


video attention is all you need


from a fellow youtuber and inspiration


of mine janik


kilter covering this amazing paper




so we know that they use transformers


and guns together to generate better and


more realistic scenes


explaining the name of this paper




but why and how did they do that exactly


as for the y they did that to generate


complex and realistic scenes


like this one automatically this could


be a powerful application for many


industries like movies or video games


requiring a lot less time and effort


than having an


artist create them on a computer or even


make them


in real life to take a picture of it




imagine how useful it could be for


designers when coupled with text to


image translation generating many


different scenes from a single text




and pressing a random button they use a


state-of-the-art style gun architecture


because guns are powerful generators


when we talk about the general image


because guns work using convolutional


neural networks


they are by nature using local


information of the pixels


merging them to end up with the general


information regarding the image


missing out on the long range


interaction of the faraway pixel


for the same reason this causes guns to


be powerful generators for the overall


style of the image


still they are a lot less powerful


regarding the quality of the small


details in the generated image


for the same reason being unable to


control the style of localized regions


within the generated image itself this


is why they had the idea to combine


transformers and gans in one


architecture they called


bipartite transformer as gpt3 and many


other papers already proved transformers


are powerful for long-range interactions


drawing dependencies between them and


understanding the context of text


or images we can see that this simply


added attention layers


which is the base of the transformer’s


network in between the convolutional


layers of both the generator and




thus rather than focusing on using


global information and controlling


all features globally as convolutions do


by nature


they use this attention to propagate


information from the local pixels to the


global high level representation


and vice versa like other transformers


applied to images


this attention layer takes the pixel’s


position and the style gun to latent


spaces w


and z the latent space w is an encoding


of the input into an intermediate latent




done at the beginning of the network


denoted here


as a while the encoding z is just the


resulting features of the input at the


current step of the network


this makes the generation much more


expressive over the whole image


especially in generating images


depicting multi-object


scenes which is the goal of this paper


of course this was just an overview of


this new paper by facebook ai research


and stanford university


i strongly recommend reading the paper


to have a better understanding of this


approach it’s the first link in the


description below


the code is also available and linked in


the description as well


if you went this far in the video please


consider leaving a like


and commenting your thoughts i will


definitely read them and answer you


and since there’s still over 80 percent


of you guys that are not subscribed yet


please consider clicking this free


subscribe button


to not miss any further news clearly




thank you for watching



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Newsletter: Elon Musk’s genius + Surgical robots




  • FDA greenlights Memic’s breakthrough robotic surgery
  • Elon Musk’s genius is battery powered
  • Webinar, Mar. 10: Making families affordable
  • D-ID drives MyHeritage deep nostalgia animation
  • Rewire banks $20M, led by OurCrowd
  • Consumer Physics’ SCiO lets you wake up and taste the coffee
  • Medisafe raises $30M for AI that helps people take medication
  • Arcadia guides companies on homeworking energy costs
  • Volvo unit teams with Israel’s Foretellix on autonomous safety
  • Tmura: Startup options help those in need
  • Introductions
  • 1,000 high-tech jobs

FDA greenlights Memic’s breakthrough robotic surgery

The FDA has granted De Novo Marketing Authorization for the breakthrough Hominis robotic surgery system developed by Memic – a notable first. The patented technology allows the surgeon to control tiny, human-like robotic arms that enables procedures currently considered unfeasible, and reduces scarring. “This FDA authorization represents a significant advance in the world of robot-assisted surgery and fulfills an unmet need in the world of robotic gynecological surgery,” said Professor Jan Baekelandt, MD, PhD, a gynecologist at Imelda Hospital in Bonheiden, Belgium, who performed the first hysterectomy using the Hominis system. “Research shows vaginal hysterectomy provides optimal clinical benefits to patients including reduced pain, recovery time and rates of infection. Hominis is the only robot specifically developed for transvaginal surgery and is therefore small and flexible enough to perform surgery through a small incision.” Memic’s strong management team is led by Chairman Maurice R. Ferré MD, who founded Mako Surgical, a surgical robotics company that was sold for $1.65B.

Learn More.

