We are nearing the end of 2017. But with each ending chapter, we have remarkable achievements to be thankful for. Similarly, for the data science community, this year was filled with a number of new technologies, tools, version updates etc. 2017 saw blockbuster releases such as PyTorch, TensorFlow 1.0 and Caffe 2, among many others.
We invite data scientists, machine learning experts, and other data science professionals to come together on this Thanksgiving Day, and thank the organizations, which made our interactions with AI easier, faster, better and generally more fun. Let us recall our blessings in 2017, one month at a time…
[dropcap]Jan[/dropcap]
Thank you, Facebook and friends for handing us PyTorch
Hola 2017! While the world was still in the New Year mood, a brand new deep learning framework was released. Facebook along with a few other partners launched PyTorch. PyTorch came as an improvement to the popular Torch framework. It now supported the Python language over the less popular Lua. As PyTorch worked just like Python, it was easier to debug and create unique extensions. Another notable change was the adoption of a Dynamic Computational Graph, used to create graphs on the fly with high speed and flexibility.
[dropcap]Feb[/dropcap]
Thanks Google for TensorFlow 1.0
The month of February brought Data Scientist’s a Valentine’s gift with the release of TensorFlow 1.0. Announced at the first annual TensorFlow Developer Summit, TensorFlow 1.0 was faster, more flexible, and production-ready. Here’s what the TensorFlow box of chocolate contained:
- Fully compatibility with Keras
- Experimental APIs for Java and Go
- New Android demos for object and image detection, localization, and stylization
- A brand new Tensorflow debugger
- An introductory glance of XLA–a domain-specific compiler for TensorFlow graphs
[dropcap]Mar[/dropcap]
We thank Francois Chollet for making Keras 2 a production ready API
Congratulations! Keras 2 is here. This was a great news for Data science developers as Keras 2, a high- level neural network API allowed faster prototyping. It provided support both CNNs (Convolutional Neural Networks) as well as RNNs (Recurrent Neural Networks). Keras has an API designed specifically for humans. Hence, a user-friendly API. It also allowed easy creation of modules, which meant it is perfect for carrying out an advanced research. Developers can now code in Python, a compact, easy to debug language.
[dropcap]Apr[/dropcap]
We like Facebook for brewing us Caffe 2
Data scientists were greeted by a fresh aroma of coffee, this April, as Facebook released the second version of it’s popular deep learning framework, Caffe. Caffe 2 came up as a easy to use deep learning framework to build DL applications and leverage community contributions of new models and algorithms. Caffe 2 was fresh with a first-class support for large-scale distributed training, new hardware support, mobile deployment, and the flexibility for future high-level computational approaches. It also provided easy methods to convert DL models built in original Caffe to the new Caffe version. Caffe 2 also came with over 400 different operators–the basic units of computation in Caffe 2.
[dropcap]May[/dropcap]
Thank you, Amazon for supporting Apache MXNet on AWS and Google for your TPU
The month of May brought in some exciting launches from the two tech-giants, Amazon and Google. Amazon Web Services’ brought Apache MXNet on board and Google’s Second generation TPU chips were announced.
Apache MXNet, which is now available on AWS allowed developers to build Machine learning applications which can train quickly and run anywhere, which means it is a scalable approach for developers.
Next up, was Google’s second generation TPU (Tensor Processing Unit) chips, designed to speed up machine learning tasks. These chips were supposed to be (and are) more capable of CPUs and even GPUs.
[dropcap]Jun[/dropcap]
We thank Microsoft for CNTK v2
The mid of the month arrived with Microsoft’s announcement of the version 2 of its Cognitive Toolkit. The new Cognitive Toolkit was now enterprise-ready, had production-grade AI and allowed users to create, train, and evaluate their own neural networks scalable to multiple GPUs. It also included the Keras API support, faster model compressions, Java bindings, and Spark support. It also featured a number of new tools to run trained models on low-powered devices such as smartphones.
[dropcap]Jul[/dropcap]
Thank you, Elastic.co for bringing ML to Elastic Stack
July made machine learning generally available for the Elastic Stack users with its version 5.5. With ML, the anomaly detection of the Elasticsearch time series data was made possible. This allows users to analyze the root cause of the problems in the workflow and thus reduce false positives. To know about the changes or highlights of this version visit here.
