PySpark : Combining Machine Learning & Big Data

With the ever increasing flow of data, comes the industry focus on how to use those data for driving business & insights; but what about the size of the data these days, we have to deal with ?

The more cleaner data you have, its good for training your ML ( Machine Learning ) models, but sadly neither the world feeds you clean data nor the huge amount of data is capable of fast processing using common libraries like Pandas etc.

How about using the potential of big data libraries with support in Python to deal with this huge amount of data for deriving business insights using ML techniques? But how can we amalgamate the two?

Here comes “ **PySpark : Combining Machine Learning & Big Data** “.

Usually people in the ML domain prefer using Python; so combining the potential of Big Data technologies like Spark etc to supplement ML is a matter of ease with pyspark ( A Python package to use the Spark’s capabilities ).

**This talk would revolve around** -

1) Why do we need to fuse Big Data with Machine Learning ?

2) How Spark’s architecture will help us boost our preparations for faster ML ?

3) How pyspark’s MLlib ( Machine Learning library ) helps you do ML so seamlessly ?

Microservices & Docker for Data Science

The growth of Data Science has led to the emergence of using Docker & Microservices to serve the Data Science based applications to the real world in an optimized manner.

In this talk, I aim to cover :

- What is Microservices?
- What is Docker?
- Key components of a Data Science Process
- Where Microservices & Docker fits in a Data Science process?
- Using Microservices for Data Science
- Using Docker for Data Science

How to Build Successful Career in AI/ML

In this roundtable, we will hear different opinions on what would be the best way to build a career in data science. From taking part in Kaggle competitions to Hackathons, there are many paths that can take you to success.

Exploring the arena of Competitive Data Science

In the recent times, the passion towards Data Science has sky-rocketed due to the huge potential of Data.

With this started the growth of Data Science competitions. In this talk I aim to cover :

- What is Competitive Data Science ( CDS ) ?
- Why should you try CDS at least once?
- How should you start CDS? ( Approach & Start )
- Where to & How to get involved with core level CDS? ( Launch )
- Is CDS everything?
- What other than CDS you should focus on, to become hirable in 2023?

Diving into SustAInability

In this captivating talk, we'll explore the dynamic landscape where AI meets the pressing challenges of our planet's sustainability focusing on the United Nation's SDGs & how AI can help followed by how we can do AI sustainably.

The session will commence by unraveling the fundamental components of AI—Data, Models, and Compute—providing a backdrop that doesn't dwell on intricate technicalities but rather sets the stage for understanding AI's evolution over time. We'll navigate through the transformative power of AI without getting lost in jargon, making it accessible to all audiences.

As we progress, the spotlight will shift to the globally recognized United Nations Sustainable Development Goals. A succinct overview of these goals will pave the way for a deeper exploration of the marriage of AI and sustainability. Through engaging examples, we will spotlight specific sustainable goals and elucidate how AI can act as a catalytic force, steering us towards a brighter and greener future.

A captivating demonstration will take center stage, illustrating how AI can be harnessed to surmount challenges linked to achieving sustainable goals. This hands-on showcase will illuminate the immense potential AI holds in revolutionizing sectors like environmental conservation, resource management, and more.

However, amidst the achievements and innovations, a critical realization emerges—AI's remarkable strides have brought along environmental concerns, echoing in the form of compute-related emissions. This session will drive home the message that while AI has made monumental contributions, the need of the hour is to ensure its sustainable utilization.

Steering the conversation towards actionable change, the talk will arm attendees with the knowledge needed to embrace AI sustainably. It will empower individuals with insights into how they can harness AI's potential while minimizing its ecological footprint. By the end of this talk, attendees will be equipped with a profound understanding of AI's journey, its impact on emissions, and the pivotal role they can play in shaping an AI-powered world that coexists harmoniously with our planet.

For the attendees, this talk will be a thought-provoking exploration that serves as the first step towards a sustainable AI future. Let's embark on this enlightening voyage together, charting a course towards a world where innovation and environmental stewardship walk hand in hand.

Target audience : Beginner to advance level practitioners of AI
Preferred session duration : 30 minutes