Elon Musk’s genius is battery powered

In 2004, PayPal co-founder Elon Musk took what appeared to be a huge and perhaps reckless gamble. Departing from the seamless technology of the booming online payments business, Musk sank $30 million of his newly-minted internet fortune into a year-old startup that dreamed of transforming a century-old global industry mired in heavy industrial plants, expensive freight and complex global supply chains. Musk’s startup, of course, was Tesla, and the dream was an all-electric car. Today, the electric car is a mass-market reality and Musk’s gamble has sent him powering past Gates and Bezos straight to the Forbes top slot – at least for a while. Musk’s winning bet was not on automobiles, but on energy. The heart of Tesla is not mobility or cars, it is power and battery storage. In 2004, Musk was way ahead of the curve in foreseeing the transformation of energy from fossil fuels to renewables. Today, the signs are everywhere. Read more in my regular ‘Investors on the Frontlines’ column here.

Webinar, Mar. 10: Making families affordable

A key pandemic-driven trend is the acceleration of the fertility tech sector, including egg freezing and IVF, with a market predicted to be worth $37+ billion in less than 10 years. Hear from:

  • Claire Tomkins, PhD, founder and CEO of Future Family, startup veteran who was formerly an Investment Bank advisor and director of Richard Branson’s Carbon War Room accelerator, who will speak on the category potential and her startup’s disruptive solution
  • Angeline N. Beltsos, Chief Medical Officer and CEO, Vios Fertility Institute, on the challenges and progress represented by fertility issues
  • Ashley Gillen Binder, Future Family client, on her life-changing experience with the company

Future Family is the first company to bring together financing, technology, and concierge care in an easy-to-use online platform. With established revenues and growth, Future Family is a leading example of next-gen FinTech, which will provide customized solutions for specific verticals. Moderated by Richard Norman, Managing Director, OurCrowd.

Wednesday, March 10th at 9:00AM San Francisco | 12:00PM New York | 7:00PM Israel 
Register Here. 

D-ID drives MyHeritage deep nostalgia animation

Jimmy Kimmel didn’t get much work done on Wednesday. He said he was too busy using technology developed by OurCrowd portfolio company D-ID to bring a photograph of his great grandfather to life. Genealogy platform MyHeritage released a feature that animates faces in still photos using video reenactment technology designed by D-ID. Called Deep Nostalgia, it produces a realistic depiction of how a person could have moved and looked if they were captured on video, using pre-recorded driver videos that direct the movements in the animation and consist of sequences of real human gestures. Users can upload a picture regardless of the number of faces in it. The photos are enhanced prior to animation using the MyHeritage Photo Enhancer, USA Today reports. MyHeritage tells the BBC that some people might find the feature “creepy” while others would consider it “magical”. It’s been a good few days for MyHeritage, which was acquired by Francisco Partners last week for a reported $600M.

Rewire banks $20M, led by OurCrowd

OurCrowd led a successful Series B $20M round for Rewire, a provider of cross-border online banking services for expatriate workers. The finance will be used to add new products to its platform, such as bill payments, insurance, savings, and credit and loan services. New investors included Renegade Partners, Glilot Capital Partners and AME Cloud Ventures as well as previous investors Viola FinTech, BNP Paribas through their venture capital fund Opera Tech Ventures, Moneta Capital, and private angel investors. Rewire has also secured an EU Electronic Money Institution license from the Dutch Central Bank to support its European expansion plans, PitchBook reports. Rewire was also granted an expanded Israeli Financial Asset Service Provider license. Acquiring these licenses is another major step for the FinTech startup in its mission to provide secure and accessible financial services for migrant workers worldwide, Globes reports.

Learn More.