[dropcap]Aug[/dropcap]
Thank you, Google for your Deeplearn.js
August announced the arrival of Google’s Deeplearn.js, an initiative that allowed Machine Learning models to run entirely in a browser. Deeplearn.js was an open source WebGL- accelerated JS library. It offered an interactive client-side platform which helped developers carry out rapid prototyping and visualizations. Developers were now able to use hardware accelerator such as the GPU via the webGL and perform faster computations with 2D and 3D graphics. Deeplearn.js also allowed TensorFlow model’s capabilities to be imported on the browser. Surely something to thank for!
[dropcap]Sep[/dropcap]
Thanks, Splunk and SQL for your upgrades
September surprises came with the release of Splunk 7.0, which helps in getting Machine learning to the masses with an added Machine Learning Toolkit, which is scalable, extensible, and accessible. It includes an added native support for metrics which speed up query processing performance by 200x. Other features include seamless event annotations, improved visualization, faster data model acceleration, a cloud-based self-service application.
September also brought along the release of MySQL 8.0 which included a first-class support for Unicode 9.0. Other features included are
- An extended support for native JSOn data
- Inclusion of windows functions and recursive SQL syntax for queries that were previously impossible or difficult to write
- Added document-store functionality
So, big thanks to the Splunk and SQL upgrades.
[dropcap]Oct[/dropcap]
Thank you, Oracle for the Autonomous Database Cloud and Microsoft for SQL Server 2017
As Fall arrived, Oracle unveiled the World’s first Autonomous Database Cloud. It provided full automation associated with tuning, patching, updating and maintaining the database. It was self scaling i.e., it instantly resized compute and storage without downtime with low manual administration costs. It was also self repairing and guaranteed 99.995 percent reliability and availability. That’s a lot of reduction in workload!
Next, developers were greeted with the release of SQL Server 2017 which was a major step towards making SQL Server a platform. It included multiple enhancements in Database Engine such as adaptive query processing, Automatic database tuning, graph database capabilities, New Availability Groups, Database Tuning Advisor (DTA) etc. It also had a new Scale Out feature in SQL Server 2017 Integration Services (SSIS) and SQL Server Machine Learning Services to reflect support for Python language.
[dropcap]Nov[/dropcap]
A humble thank you to Google for TensorFlow Lite and Elastic.co for Elasticsearch 6.0
Just a month more for the year to end!! The Data science community has had a busy November with too many releases to keep an eye on with Microsoft Connect(); to spill the beans.
So, November, thank you for TensorFlow Lite and Elastic 6.
Talking about TensorFlow Lite, a lightweight product for mobile and embedded devices, it is designed to be:
- Lightweight: It allows inference of the on-device machine learning models that too with a small binary size, allowing faster initialization/ startup.
- Speed: The model loading time is dramatically improved, with an accelerated hardware support.
- Cross-platform: It includes a runtime tailormade to run on various platforms–starting with Android and iOS.
And now for Elasticsearch 6.0, which is made generally available. With features such as easy upgrades, Index sorting, better Shard recovery, support for Sparse doc values.There are other new features spread out across the Elastic stack, comprised of Kibana, Beats and Logstash. These are, Elasticsearch’s solutions for visualization and dashboards, data ingestion and log storage.
[dropcap]Dec[/dropcap]
Thanks in advance Apache for Hadoop 3.0
Christmas gifts may arrive for Data Scientists in the form of General Availability of Hadoop 3.0. The new version is expected to include support for Erasure Encoding in HDFS, version 2 of the YARN Timeline Service, Shaded Client Jars, Support for More than 2 NameNodes, MapReduce Task-Level Native Optimization, support for Opportunistic Containers and Distributed Scheduling to name a few. It would also include a rewritten version of Hadoop shell scripts with bug fixes, improved compatibility and many changes in some existing installation procedures.
Pheww! That was a large list of tools for Data Scientists and developers to thank for this year. Whether it be new frameworks, libraries or a new set of software, each one of them is unique and helpful to create data-driven applications. Hopefully, you have used some of them in your projects. If not, be sure to give them a try, because 2018 is all set to overload you with new, and even more amazing tools, frameworks, libraries, and releases.