Top Tech News

Consumer Physics’ SCiO lets you wake up and taste the coffee


Israeli and Colombian agriculture tech startup Demetria emerged from stealth with $3M seed round to support its artificial-intelligence-powered green coffee quality analysis system. Demetria has added AI to the SCiO technology developed by OurCrowd portfolio company Consumer Physics to enable fast sampling and quality control for coffee. “We use this sensory fingerprint in combination with complex AI and cupping data to discern ‘taste,’” Felipe Ayerbe, CEO and co-founder of Demetria, tells Daily Coffee News. “Demetria is an accurate predictor of cupping analysis. We have conducted the same process that is required to train and certify a ‘cupper,’ but instead of using human senses of taste and smell, we use state-of-the-art sensors that read the biochemical markers of taste, and couple that information with AI.” The technology “has pioneered the digitization of coffee aroma and taste, the most important quality variables of the coffee bean. For the first time, quality and taste can now be assessed at any stage of the coffee production and distribution process, from farm to table,” says Food and Drink International

Medisafe raises $30M for AI that helps people take medication
Medisafe, an OurCrowd portfolio company developing a personalized medication management platform to help patients stay on top of their prescriptions, raised $30M in a round led by Sanofi Ventures and Alive Israel HealthTech Fund with participation from Merck Ventures, Octopus Ventures, OurCrowd and others. About 20% to 30% of medication prescriptions are never filled, and approximately 50% of medications for chronic disease aren’t taken as prescribed, causing approximately 125,000 deaths and costing the U.S. health care system more than $100B a year. “Medisafe can email and text patients to remind them to take their medications on time. Moreover, the platform can target ‘rising-risk’ patients with analytics and insights based on real-time behavioral assessments, boosting adherence up to 20%”, VentureBeat reports.

Arcadia guides companies on homeworking energy costs
With millions of staff working from home, who should foot the bill for heating and other energy costs? Biotechnology company Biogen and financial giant Goldman Sachs are both working with alternative energy firm Arcadia to help employees switch their homes to wind or solar power – just a couple of the companies assisting staff to explore renewable energy sources for residential use. Alexa Minerva, Arcadia’s senior director of partnerships, says the offering is just one of many new kinds of employee benefits that may arise as work becomes more geographically flexible in the pandemic era and beyond, and perks like an office cafeteria become less of a draw. “It says something not just about…how you value a person, but it also says what you value as a company,” Minerva tells Time magazine. “It’s a great hiring strategy and retention tactic.”

Volvo unit teams with Israel’s Foretellix on autonomous safety
Volvo Autonomous Solutions has formed a partnership with OurCrowd portfolio company Foretellix to jointly create a coverage-driven verification solution for autonomous vehicles and machines on public roads and in confined areas such as mines and quarries. The technology will facilitate testing of millions of different scenarios, which will validate autonomous vehicles and machines’ ability to deal with anything they might encounter. Foretellix has developed a novel verification platform that uses intelligent automation and big data analytics tools that coordinate and monitor millions of driving scenarios, to expose bugs and edge cases, including the most extreme cases. “The partnership with Foretellix gives us access to the state-of-the-art verification tools and accelerating our time to market,” Magnus Liljeqvist, Global Technology Manager, Volvo Autonomous Solutions tells Globes.  

Tmura: Startup options help those in need
The sale of MyHeritage to Francisco Partners for a reported $600M will directly help Israeli nonprofits working in education and youth projects through the sale of options the startup donated in 2013 to Tmura, an Israeli nonprofit, The Times of Israel reports. OurCrowd has supported Tmura since its inception, encouraging all our startups to donate a percentage of their options. If they have a big exit, it benefits those who need help. A total of 718 Israeli companies have made donations to Tmura, with a record number of 62 new donor companies contributing in 2020, as nonprofit organizations struggled to raise funds elsewhere during the pandemic. Exit proceeds in 2020 totaled $1.9 million, the second-highest year ever, after 2013, when Waze was sold to Google for some $1 billion. Waze’s options,


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Artificial Intelligence

How I’d Learn Data Science If I Were To Start All Over Again




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@santiviquezSantiago Víquez

Physicist turned data scientist. Creator of

A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting.

I’m aware that we all learn in different ways. Some prefer videos, others are ok with just books and a lot of people need to pay for a course to feel more pressure. And that’s ok, the important thing is to learn and enjoy it.

So, talking from my own perspective and knowing how I learn better I designed this path if I had to start learning Data Science again.

As you will see, my favorite way to learn is going from simple to complex gradually. This means starting with practical examples and then move to more abstract concepts.

Kaggle micro-courses

I know it may be weird to start here, many would prefer to start with the heaviest foundations and math videos to fully understand what is happening behind each ML model. But from my perspective starting with something practical and concrete helps to have a better view of the whole picture.

In addition, these micro-courses take around 4 hours/each to complete so meeting those little goals upfront adds an extra motivational boost.

Kaggle micro-course: Python

If you are familiar with Python you can skip this part. Here you’ll learn basic Python concepts that will help you start learning data science. There will be a lot of things about Python that are still going to be a mystery. But as we advance, you will learn it with practice.


Price: Free

Kaggle micro-course: Pandas

Pandas is going to give us the skills to start manipulating data in Python. I consider that a 4-hour micro-course and practical examples is enough to have a notion of the things that can be done.


Price: Free

Kaggle micro-course: Data Visualization

Data visualization is perhaps one of the most underrated skills but it is one of the most important to have. It will allow you to fully understand the data with which you will be working.


Price: Free

Kaggle micro-course: Intro to Machine Learning

This is where the exciting part starts. You are going to learn basic but very important concepts to start training machine learning models. Concepts that later will be essential to have them very clear.


Precio: Free

Kaggle micro-course: Intermediate Machine Learning

This is complementary to the previous one but here you are going to work with categorical variables for the first time and deal with null fields in your data.


Price: Free

Let’s stop here for a moment. It should be clear that these 5 micro-courses are not going to be a linear process, you are probably going to have to come and go between them to refresh concepts. When you are working in the Pandas one you may have to go back to the Python course to remember some of the things you learned or go to the pandas documentation to understand new functions that you saw in the Introduction to Machine Learning course. And all of this is fine, right here is where the real learning is going to happen.

Now, if you realize these first 5 courses will give you the necessary skills to do exploratory data analysis (EDA) and create baseline models that later you will be able to improve. So now is the right time to start with simple Kaggle competitions and put in practice what you’ve learned.

Kaggle Playground Competition: Titanic

Here you’ll put into practice what you learned in the introductory courses. Maybe it will be a little intimidating at first, but it doesn’t matter it’s not about being first on the leaderboard, it’s about learning. In this competition, you will learn about classification and relevant metrics for these types of problems such as precision, recall, and accuracy.


Kaggle Playground Competition: Housing Prices

In this competition, you are going to apply regression models and learn about relevant metrics such as RMSE.


By this point, you already have a lot of practical experience and you’ll feel that you can solve a lot of problems, buuut chances are that you don’t fully understand what is happening behind each classification and regression algorithms that you have used. So this is where we have to study the foundations of what we are learning.

Many courses start here, but at least I absorb this information better once I have worked on something practical before.

Book: Data Science from Scratch

At this point, we will momentarily separate ourselves from pandas, scikit-learn ,and other Python libraries to learn in a practical way what is happening “behind” these algorithms.

This book is quite friendly to read, it brings Python examples of each of the topics and it doesn’t have much heavy math, which is fundamental for this stage. We want to understand the principle of the algorithms but with a practical perspective, we don’t want to be demotivated by reading a lot of dense mathematical notation.

Link: Amazon

Price: $26 aprox

If you got this far I would say that you are quite capable of working in data science and understand the fundamental principles behind the solutions. So here I invite you to continue participating in more complex Kaggle competitions, engage in the forums, and explore new methods that you find in other participants’ solutions.

Online Course: Machine Learning by Andrew Ng

Here we are going to see many of the things that we have already learned but we are going to watch it explained by one of the leaders in the field and his approach is going to be more mathematical so it will be an excellent way to understand our models even more.


Price: Free without the certificate — $79 with the certificate

Book: The Elements of Statistical Learning

Now the heavy math part starts. Imagine if we had started from here, it would have been an uphill road all along and we probably would have given up easier.

Link: Amazon

Price: $60, there is an official free version on the Stanford page.

Online Course: Deep Learning by Andrew Ng

By then you have probably already read about deep learning and play with some models. But here we are going to learn the foundations of what neural networks are, how they work, and learn to implement and apply the different architectures that exist.


Price: $49/month

At this point it depends a lot on your own interests, you can focus on regression and time series problems or maybe go more deep into deep learning.

I wanted to tell you that I launched a Data Science Trivia game with questions and answers that usually come out on interviews. To know more about this Follow me on Twitter.

Also published at


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